#include <Message.h>
|
| json | api_call (const std::string &url, const std::string &method="GET", const json &data=json(), const std::string &content_type="", const bool cache=true) |
| | helper function to communicate with the http api More...
|
| |
| std::string | _token |
| | token to authentificate with the discord api More...
|
| |
◆ Message() [1/3]
| DiscordCPP::Message::Message |
( |
const json & |
data, |
|
|
const std::string & |
token |
|
) |
| |
- Parameters
-
| [in] | data | raw JSON data |
| [in] | token | discord token for the API |
- Returns
- Message object
◆ Message() [2/3]
| DiscordCPP::Message::Message |
( |
const Message & |
old | ) |
|
- Parameters
-
- Returns
- copied Message object
◆ Message() [3/3]
| DiscordCPP::Message::Message |
( |
| ) |
|
◆ ~Message()
| DiscordCPP::Message::~Message |
( |
| ) |
|
◆ delete_msg()
| void DiscordCPP::Message::delete_msg |
( |
| ) |
|
◆ edit()
Edit this message.
- Parameters
-
| [in] | content | New message-content. |
- Returns
- Updated message object.
◆ author
| User* DiscordCPP::Message::author = NULL |
The author of this message.
◆ channel
the channel the message was sent in
◆ content
| std::string DiscordCPP::Message::content |
The content of this message.
◆ edited_timestamp
| std::string DiscordCPP::Message::edited_timestamp |
when the message was edited
◆ embeds
| std::vector<Embed*> DiscordCPP::Message::embeds |
◆ mention_everyone
| bool DiscordCPP::Message::mention_everyone |
true, if everyone is mentioned
◆ mentions
| std::vector<User*> DiscordCPP::Message::mentions |
◆ pinned
| bool DiscordCPP::Message::pinned |
true, if this message has been pinned
◆ timestamp
| std::string DiscordCPP::Message::timestamp |
when the message was created
◆ tts
| bool DiscordCPP::Message::tts |
true, if this is a tts message
◆ type
| int DiscordCPP::Message::type |
◆ webhook_id
| std::string DiscordCPP::Message::webhook_id |
the webhook id, if the message was generated by a webhook
The documentation for this class was generated from the following files:


Fandangepicodus
Come see a movie:
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Superman vs. Batman
Captain America 3
X-Men: Apocalypse
Date
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Age
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Pochettino a 'top coach' – Bayern defender Kimmich
by Sports DeskJoshua Kimmich gave a strong endorsement of Mauricio Pochettino's coaching ability after the rumoured Bayern Munich target departed Tottenham.
Spurs brought Pochettino's well-regarded stint in charge to an abrupt end on Tuesday, dismissing the man who led them to the Champions League final following a poor start to the new season.
The development could have ramifications in Bavaria, where the Argentinian is said to be held in high regard.
Bayern were reportedly interested in Pochettino even before they parted company with head coach Niko Kovac at the beginning of November and could now pursue him as a free agent.
The Bundesliga champions have entrusted Hansi Flick with overseeing the team until the end of 2019 but have not committed to a long-term appointment.
"I do not know if he is a candidate for us," Kimmich told reporters when asked about Pochettino after Germany's win over Northern Ireland in Euro 2020 qualifying.
"What you can say is that Tottenham were fighting at the top for years. They have had huge consistency.
"He's a top coach, but I do not know if he's an option for us."
Flick has won each of his two matches since taking charge on an interim basis, the second a 4-0 thrashing of Der Klassiker rivals Borussia Dortmund.
Bayern CEO Karl-Heinz Rummenigge last week said: "We decided that he will be coach at least until Christmas and possibly beyond. We trust Hansi Flick."
Bayern have reportedly identified Ajax boss Erik ten Hag and Paris Saint-Germain's Thomas Tuchel as their preferred candidates but would likely struggle to prise either coach away from their clubs before the end of the season.
Jose Mourinho had also been linked with the Bavarian club, but he was appointed Pochettino's successor at Spurs on Wednesday.


I believe,
"Education and Technology is the key which can take underprivileged
children, their families, and generations from darkness of poverty, illiteracy, and sorrows
towards brightness of prosperity, wisdom and happiness."
- Adwaita Jadhav
Contributing towards community by developing useful softwares
and conducting workshops gives me immense pleasure. I have developed softwares for smart cities, health and transportation etc.
Teaching is one of my another passion. I conduct technical workshops for children from villages where even being
able to see a computer is still a dream.
Currently I am also raising funds for such schools.
Know more regarding my technical projects here..
Want to know more about me? Great! I am quite congenial. Would love to let you know about me.
Know about my skills, passions, and experiences over here.


2 Coelhos / Two Rabbits
2 Coelhos is a movie starring Fernando Alves Pinto, Alessandra Negrini, and Caco Ciocler. In São Paulo, the scheme of the corrupt DA Júlia and her mate, the lawyer Henrique with the gangster Maicom is discovered and sent to the...


---
layout: page
title: Summoner School
headline: Summoner School
---
What is Summoner School?
Interested in improving your skills or sharing your game knowledge with others? Come to summoner school as a mentor or a mentee! We'll provide a location and free food.
FAQ
Will there be food?
Yes, of course.
Is there any minimum requirements to be a mentor/mentee?
There are absolutely no requirements, anyone can be a mentor or mentee.


Philippe Jaroussky
He has been praised for performances in all the most prestigious concert halls in France (Théâtre des Champs-Elysées, Théâtre du Châtelet, Salle Pleyel, Salle Gaveau, Opéra de Lyon, Opéra de Montpellier, Opéra de Nancy, Arsenal de Metz, Théâtre de Caen) and abroad (The Barbican Centre and Southbank Cente in London, the Palais des Beaux Arts in Brussels, Grand Théâtre du Luxembourg, the Konzerthaus in Vienna, the Staatsoper and Philharmonie in Berlin, Teatro Real in Madrid, Carnegie Hall and the Lincoln Center in New York).
Philippe Jaroussky is signed exclusively to Erato and has received numerous awards for his recordings with the label. With Heroes (Vivaldi opera arias) and La Dolce Fiamma he achieved Gold disc status; his tribute to Carestini (with le Concert d'Astrée and Emmanuelle Haim) won CD of the year at the Victoires de la Musique and the Midem Classical Awards in 2009; his Stabat Mater with soprano Julia Lezhneva and I Barocchisti picked up the 2014 International Classical Music Awards for best Baroque Vocal Album and Best Opera Album. Of the 2013 release Farinelli: Porpora Arias, Gramophone wrote: “Jaroussky's rapid passagework in quick heroic arias is precise...but the outstanding moments are slow arias that could have been tailor-made for Jaroussky's sweetly graceful melodic singing”.
In recent years he has collaborated with singers including Cecilia Bartoli and Nathalie Stutzmann, as well as singing with and directing the Ensemble Artaserse, the Baroque orchestra he founded in 2002. The group takes its name from the Vinci opera Artaserse, which Jaroussky revived in its spectacular modern-day permiere as one of five exceptional countertenors in the cast.


Date: 17 Feb, 2015
Our Architectural Office provides wide range of services, especially architecture, master planning, urban design, interiors, landscape and environmental design, also refurbishment and renovation services around the world.


Today's Events
OPENING LECTURE
Failure doesn't mean game over, It means try again with experience
Speaker: RG Mamba
PUBG Mobile
Placement-Points vs Kill-Points
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CALL of DUTY Mobile
Battle-Royale vs Multiplayer
Speaker: UncleDV
PES MOBILE
Attack vs Defence
Speaker: Crappy & Papa


Data structure describing the drive model components of a vehicle with up to 4 driven wheels and up to 16 un-driven wheels. The drive model incorporates engine, clutch, gears, autobox, differential, and Ackermann steer correction. More...
#include <PxVehicleDrive4W.h>
Public Member Functions | |
| PxVehicleDriveSimData4W () | |
| PX_FORCE_INLINE const PxVehicleDifferential4WData & | getDiffData () const |
| Return the data describing the differential. More... | |
| PX_FORCE_INLINE const PxVehicleAckermannGeometryData & | getAckermannGeometryData () const |
| Return the data describing the Ackermann steer-correction. More... | |
| void | setDiffData (const PxVehicleDifferential4WData &diff) |
| Set the data describing the differential. More... | |
| void | setAckermannGeometryData (const PxVehicleAckermannGeometryData &ackermannData) |
| Set the data describing the Ackermann steer-correction. More... | |
| PxVehicleDriveSimData4W (const PxEMPTY) | |
| Public Member Functions inherited from PxVehicleDriveSimData | |
| PX_FORCE_INLINE const PxVehicleEngineData & | getEngineData () const |
| Return the engine data. More... | |
| void | setEngineData (const PxVehicleEngineData &engine) |
| Set the engine data. More... | |
| PX_FORCE_INLINE const PxVehicleGearsData & | getGearsData () const |
| Return the gears data. More... | |
| void | setGearsData (const PxVehicleGearsData &gears) |
| Set the gears data. More... | |
| PX_FORCE_INLINE const PxVehicleClutchData & | getClutchData () const |
| Return the clutch data. More... | |
| void | setClutchData (const PxVehicleClutchData &clutch) |
| Set the clutch data. More... | |
| PX_FORCE_INLINE const PxVehicleAutoBoxData & | getAutoBoxData () const |
| Return the autobox data. More... | |
| void | setAutoBoxData (const PxVehicleAutoBoxData &autobox) |
| Set the autobox data. More... | |
| PxVehicleDriveSimData () | |
| PxVehicleDriveSimData (const PxEMPTY) | |
Static Public Member Functions | |
| static void | getBinaryMetaData (PxOutputStream &stream) |
| Static Public Member Functions inherited from PxVehicleDriveSimData | |
| static void | getBinaryMetaData (PxOutputStream &stream) |
Private Member Functions | |
| bool | isValid () const |
| Test if the 4W-drive simulation data has been setup with legal data. More... | |
Private Attributes | |
| PxVehicleDifferential4WData | mDiff |
| Differential simulation data. More... | |
| PxVehicleAckermannGeometryData | mAckermannGeometry |
| Data for ackermann steer angle computation. More... | |
Friends | |
| class | PxVehicleDrive4W |
Additional Inherited Members | |
| Protected Member Functions inherited from PxVehicleDriveSimData | |
| bool | isValid () const |
| Test that a PxVehicleDriveSimData instance has been configured with legal data. Call only after setting all components with setEngineData,setGearsData,setClutchData,setAutoBoxData. More... | |
| Protected Attributes inherited from PxVehicleDriveSimData | |
| PxVehicleEngineData | mEngine |
| PxVehicleGearsData | mGears |
| PxVehicleClutchData | mClutch |
| PxVehicleAutoBoxData | mAutoBox |
Data structure describing the drive model components of a vehicle with up to 4 driven wheels and up to 16 un-driven wheels. The drive model incorporates engine, clutch, gears, autobox, differential, and Ackermann steer correction.
|
inline |
|
inline |
|
inline |
Return the data describing the Ackermann steer-correction.
|
static |
|
inline |
Return the data describing the differential.
|
private |
Test if the 4W-drive simulation data has been setup with legal data.
| void PxVehicleDriveSimData4W::setAckermannGeometryData | ( | const PxVehicleAckermannGeometryData & | ackermannData | ) |
Set the data describing the Ackermann steer-correction.
| void PxVehicleDriveSimData4W::setDiffData | ( | const PxVehicleDifferential4WData & | diff | ) |
Set the data describing the differential.
|
friend |
|
private |
Data for ackermann steer angle computation.
|
private |
Differential simulation data.


virtual function void
build_phase
( uvm_phase
phase)
Build phase method used to instantiate components
Arguments: phase : the phase scheduled for build_phase method
virtual function svaunit_status_type compute_status
( int unsigned
a_nof_tests,
int unsigned
a_nof_failures)
Compute the status according to a_nof_tests and a_nof_failures
Returns: the computed status
Arguments: a_nof_tests : a number of total tests exercised during test simulation a_nof_failures : a number of total failed tests exercised during test simulation
virtual function void
connect_phase
( uvm_phase
phase)
Connect phase method used to connect pointers
Arguments: phase : the phase scheduled for connect_phase method
virtual function string
form_tree
( int
a_level)
Form the test topology as a tree
Returns: a string representing the tree
Arguments: a_level : the level where the test is created


On your XLSX document "Select All" then "copy" the data, come to this page, put focus on the <textarea> box and "paste" the text in. In some instance (large data sets) this might take a couple of seconds. Once there simply click the button to product the table.


: Mensia
Technologies SA, Paris, France
: Child
and Adolescent Psychiatry Department, Robert Debré Hospital, Paris, France
:
GIPSA-Lab, CNRS, University Grenoble Alpes, National Polytechnic Institute, Grenoble, France
Full postal address
Mensia Technologies SA
Plateforme d’innovation Boucicaut
130 rue de Lourmel
75015 Paris - France
Aurore Bussalb’s e-mail<EMAIL_ADDRESS>Louis Mayaud’s e-mail<EMAIL_ADDRESS>A. Bussalb, Q. Barthélemy, D. Ojeda, and L. Mayaud work for Mensia Technologies. M. Congedo served as scientiﬁc advisor for Mensia Technologies when this work was conducted.
This research was conducted as part of a PhD thesis funded by Mensia Technologies and the Association Nationale Recherche et Technologie (ANRT) as well as the EU H2020 NEWROFEED grant number 684809.
Number of words: 6 158
Number of ﬁgures: 4
Abstract
Meta-analyses have been extensively used to evaluate the eﬃcacy of neurofeedback (NFB) treatment for Attention Deﬁcit/Hyperactivity Disorder (ADHD) in children and adolescents. However, each meta-analysis published in the past decade has contradicted the methods and results from the previous one, thus making it diﬃcult to determine a consensus of opinion on the eﬀectiveness of NFB. This works brings continuity to the ﬁeld by extending and discussing the last and much controversial meta-analysis by Cortese et al. [2016].
The extension comprises an update of that work including the latest control trials, which have since been published and, most importantly, oﬀers a novel methodology. Speciﬁcally, NFB literature is characterized by a high technical and methodological heterogeneity, which partly explains the current lack of consensus on the eﬃcacy of NFB. This work takes advantage of this by performing a Systematic Analysis of Biases (SAOB) in studies included in the previous meta-analysis.
Our extended meta-analysis (k = 16 studies) conﬁrmed the previously obtained results of eﬀect sizes in favor of NFB eﬃcacy as being signiﬁcant when clinical scales of ADHD are rated by parents (non-blind, p-value = 0.0014), but not when they are rated by teachers (probably blind, p-value = 0.27). The eﬀect size is signiﬁcant according to both raters for the subset of studies meeting the deﬁnition of "standard NFB protocols" (parents’ p-value = 0.0054; teachers’ p-value = 0.043, k = 4). Following this, the SAOB performed on k = 33 trials identiﬁed three main factors that have an impact on NFB eﬃcacy: ﬁrst, a more intensive treatment, but not treatment duration, is associated with higher eﬃcacy; second, teachers report a lower improvement compared to parents; third, using high-quality EEG equipment improves the eﬀectiveness of the NFB treatment.
The identiﬁcation of biases relating to an appropriate technical implementation of NFB certainly supports the eﬃcacy of NFB as an intervention. The data presented also suggest that the probably blind assessment of teachers may not be considered a good proxy for blind assessments, therefore stressing the need for studies with placebo-controlled intervention as well as carefully reported neuromarker changes in relation to clinical response.
Keywords: ADHD, neurofeedback, meta-analysis, analysis of bias
Attention Deﬁcit/Hyperactivity Disorder (ADHD) is a common childhood psychiatric disorder characterized by impaired attention and/or hyperactivity/impulsivity. Symptoms may persist in adulthood with clinical signiﬁcance, which makes ADHD a life-long problem for many patients [Faraone et al., 2006]. The prevalence of ADHD is around 5% in school-aged children, thus aﬀecting an estimated 2.5 million children in Europe [The American Psychiatric Association, 2013]. ADHD negatively impacts children’s well-being, with many suﬀering from low self-esteem [Shaw-Zirt et al., 2005] and underachievement in school [Barry et al., 2002]. Parents are equally aﬀected, since the child’s behavior is frequently attributed to bad parenting [Harpin, 2005]. From a societal point of view, ADHD also has a high ﬁnancial impact: a 2013 survey in Europe estimated costs related to ADHD between and Euros per patient/year [Le et al., 2014].
The diagnosis of ADHD primarily relies on questionnaire-based clinical evaluation [The American Psychiatric Association, 2013], which can be supported by objective assessment metrics of executive function such as the Test of Variables of Attention (TOVA) [Forbes, 1998], the Continuous Performance Test (CPT) [Barkley, 1991], and the Sustained Attention to Response Task (SART) [Robertson et al., 1997]. Objective markers of brain function using electroencephalogram (EEG), functional Magnetic Resonance Imaging (fMRI), or Positron Emission Tomography (PET) are not considered to be useful for improving diagnosis at the individual level, but can help in diﬀerentiating groups of patients [Johnstone et al., 2005]. In particular, diﬀerent phenotypes of ADHD patients present with an increase in the EEG theta wave power (4-8Hz) and/or a decrease of EEG beta wave power (12-32Hz) in frontal areas, or a decrease in the EEG Sensorimotor Rhythm (SMR) power (13-15Hz) in the central area [Monastra, 2005; Matoušek et al., 1984; Janzen et al., 1995; Loo et al., 2017]. A device using EEG to help clinicians more accurately diagnosis ADHD was cleared by the Food and Drug Administration (FDA) [Neba Health, 2015].
Psychostimulants are the most common treatment currently in use, and have proven to be eﬃcacious [Taylor, 2014; Storebo et al., 2015]. However, their long-term eﬀectiveness and side eﬀects are still debated and form an active area of research [DuPaul, 1998; Swanson et al., 2001; Jensen, 1999; Su et al., 2016; Becker et al., 2016]. Moreover, ADHD children under medication commonly suﬀer from mild side eﬀects such as loss of appetite and sleep disturbance, although serious adverse events are rare [Storebo et al., 2015; Cooper et al., 2011]. These drawbacks make some parents and clinicians reluctant to opt for such treatment, instead turning to non-pharmaceutical alternatives such as dietary changes [Bélanger et al., 2009] and behavioral therapy, which have been proven to be less eﬃcacious [Sonuga-Barke et al., 2013].
Neurofeedback (NFB) is another non-pharmaceutical and non-invasive approach aiming at the reduction of ADHD symptoms [Arns et al., 2015; Steﬀert and Steﬀert, 2010; Marzbani et al., 2016]. Shortly after the discovery of the brain’s electric activity by Berger [1929], Durup and Fessard [1935] demonstrated it could be voluntarily modulated, leading to a series of ﬁndings on the self-regulation of brain activity. The ﬁrst indication of the therapeutic potential of brain activity operant-conditioning came forty years later when Sterman et al. [1974] found that training the SMR activity reduces the incidence of epileptic crisis in kerosene-exposed cats. The technique, then known as NFB, rapidly became the subject of investigation in various ﬁelds of neuropsychiatry including, most notably, ADHD [Lubar and Shouse, 1976; Rossiter and La Vaque, 1995; Linden et al., 1996; Maurizio et al., 2014].
NFB is a self-paced brain neuromodulation technique that represents brain activity in real-time using auditory or visual modulations, on which learning paradigms, such as operant conditioning [Reynolds, 1975] or voluntary control, can be applied. To deliver this intervention, neurophysiological time series are analyzed online in order to drive feedback applications such as serious games [Wang et al., 2010]. The signal of interest should represent the activity of a population of neurons involved in attentional networks, which is translated into visual or auditory cues. The sensory feedback constitutes the rewards mechanism, promoting learning using, for instance, operant conditioning protocols [Sherlin et al., 2011]. Operant conditioning enables neural plasticity, thus supporting the child in the task repetition [Skinner, 1984], which is believed to result in long-lasting neuronal reorganization [Van Doren et al., 2017].
Several NFB protocols have been proposed and investigated for decreasing the symptoms of ADHD:
Moreover, NFB protocols can be personalized: some studies did not use the usual deﬁnitions of EEG band ranges but determined them thanks to the individual Alpha Peak Frequency (iAPF) [Klimesch, 1999], giving individualized NFB protocols [Liu et al., 2016; Escolano et al., 2014; Bazanova et al., 2018].
NFB eﬃcacy on the core symptoms of ADHD (inattention, hyperactivity, and impulsivity) has been the subject of several meta-analytic studies [Loo and Barkley, 2005; Lofthouse et al., 2012; Arns et al., 2009; Micoulaud-Franchi et al., 2014; Sonuga-Barke et al., 2013]. To date, studies have not reached a consensus on the eﬃcacy of NFB; while Arns et al. [2009] and Micoulaud-Franchi et al. [2014] claim results in favor of its eﬃcacy, especially on the inattention component highlighted by Micoulaud-Franchi et al., other authors, such as Loo and Barkley [2005]; Lofthouse et al. [2012], and Sonuga-Barke et al. [2013] express their reservations, asking for further evidence from blind assessment.
The most recent meta-analysis addressing the eﬃcacy of NFB was published by Cortese et al. [2016], including a total of 13 Randomized Controlled Trials (RCTs). The results of this analysis are mixed: when based on parent assessments, which are not blind to treatment, they are signiﬁcantly in favor of NFB, whereas when the evolution of symptoms is rated by teachers (considered as probably blind), the results are no longer signiﬁcant. The authors concluded that further evidence from blind assessments is needed in order to support NFB as a treatment for ADHD symptoms. However, some of the choices made in this meta-analysis, which may have had an impact on the results, have since been debated by the community. Speciﬁcally, Micoulaud-Franchi et al. [2016] criticized the use of an uncommon behavioral scale provided by Steiner et al. [2014] for the teachers’ assessments and the inclusion of a pilot study carried out by Arnold et al. [2014].
As a result of these criticisms and the concurrent publication of new RCTs meeting Cortese et al.’s inclusion criteria, we decided to update this meta-analysis and take the opportunity to investigate the impact of its controversial choices. While performing our investigation, we observed two shortcomings: the assumption that the diﬀerence between teacher and parent assessments can solely be explained by the placebo eﬀect, and pooling together heterogeneous studies in terms of methodology and technical implementation. An interesting approach, albeit not commonly performed, to assess the NFB eﬃcacy would be to analyze the speciﬁcity of the EEG changes with respect to trained neuromarkers [Maurizio et al., 2014]. In our case, based on the data at our disposal, we used the technical and methodological heterogeneity of the NFB trials to our advantage rather than disadvantage by extending the previous work with a novel method, the Systematic Analysis of Biases (SAOB). Indeed, the NFB domain is characterized by clinical literature that is extremely heterogeneous: studies diﬀer methodologically (for instance, random assignment and presence of a blind assessment), in the NFB implementation (for instance, number of sessions, session and treatment length, and type of protocol) as well as on the acquisition and processing of the EEG signal. Description and analysis of diﬀerent types of NFB implementation was subject to several studies [Arns et al., 2014; Enriquez-Geppert et al., 2017; Vernon et al., 2004; Jeunet et al., 2018]. However to the best of our knowledge, none of these studies has used statistical tools to quantify their inﬂuence on clinical endpoints.
Since methodological and technical implementations of studies are highly likely to inﬂuence their outcomes [Congedo et al., 2004], we suggest identifying which of the factors independently inﬂuence the clinical eﬃcacy with the use of appropriate statistical tools. In addition, we have made available all the raw RCT data we have used and a complete Python library for performing meta-analysis [Bussalb, 2019a]. Through doing so, we hope to foster the replication of our and previous studies and to facilitate similar future projects.
Search terms were directly taken from Cortese et al. [2016] with the exception of the need for a control arm, which is detailed in Supplemental Materials [Bussalb, 2019b]. The requirements included:
The studies satisfying all these criteria were included in the SAOB. In order to replicate and update Cortese et al.’s meta-analysis, we applied the original inclusion criteria of their meta-analysis to our search (the main diﬀerence being the presence of a control group).
In the included studies, the severity of ADHD symptoms have been assessed by parents and, whenever available, by teachers. Cortese et al. [2016] and Micoulaud-Franchi et al. [2014] deﬁned parents as Most Proximal (MPROX) raters who were not blind to the treatment, as opposed to teachers, who were considered as Probably Blind (PBLIND) raters. This distinction is intended to assess the amplitude of the placebo eﬀect, where it is hypothesized that teachers, who are presumed to be more blind to the intervention, are less inﬂuenced in their assessment. Eﬃcacy of NFB was measured using clinical scales, such as the ADHD-RS [Pappas, 2006], on the following outcomes: inattention, hyperactivity/impulsivity, and total scores. The factor analysis was performed using the total score.
The goal of a meta-analysis is to aggregate results from diﬀerent clinical investigations and oﬀer a consolidated body of evidence. To achieve this, it is necessary to assume some level of homogeneity in the design of the studies: inclusion criteria, and the presence and type of control (active, semi-active, or non-active). Because studies occasionally use slight variations of a clinical scale and because of the clinical heterogeneity of patients and control, the scores are standardized before being pooled into a Summary Eﬀect (SE). The between-Eﬀect Size (ES) is one such standardized metric, which we have implemented in this paper (see Supplemental Materials [Bussalb, 2019b]).
The meta-analysis was performed with a Python package developed for this work. The package oﬀers a transparent approach for the choice of parameters in an eﬀort to ease replicability. We have benchmarked it against RevMan version 5.1 [Cochrane Collaboration, 2011, UK, London] by replicating Cortese et al. [2016]’s work. The code is made fully available on a GitHub repository [Bussalb, 2019a], together with all the RCTs raw data we have used in the present study.
Before updating the Cortese et al. [2016] work with recently published studies [Strehl et al., 2017; Baumeister et al., 2016], we decided to run a sensitivity analysis investigating the choices that later proved controversial [Micoulaud-Franchi et al., 2016]. The investigated changes included:
As initially suggested by Cortese et al., the analysis was run on two subgroups of studies with the two choices described above: one gathering studies following the standard protocol deﬁned by Arns et al. [2014] and a second including only participants not taking medications during the clinical trial.
While revisiting the existing meta-analyses, it became apparent that the studies pooled together were highly heterogeneous in terms of methodological and practical implementation. For instance, all NFB interventions were pooled together regardless the quality of acquisition, the quality of EEG data, and the trained neuromarker. Equally, the methodological implementations varied signiﬁcantly, requiring the ’subgroup’ analysis (for instance, gathering studies following standard protocols) that are somewhat arbitrary. To circumvent these limitations, we implemented a novel approach: the SAOB. With this method, the within-ES of each intervention was considered as a dependent variable to be explained by methodological and technical factors. Such analysis aims at identifying known methodological biases (e.g. blind assessments negatively associated with within-ES) and possible technical factors (e.g. a good control on real time data quality positively inﬂuences the treatment outcome).
We classiﬁed the factors inﬂuencing the eﬃcacy of NFB into ﬁve categories: methodological, technical, demographics, and quality of the signal and acquisition. Factors were chosen based on that reported in the literature as presumed to inﬂuence ES, and categorized as follows:
A quality score equal to 3 was assigned if all the above criteria were satisﬁed. If at least one was satisﬁed the quality score was set to 2, otherwise the score was set to 1.
To prevent any bias in the analysis, the names of the factors were hidden during the entire analysis so that the data scientists (AB, QB, DO, and LM) were fully blind to them. The names were revealed only when the data analysis and results were accepted as valid: this included choice of variable normalization and validation of model hypothesis, as detailed below.
The pre-processing of factors for the analysis included the following steps: factors for which there were too many missing observations arbitrarily set to more than 20% of the total observations, were removed from the analysis. Furthermore, if a factor had more than 80% similar observations it was also removed. A study did not systematically correspond to an observation: when several clinical scales and/or raters were available in a study, each couple clinical scale - rater was considered as an observation. Categorical variables were coded as dummies, i.e., the presence of the factor was represented with 1 and its absence with 0. All variables were standardized by subtracting the mean and then dividing by the standard deviation (not applied before the decision tree described below).
To compute the within-ES, the means of total ADHD scores given by parents and teachers were used. Moreover, in case studies providing results for more than one behavioral scale the within-ES scores were computed for each one as:
where is the mean of clinical scale, for treatment T, taken at time t (pre-test or post-test) and represents its standard deviation. With this deﬁnition, we focus on the eﬀect of the treatment within a group [Cohen, 1988] as commonly reported in the literature [Arns et al., 2009; Maurizio et al., 2014; Strehl et al., 2017]. This within-ES enables us to quantify the eﬃcacy of NFB inside the treatment group. Finally, to avoid to break analysis methods assumptions, an outlier rejection was applied deﬁning thresholds of acceptance as , with and being respectively the mean and the standard deviation of all within-ES computed [Shewhart, 1931].
The within-ES was then considered as a dependent variable to be explained by the factors (the independent variables). The following three methods, implemented with the Scikit-Learn Python [Pedregosa et al., 2011, version 0.18.1] and the Statsmodels Python [Seabold and Perktold, 2010, version 0.8.0] libraries, were used to perform the regression:
The aim of the linear regression is to estimate the regression coeﬃcients linking the factors to the within-ES. A signiﬁcant coeﬃcient (here and hereafter meaning signiﬁcantly diﬀerent from zero) indicates that the associated factor has an inﬂuence on NFB eﬃcacy and its sign the direction of the eﬀect. The WLS diﬀers from a traditional linear regression estimated with Ordinary Least Squares (OLS) in that a weight is assigned to each observation in order to account for the multiplicity of reported clinical endpoints in some studies. In addition, the weight was also set as a function of the sample size to account for variations in sample sizes. Speciﬁcally, the weight of each study was taken as the ratio between the experimental group’s sample size and the number of behavioral scales available. We also ran the analysis using the OLS method to assess the impact of the weights on the results.
The second linear method applied was the LASSO, which naturally incorporates variable selection into the linear model thanks to -norm applied on the coeﬃcients. A coeﬃcient not set to zero means that the associated factor has an inﬂuence on NFB eﬃcacy and its sign indicates the direction of the eﬀect.
The last method used to determine factors inﬂuencing NFB was the decision tree [Quinlan, 1986], a hierarchical and non-linear method. This breaks down a dataset into smaller and smaller subsets using, at each iteration, a variable and a threshold chosen to optimize a simple Mean Square Error (MSE) criterion [James et al., 2013]. A tree is composed of several nodes and leafs, the importance of which decreases from the top node, called the root node, downward.
Given that these methods are intrinsically diﬀerent we compared their results. For instance, the decision tree captures variable interactions and can relate factors to within-ES in a non-linear fashion. On the other hand, the LASSO oﬀers an elegant mathematical framework for variable selection. Further details are provided in the Supplemental Material [Bussalb, 2019b].
Search terms entered in Pubmed returned 155 results during the last check on February 12, 2018, including 22 articles used in previous meta-analyses on NFB. Following the selection process illustrated in Figure 1, 33 studies were included in the SAOB and 16 in the meta-analysis, as summarized in Table 1. The 33 studies selected for the SAOB followed Cortese et al.’s criteria, with the exception of the requirement for a control group. Indeed, since within-ES were considered in this analysis, a control group was not required.
The replication of Cortese et al.’s results obtained are presented in Table 2:
The meta-analysis was then extended by adding three new articles [Strehl et al., 2017; Baumeister et al., 2016; Bazanova et al., 2018]. Bazanova et al. [2018] gave parents’ assessments for all outcomes, Baumeister et al. [2016] provided results solely for parents’ total outcome, whereas Strehl et al. [2017] gave both teachers’ and parents’ assessments for all outcomes. To be consistent with the SAOB, only the standard NFB group of Bazanova et al. [2018] was included in this update. Despite favorable results for NFB, particularly on parents’ assessments, adding these three new studies did not change either the magnitude or the signiﬁcance of the SE, for any outcome regardless of the raters, as illustrated in Figure 2.
Regarding the "standard protocol" subgroup, Cortese et al. [2016] found all the outcomes signiﬁcant except for the hyperactivity symptoms rated by teachers, which showed only a statistical trend (p-value = 0.11). Similar results were obtained when adding the most recent studies meeting this deﬁnition [Strehl et al., 2017; Baumeister et al., 2016] (p-value = 0.11). The SE for the total outcome assessed by teachers remained signiﬁcant with the addition of the new RCT (p-value = 0.043), thus giving more strength to this result since it is now based on four studies including 283 patients in total.
As for the no-drug subgroup, SEs were found signiﬁcant for the inattention symptoms assessed by parents (p-value = 0.017). In addition, the diﬀerences in Arnold et al. [2014] values and the inclusion of Bazanova et al. [2018] caused a loss of signiﬁcance in hyperactivity outcome for parents (p-value = 0.062) compared to Cortese et al. [2016] (p-value = 0.016). Only Bazanova et al. [2018] was included in this subgroup: in the two other studies the subjects were taking psychostimulants during the trial.
All the clinical scales used to compute the between-ES following our choices are summarized in the Supplemental Materials.
This analysis was performed on 33 trials (corresponding to 67 observations) assessing the eﬃcacy of NFB, as presented in Table 1. The outlier rejection removed two training groups of Bazanova et al. [2018] from the analysis because their within-ES were out of the bounds. Among the 28 factors selected, nine were removed because there were too many missing observations or because they were too homogeneous: beta up in frontal areas, the use of a transfer card, the type of threshold for the discrete rewards (incremental or ﬁxed), the EEG quality equal of 3, the presence of a control group, the individualization of the frequency bands based on the iAPF, coupling NFB with EMG-Biofeedback, the severity of ADHD symptoms at baseline, and the degree of engagement with NF intervention.
All results are presented in Table 3. These results, require careful interpretation since each technique provided slightly diﬀerent results. These diﬀerences may depend on the diﬀerent assumptions of the model and several other factors. Nonetheless, we are inclined to trust the ﬁndings that are consistent across methods.
The WLS technique identiﬁed nine signiﬁcant factors for an adjusted R-squared of 0.62 (see second column of Table 3). When applying the OLS, the same factors were signiﬁcant except the EEG quality equal of 2 and the presence of more than one active electrode) with a lower adjusted R-squared (0.35). The LASSO regression selected six signiﬁcant factors (see third column of Table 3). With these methods, a negative coeﬃcient means that the factor is in favor of the eﬃcacy of NFB. The decision tree is presented in Figure 3: the best predictor in our case was the PBLIND (see last column of Table 3). Four other factors also split the subsets; however, increasingly fewer samples are available the lower we get into the tree, making the interpretation increasingly doubtful.
Several factors were common to the three methods used. These included, in particular: the assessment by a blind rater, the treatment length, and an EEG quality score equal to 2 (see lines 1, 9, and 19 of Table 3). The methods also agreed on the direction of the eﬀect for these factors: a shorter treatment and recording of the EEG with a good-quality system appears preferable, whereas teachers’ assessment appears less favorable compared to parents’ assessment.
It is more doubtful the inﬂuence of the factors returned by only one or two methods (see lines 2, 3, 7, 8, 11, 16, 17, and 20 of Table 3). In particular:
Five factors were returned by only one of the methods: randomizing the groups, the IRB approval, the session length, the presence of a transfer phase, and the correction or rejection of ocular artifacts.
Eight factors were never selected by the three methods (see lines 4, 5, 6, 12, 13, 14, 15 and 22 of Table 3): the children’s minimum and maximum age, being on drugs during NFB treatment, the protocols SMR, beta up in central areas, theta down, and SCP, and the artifact correction based on amplitude. Thus, these factors overwhelmingly appear not to have an inﬂuence on NFB eﬃcacy.
In the next section we discuss only the factors that were selected by at least two of the three methods.
This replication and update of a meta-analysis did not meet all PRISMA recommendations [Moher et al., 2009]. In particular, the risk of bias in individual and across studies was not assessed.
In the meta-analysis performed here, we challenged some choices made by Cortese et al., which proved controversial: the computation of between-ES based on an unusual scale [Steiner et al., 2014] and the inclusion of a pilot study [Arnold et al., 2014] whose endpoint values were not available at the time Cortese et al. conducted their meta-analysis.
 However, relying on one or the other scale did not change the signiﬁcance of the between-ES, regardless of outcome.
Second, to compute the between-ES of Arnold et al. [2014] the clinical scores taken when all sessions were completed were used instead of looking at interim results as with Cortese et al.. Some studies suggested that the number of sessions correlates positively with the changes observed in the EEG [Vernon et al., 2004] so that a lower number of sessions would lead to artiﬁcially smaller between-ES. Here, the between-ES computed with the values at post test of Arnold et al. [2014] were smaller than those obtained after 12 sessions; however, these diﬀerences did not lead to a change of signiﬁcance of the SE.
To conclude on this meta-analysis, although some points were controversial, the impact on the meta-analysis was minimal and did not change the statistical signiﬁcance of any outcome. The addition of the three new studies [Strehl et al., 2017; Baumeister et al., 2016; Bazanova et al., 2018] further conﬁrmed the original results. Indeed, the signiﬁcance did not change for any outcome: the SE remained signiﬁcant for MPROX raters and non-signiﬁcant for PBLIND. Adding three more studies increased the signiﬁcance of the sensitivity analysis run by Cortese et al., most notably the SE of studies corresponding to NFB "standard protocols" [Arns et al., 2014]. While Cortese et al. found that this subset tends to perform better, particularly on the PBLIND outcome, adding two studies conﬁrmed this result on the total clinical score (p-value < 0.05). Despite the obvious heterogeneity of the studies included in this subset (particularly in terms of protocol used), these results suggest a positive relation between the features of this standard design and NFB performance. This result is a breakthrough in the demonstration of standard NFB protocol eﬃcacy for the treatment of ADHD. Nonetheless, the studies included in this subset are still highly heterogeneous (particularly in terms of protocol used), a factor which should be accounted for.
Description and analysis of diﬀerent types of NFB implementation were subject to several studies [Arns et al., 2014; Enriquez-Geppert et al., 2017; Vernon et al., 2004; Jeunet et al., 2018; Arns et al., 2009; Cortese et al., 2016]. However, to the best of our knowledge, none of these studies has implemented a systematic multivariate approach to associate factors to clinical endpoints therefore exposing their univariate analysis to a greater extend to the presence of a confounding factor.
Two observations were detected as outliers and so removed from the dataset before performing the SAOB: Bazanova et al.’s individualized NFB and individualized NFB coupled with EMG-Biofeedback groups. Indeed, these two groups presented very large within-ES (-3.41 and -3.95), even bigger than those reported in the literature on medication treating ADHD [Luan et al., 2017]. These large values broke our working hypothesis, so in order to be able to conclude on the results obtained by the SAOB, an outlier rejection was implemented.
As expected, the number of sessions was found to be signiﬁcant, even if it was by only two methods, which was in compliance with existing literature. For instance, using several univariate regressions without correction for multiple testing Arns et al. [2009], Arns et al. [2014] stated that performing less than 20 NFB sessions leads to smaller eﬀects. Similarly, Vernon et al. [2004] observed that positive changes in the EEG and behavioral performance occurred after a minimum of 20 sessions. However, Enriquez-Geppert et al. [2017] insisted that the number of sessions should be carefully chosen in order to avoid "overtraining". The fact that the number of sessions was not identiﬁed by the LASSO as a positively contributing factor might be explained by the presence of only two data points with 20 sessions or less. Conceivably, the temporal threshold of eﬃcacy was passed for all included studies, making the identiﬁcation of this factor by the three methods unlikely on this dataset. However, the two methods that identiﬁed this factor both agreed that as expected, the more sessions performed, the more eﬃcient the NFB tends to be.
Interestingly, [Minder et al., 2018] suggests that the subject location of the NFB training may also be an important contributory factor to clinical eﬀectiveness. However, this has been challenged by a recent study [Minder et al., 2018] showing that performing NFB at school or at the clinic has no signiﬁcant impact on treatment response.
The type of NFB protocol was not identiﬁed by any method, and did not appear to inﬂuence the NFB results. This minimal importance granted by the methods to the NFB protocols is counter-intuitive given the centrality of the protocols in the neurophysiological mode of action and subsequent expected impact on therapeutic eﬀectiveness [Vernon et al., 2004]. A possible explanation for this result is that these protocols were equally eﬃcacious for the populations to whom they were oﬀered and thereby did not constitute a signiﬁcant explanatory factor. This result, however, does not preclude a combined and personalized strategy (oﬀering personalized protocols based on phenotypes) to further improve performance, as previously suggested by Alkoby et al. [2017].
Several factors were selected by all three methods with the same direction of inﬂuence: the EEG quality, the treatment length, and the rater’s probably blindness to the treatment. First, our analysis highlighted that recording EEG in good conditions leads to better results. This can be explained by the fact that better signal quality enables more accurate extraction of EEG patterns linked to ADHD and hence leads to better learning and therapeutic eﬃcacy [Congedo et al., 2004]. However, it remains diﬃcult to assess the quality of EEG hardware (such as the ampliﬁer used) because little information is provided in these studies. This calls for greater care in future studies, which should strive to assess and report the quality of the data.
Next, it appears that the longer the course of treatment, the less eﬃcient it becomes. This may be explained by the degree of engagement with NFB intervention: it may be harder to be engaged with a long course of treatment. However, it is diﬃcult to quantify because either no questionnaires assessing engagement were submitted to children or this information was not provided. It is an interesting point to investigate, so we invite future studies to share it if possible.
Arguably, the treatment length is a proxy for treatment intensity, suggesting that a shorter period of treatment is more likely to succeed because the frequency of the sessions is higher. This hypothesis is supported by the fact that the variable session pace (number of sessions per week) is also associated with larger within-ES according to the LASSO and the decision tree. The impact of the intensity of treatment has been investigated by [Rogala et al., 2016] on healthy adults: it was observed that studies with at least four training sessions completed on consecutive days were all beneﬁcial. Overall, these results suggest adopting a high session pace, which is not common knowledge in the ﬁeld.
Some other factors’ inﬂuence would have been interesting to investigate, such as using personalized NFB protocols based on the iAPF [Liu et al., 2016], which seems promising according to Bazanova et al. [2018]; Escolano et al. [2014]. However, it could not be included in the SAOB because only two studies used personalized NFB protocols. This lack of studies is also the reason why the impact of coupling EMG-Biofeedback with NFB could not be included in the SAOB. Another interesting factor, which could have helped explain the result on the treatment duration, was excluded from the analysis: the severity at baseline. Although pre-test scores were available for each study, they could not be compared because they were measured on diﬀerent scales. To address this problem, the scores were normalized using the maximum score to be obtained on each scale. However, this value was not found for several clinical scales, which led to missing observations. When more studies including these features will be available, it would be interesting to run the SAOB to determine the inﬂuence of these excluded factors.
In general our results strongly support the eﬀectiveness of NFB for the treatment of ADHD. However, as expected, the assessment of symptoms by non-blind raters leads to far more favorable results than by PBLIND raters, a result widely expected and in close compliance with the existing meta-analysis [Cortese et al., 2016; Micoulaud-Franchi et al., 2014]. This observation would certainly be contradictory should teachers’ assessments reﬂect a placebo eﬀect, which has long been documented in the literature [Sollie et al., 2013; Narad et al., 2015; Minder et al., 2018]. This point is investigated in greater detail in the following section.
Teachers were considered as PBLIND raters by Cortese et al. and Micoulaud-Franchi et al.. Unexpectedly, the data provided did not exactly match the widely accepted hypothesis stating that the diﬀerence between MPROX and PBLIND can solely be explained by the placebo eﬀect. Nonetheless, the emphasis put on ’probably’ indicates that teachers may be aware of the treatment followed. An element that corroborates this hypothesis is the fact that, for all the studies included in this work, the amplitude of the clinical scale at baseline suggests that teachers did not capture the full extent of the symptoms or, put diﬀerently, that they were blind more to the symptoms than to the intervention, as illustrated in Figure 4. Indeed, before the intervention, teachers rated the symptoms less severely compared to parents and observed less improvement at post-test: this tends to correspond more to case A with no placebo eﬀect than case B. The expected diﬀerences of ratings between teachers and parents have been extensively studied [Sollie et al., 2013; Narad et al., 2015; Minder et al., 2018], observing that teachers are more likely to underrate a child’s symptom severity, especially for younger children. As a consequence, teachers might simply be less likely to observe a clinical change over the course of the treatment [Sollie et al., 2013; Narad et al., 2015; Minder et al., 2018]. Moreover, it is also clear that there is more variability in teachers’ scores compared to parents’, which could partly explain the lower ES obtained for PBLIND raters, since the variability deﬂates the ES. In conclusion, using PBLIND as an estimate for correcting the placebo eﬀect does not appear an appropriate choice.
Another way to highlight a possible placebo eﬀect is to focus on the decision tree illustrated in Figure 3. The top node splits: on the one hand 46 observations corresponding to MPROX raters and, on the other, 21 observations corresponding to PBLIND. If the diﬀerences observed between PBLIND and MPROX raters were due to the placebo eﬀect, one would expect to ﬁnd in the MPROX sub-tree some factors linked to the perception of the implication in the treatment. This was indeed the case: treatment length was found to be signiﬁcant but not in the direction corroborating the hypothesis that they are a part of a placebo eﬀect. Indeed, one would expect that the longer the treatment, the higher the placebo eﬀect and the greater the within-ES. Instead, the opposite was found, somewhat invalidating the hypothesis.
Overall, these results suggest that PBLIND assessments could hardly be used to assess placebo eﬀect as they seem to be blinder to symptoms than to intervention. In the absence of an ethically [Holtmann et al., 2014] and technically [Birbaumer, 1991] feasible sham for NFB protocols [World Medical Association, 2000], it is necessary to fall back on an acceptable methodological alternative for the demonstration of clinical eﬀectiveness. Among those are the analysis of neuromarkers collected during NFB treatment demonstrating that patients do control the trained neuromarkers; that they learn (reinforce control over time), and that these possibly lead to lasting brain reorganization (e.g., changes in their baseline resting state activity). The speciﬁcity of these changes, in relation to which neuromarkers were trained and to the clinical improvement, will be an essential component of this demonstration.
In this work we provide additional elements in favor of the eﬀectiveness of NFB for the treatment of ADHD. First, we conﬁrm that a subgroup of standard NFB studies shows a statistically signiﬁcant improvement on PBLIND assessments (k = 4 studies instead of 3, n = 283 patients instead of 158 Cortese et al. [2016]).
Second, we identify technical factors as positive contributors to clinical eﬀectiveness, which strongly suggests that it is mediated by a real mechanism of action based on EEG conditioning. Equally, treatment intensity was also found to contribute, corroborating what is known from learning theory (memory consolidation) [Mowrer, 1960]; that is to say, a more intense treatment leads to an increased clinical eﬃcacy.
While these ﬁndings certainly contribute to the debate, this work also suggests that the ultimate demonstration of evidence remains out of reach, as teachers’ assessments were partly invalidated as a proxy for the quantiﬁcation of the placebo eﬀect. As a consequence, using PBLIND endpoints to address the speciﬁcity of the clinical eﬃcacy is not recommended and we instead advise a reliance on other available methodological tools. These tools include sham NFB and neuromarker analysis investigating the speciﬁcity of the EEG changes with respect to trained neuromarkers as well as changes in clinical endpoints.
This work also oﬀers an open-source toolbox for running meta-analysis and SAOB: the code and data used are available, thus ensuring the transparency and replicability of these analysis, as well as fostering future ones. Regarding perspectives, this two-fold methodological framework applied to NFB for ADHD could be suitable for other NFB applications [Marzbani et al., 2016].
A. Bussalb, Q. Barthélemy, D. Ojeda, and L. Mayaud work for Mensia Technologies. M. Congedo served as scientiﬁc advisor for Mensia Technologies when this work was conducted.
AB extracted all data from articles and performed the analysis, MC provided advice and expertise concerning the design of the study and the methods used, RD and EA provided clinical interpretation of the results. QB and DO supported the toolbox implementation and validation. LM supervised the entirety of this work.
This research was conducted as part of a PhD thesis funded by Mensia Technologies, the Association Nationale Recherche et Technologie (ANRT), and the EU H2020 NEWROFEED grant number 684809.
We would like to thank Jean-Arthur Micoulaud-Franchi, MD, PhD for his helpful comments and ideas stimulating this work. We also want to thank Samuele Cortese, MD, PhD for his help while replicating his meta-analysis. Finally, we also would like to thank the Association Nationale Recherche et Technologie (ANRT) for partially funding the PhD thesis of the ﬁrst author as well as the EU H2020 NEWROFEED grant number 684809.
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| Analysis | Study | Year |
|
||
| Arnold et al. | 2014 | 26 | |||
| Bakhshayesh et al. | 2011 | 18 | |||
| Beauregard and Levesque | 2006 | 15 | |||
| Bink et al. | 2014 | 45 | |||
| Christiansen et al. | 2014 | 14 | |||
| Gevensleben et al. | 2009 | 59 | |||
| Heinrich et al. | 2004 | 13 | |||
| Holtmann et al. | 2009 | 20 | |||
| Linden et al. | 1996 | 9 | |||
| Maurizio et al. | 2014 | 13 | |||
| Steiner et al. | 2011 | 9 | |||
| Steiner et al. | 2014 | 34 | |||
| van Dongen-Boomsma et al. | 2013 | 22 | |||
| 13 studies | 297 | |||
| Baumeister et al. | 2016 | 8 | |||
| Bazanova et al. | 2018 | 17 | |||
| Strehl et al. | 2017 | 72 | |||
| 16 studies | 394 | |||
| Bluschke et al. | 2016 | 19 | |||
| Deilami et al. | 2016 | 12 | |||
| Drechsler et al. | 2007 | 17 | |||
| Duric et al. | 2012 | 23 | |||
| Escolano et al. | 2014 | 20 | |||
| Fuchs et al. | 2003 | 22 | |||
| Geladé et al. | 2016 | 39 | |||
| Kropotov et al. | 2005 | 86 | |||
| Lee and Jung | 2017 | 18 | |||
| Leins et al. | 2007 | 19 | |||
| Li et al. | 2013 | 32 | |||
| Meisel et al. | 2014 | 12 | |||
| Mohagheghi et al. | 2017 | 30 | |||
| Mohammadi et al. | 2015 | 16 | |||
| Monastra et al. | 2002 | 51 | |||
| Ogrim and Hestad | 2013 | 13 | |||
| Strehl et al. | 2006 | 23 | |||
| c = SAOB | 33 studies | 846 | |||
| Working hypothesis |
| Our choices | |
|
Parents | Total | () | () |
| Inattention | () | () | |
| Hyperactivity | () | () | |
Teachers | Total | () | () |
| Inattention | () | () | |
| Hyperactivity | () | () | |
|
|
|
||
Methodological | 0.12 (0.044) | 0.12 | root node |
|
| randomization | 0.15 (0.062) | 0.044 | / |
| -0.25 (0.01) | 0.00 | / |
|
Population | age max | -0.13 (0.075) | 0.00 | / |
| age min | 0.025 (0.76) | 0.00 | / |
| on drugs | -0.091 (0.29) | 0.00 | / |
| number of sessions | -0.36 (0.00) | 0.00 | 2 node |
| session length | -0.34 (0.001) | 0.00 | / |
| treatment length | 0.35 (0.00) | 0.065 | 2 and 3 nodes |
| session pace | -0.058 (0.33) | -0.0043 | 4 node |
| -0.10 (0.13) | 0.00 | / |
|
| beta up central | -0.093 (0.44) | 0.00 | / |
| theta down | 0.043 (0.72) | 0.00 | / |
| -0.026 (0.85) | 0.00 | / |
|
| transfer phase | 0.44 (0.00) | 0.00 | / |
| more than one active electrode | -0.17 (0.010) | -0.033 | / |
| EEG quality 2 | -0.18 (0.033) | -0.032 | 3 node |
Signal quality | EOG rejection or correction | -0.35 (0.001) | 0.00 | / |
| amplitude-based artifact rejection | 0.052 (0.52) | 0.00 | / |


| LIN | NSN | Item Name | PBIC | Type | DND | Qnty |
|---|---|---|---|---|---|---|
| CLEANER STEAM PRS JET-(12HP ) | 9 | INS | N | 1 | ||
| CLEANER,PRESSURE,SO-(2/045D-TEWS | 9 | INS | N | 2 | ||
| COMPUTER, PERSONAL WORKSTATION: | 9 | INS | N | 1 | ||
| COMPUTER: HP COMPAC, DC5000MT | 9 | INS | N | 3 | ||
| COMPUTER, PERSONAL WORKSTATION: | 9 | INS | N | 3 | ||
| COMPUTER, PERSONAL WORKSTATION: | 9 | INS | N | 1 | ||
| MONITOR, COLOR IMPE: DELL FLATSC | 9 | INS | N | 1 | ||
| MONITOR NEC:19" FL PANEL | 9 | INS | N | 4 | ||
| MONITOR: F/S 17" NEC LCD1880SX-B | 9 | INS | N | 4 | ||
| ANT GRP OE-254()/GRC | N | TPE | N | 2 | ||
| BALLISTIC PROTECTION SYSTEM: MED | N | TPE | N | 12 | ||
| ACFT STROBE LIGHT LENS: 1000215- | N | TPE | N | 12 | ||
| ACFT ENGINE INLET BARRIER FILTER | N | TPE | N | 12 | ||
| TOWBAR,AIRCRAFT | N | TPE | N | 1 | ||
| UH-60 A/C COVERS: KENNON | N | TPE | N | 12 | ||
| CRANE WHL MTD | N | TPE | N | 1 | ||
| CLAMSHELTER,AIRCRAF-(50B-BBB-10C | N | TPE | N | 1 | ||
| 6 PER LIVING CONTAINER: 10' X 40 | 9 | INS | N | 15 | ||
| SOLAR SYSTEM TY II | N | TPE | N | 1 | ||
| CRANE TK CAT RT41AA | N | TPE | N | 1 | ||
| TELEPHONE SATELLITE: IRIDIUM SAT | N | TPE | N | 2 | ||
| SATELLITE RECEIVER: SCIENTIFIC | 9 | INS | N | 1 | ||
| CLR SEC SYS: SSC-21DQ | 9 | INS | N | 1 | ||
| PRINTER LASERJET: LASERJET 2550L | 9 | INS | N | 1 | ||
| PRINTER LARGE FORMAT: HP 500 HP | 9 | INS | N | 2 | ||
| CLEANER, STEAM, HIGH PRESSURE WA | 9 | INS | N | 1 | ||
| POWER SUPP PP-6224B/U | H | LTT | N | 1 | ||
| RADIO SET: AN/VRC-90A | H | LTT | N | 1 | ||
| AIR CONDITIONER: SSC-24C | 9 | INS | N | 2 | ||
| TR 1 1/4T 4X4 M998P1 | N | TPE | N | 1 | ||
| TRK UTIL 1-1/4T M998 | N | TPE | Y | 2 | ||
| TRK UTIL 1-1/4T M1025 | P | TPE | Y | 1 | ||
| TRK UTIL 1-1/4T M1026 | N | TPE | Y | 1 | ||
| TRK CGO D/S M923 | N | TPE | Y | 1 | ||
| TRACTOR: 5235 JOHN DEERE | N | TPE | N | 1 | ||
| TELEVISION RECEIVER HOME TYPE, W | 9 | INS | N | 1 | ||
| HP LASER JET PRINTER: C3917A | 9 | INS | N | 1 | ||
| COPIER: SHARP AR5316 | 9 | INS | N | 1 | ||
| SAFE: GS-180 | 9 | INS | N | 1 | ||
| REFRIGERA:LG-GR-151SFA | 9 | INS | N | 1 |


Painted Desert [au70], desert, in Imperia, peasants: 10000
Last updated Turn #147
Painted Desert [au70], desert, in Imperia, peasants: 10000
------------------------------------------------------------------------
0: Garrison [av0d], garrison, Imperial Empire, with ten peasants, 11 oxen
0: guards Painted Desert [au70].
30: Overcrowding causes zero peasant deaths.
Province controlled by Castle Thorbardin [v46], castle, in Thorbardin
Mountains [ao69]
Ruled by The Bard [s2a], duke
Border closed.
Entrance fees:
Routes leaving Painted Desert:
North, plain, to Buffalo Plains [at70], 7 days
Garrison [dh4g], garrison, uncontrolled, with three pikemen
East, desert, to Painted Desert [au71], 8 days
South, desert, to Painted Desert [av70], 8 days
Garrison [ed8z], garrison, Imperial Empire, with ten peasants
Border closed.
West, desert, to Painted Desert [au69], 8 days
Inner locations:
Ilya [ae3l], city, 1 day
Oasis [ae3n], oasis, 1 day
Seen here:
Garrison [av0d], garrison, Imperial Empire, with ten peasants, 11 oxen
Pack of wolves [cu4j], wolves, number: 11


| | |
- csv_reader = reader(...)
- csv_reader = reader(iterable [, dialect='excel']
[optional keyword args])
for row in csv_reader:
process(row)
The "iterable" argument can be any object that returns a line
of input for each iteration, such as a file object or a list. The
optional "dialect" parameter is discussed below. The function
also accepts optional keyword arguments which override settings
provided by the dialect.
The returned object is an iterator. Each iteration returns a row
of the CSV file (which can span multiple input lines).
- sqrt(x, /)
- Return the square root of x.
|


|
SuanShu, a Java numerical and statistical library | |||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.numericalmethod.suanshu.number.DoubleUtils
public class DoubleUtils
This class collects the utility functions to manipulate data of types double and int.
These functions are static and stateless.
| Nested Class Summary | |
|---|---|
static class |
DoubleUtils.RoundingScheme
the schemes available to round a number. |
| Method Summary | |
|---|---|
static double[] |
abs(double[] doubles)
Get the absolute values. |
static double |
absoluteError(double x1,
double x0)
Compute the absolute difference between x1 and x0 . |
static boolean |
allZeros(double[] d,
double epsilon)
Check if a double[] array consists of all 0s, entry-by-entry. |
static boolean |
anyZero(double[] d,
double epsilon)
Check if a double[] array has any 0. |
static double[] |
ArrayList2doubleArray(java.util.ArrayList<java.lang.Double> list)
Convert an ArrayList |
static int[] |
ArrayList2intArray(java.util.ArrayList<java.lang.Integer> list)
Convert an ArrayList |
static int |
compare(double d1,
double d2)
Compare two doubles. |
static int |
compare(double d1,
double d2,
double precision)
Compare two doubles. |
static double[] |
concat(double[]... arr)
Concatenate arrays of doubles into one double[] array. |
static java.util.List<java.lang.Double> |
doubleArray2ArrayList(double... arr)
Convert an ArrayList to a double[] array. |
static int[] |
doubleArray2intArray(double... arr)
Convert a double[] array to an int[] array. |
static java.lang.String |
doubleArray2StringArray(double... arr)
Print out the numbers to a String buffer. |
static boolean |
equal(double[][] d1,
double[][] d2,
double precision)
Check if two 2D double[][] arrays are close enough, hence equal, entry-by-entry. |
static boolean |
equal(double[] d1,
double[] d2,
double precision)
Check if two double arrays are close enough, hence equal, entry-by-entry. |
static boolean |
equal(double d1,
double d2,
double precision)
Check if two doubles are close enough, hence equal. |
static boolean |
equal(int[] d1,
int[] d2)
Check if two int[] arrays are equal, entry-by-entry. |
static double[] |
foreach(double[] doubles,
UnivariateRealFunction f)
Get a double array in which each element is the result of applying the function f to the corresponding element in doubles . |
static java.util.ArrayList<java.lang.Integer> |
intArray2ArrayList(int[] ary)
Convert an int[] to ArrayList |
static double[] |
intArray2doubleArray(int... arr)
Convert an int[] array to a double[] array. |
static boolean |
isN(double x)
Check if a double is a number, i.e., it is not ∞ or NaN . |
static boolean |
isPow2(int n)
Check if an integer is a power of 2. |
static boolean |
isThereAnyDuplicate(double... arr)
Check if a double[] array contains any duplicates. |
static double |
max(double... doubles)
Get the maximum of the values. |
static int |
maxIndex(boolean moveOnTies,
int from,
int to,
double... doubles)
Get the index to the maximum of the values. |
static int |
maxIndex(double... doubles)
Get the index to the maximum of the values. |
static double |
min(double... doubles)
Get the minimum of the values. |
static int |
minIndex(boolean moveOnTies,
int from,
int to,
double... doubles)
Get the index to the minimum of the values. |
static int |
minIndex(double... doubles)
Get the index to the minimum of the values. |
static double |
relativeError(double x1,
double x0)
Compute the relative error for {x1, x0} . |
static void |
reverse(double... arr)
Reverse a double[] array. |
static void |
reverse(int... arr)
Reverse an int[] array. |
static double |
round(double d,
DoubleUtils.RoundingScheme dir)
Round up or down a double to an integer. |
static double |
round(double d,
int scale)
Round a double to the precision specified. |
static int[] |
shellsort(double... arr)
Sort the input array using Shell sort. |
static double |
sum(double... doubles)
Get the sum of the values. |
static int |
sum(int... integers)
Get the sum of the values. |
static java.lang.String |
toString(double[][] arr)
Convert a double[][] array to the format that we can copy and paste to re-create the array in Java code. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Method Detail |
|---|
public static int compare(double d1, double d2)
We use this function instead of Double.compare
, because we want to treat 0.0 and -0.0 as the same.
For instance, we want to avoid that
Double.compare(0.0, -0.0)
returns 1
, and
DoubleUtils.compare(0.0, -0.0)
returns 0
.
d1
- a doubled2
- a double
d1
is numerically equal to d2
, 1 if d1 > d2
, -1 if d1 < d2
public static int compare(double d1, double d2, double precision)
d1
- a doubled2
- a doubleprecision
- the precision used for the comparison, e.g., 1e-9
d1
is close enough to d2
, 1 if d1 > d2
, -1 if d1 < d2
public static boolean equal(double d1, double d2, double precision)
d1
- a doubled2
- a doubleprecision
- the precision used for the comparison, e.g., 1e-9
true
iff d1
is close enough to d2
, false
otherwisepublic static boolean equal(double[] d1, double[] d2, double precision)
d1
- a double[]d2
- a double[]precision
- the precision used for the comparison, e.g., 1e-9
true
iff all entries in d1
are close enough to all entries in d2
, false
otherwisepublic static boolean equal(double[][] d1, double[][] d2, double precision)
d1
- a double[][]d2
- a double[][]precision
- the precision used for the comparison, e.g., 1e-9
true
iff all entries in d1
are close enough to all entries in d2
, false
otherwisepublic static boolean equal(int[] d1, int[] d2)
d1
- a int[]d2
- a int[]
true
iff all entries in d1
are the same as all entries in d2
, false
otherwisepublic static double max(double... doubles)
doubles
- an array of doubles
public static double min(double... doubles)
doubles
- an array of doubles
public static int maxIndex(boolean moveOnTies, int from, int to, double... doubles)
moveOnTies
- prefer the later one on tiesfrom
- the initial index of the range to be consideredto
- 1 after the last index of the range to be considereddoubles
- an array of doubles
public static int maxIndex(double... doubles)
doubles
- an array of doubles
public static int minIndex(boolean moveOnTies, int from, int to, double... doubles)
moveOnTies
- prefer the later one on tiesfrom
- the initial index of the range to be consideredto
- 1 after the last index of the range to be considereddoubles
- an array of doubles
public static int minIndex(double... doubles)
doubles
- an array of doubles
public static double sum(double... doubles)
doubles
- an array of doubles
public static int sum(int... integers)
integers
- an array of ints
public static double[] abs(double[] doubles)
doubles
- an array of doubles
public static double[] foreach(double[] doubles, UnivariateRealFunction f)
f
to the corresponding element in doubles
.
doubles
- an array of double
sf
- the function to be applied to each element
public static double[] concat(double[]... arr)
The merged array is not sorted.
arr
- an array of double[] arrays
public static void reverse(double... arr)
A common usage is to call this after Arrays.sort
to sort the array in descending order.
arr
- a double[] arraypublic static void reverse(int... arr)
A common usage is to call this after Arrays.sort
to sort the array in descending order.
arr
- an int[] arraypublic static int[] shellsort(double... arr)
arr
- a double[] array
arr
, according to which the inputs are sorted in ascending orderpublic static boolean allZeros(double[] d, double epsilon)
d
- doubles or a double[]epsilon
- a precision parameter: when a number |x| ≤ ε, it is considered 0
true
iff all entries in d
are close enough to 0, false
otherwisepublic static boolean anyZero(double[] d, double epsilon)
d
- doubles or a double[]epsilon
- a precision parameter: when a number |x| ≤ ε, it is considered 0
true
iff an entry in d
are close enough to 0, false
otherwisepublic static boolean isN(double x)
∞
or NaN
.
x
- a double
true
iff x
is not ∞
or NaN
public static boolean isPow2(int n)
n
- an integer
true
iff n
is a power of 2.public static boolean isThereAnyDuplicate(double... arr)
arr
- a double[] array
true
iff there is a duplicate in the arraypublic static java.lang.String doubleArray2StringArray(double... arr)
arr
- a double[] array
public static int[] doubleArray2intArray(double... arr)
arr
- a double[] array
public static double[] intArray2doubleArray(int... arr)
arr
- an int[] array
public static double[] ArrayList2doubleArray(java.util.ArrayList<java.lang.Double> list)
list
- an ArrayListpublic static java.util.List<java.lang.Double> doubleArray2ArrayList(double... arr)
arr
- a double[] array
public static int[] ArrayList2intArray(java.util.ArrayList<java.lang.Integer> list)
list
- an ArrayListpublic static java.util.ArrayList<java.lang.Integer> intArray2ArrayList(int[] ary)
ary
- an int[] array
public static double round(double d, DoubleUtils.RoundingScheme dir)
d
- the double to be roundeddir
- round up or down or using Java default
public static double round(double d, int scale)
d
- a doublescale
- number of decimal points
public static double absoluteError(double x1, double x0)
x1
and x0
.
This function is symmetric.
ε = | x1 - x0 |
x1
- x1
x0
- x0
public static double relativeError(double x1, double x0)
{x1, x0}
.
This function is asymmetric.
ε = | (x1 - x0) / x0 | = | x1/x0 - 1 |
x1
- x1
x0
- x0
public static java.lang.String toString(double[][] arr)
arr
- a double[][]
|
SuanShu, a Java numerical and statistical library | |||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


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