Instituto de Física Gleb Wataghin
Lucas Toffoli de Menezes
On the efficacy of neurofeedback training:
can subjects learn to regulate their own brain
waves?
Sobre a eficácia do treinamento de neurofeedback:
podem indivíduos controlar suas próprias ondas
cerebrais?
CAMPINAS 2019
On the efficacy of neurofeedback training:
can subjects learn to regulate their own brain
waves?
Sobre a eficácia do treinamento de neurofeedback:
podem indivíduos controlar suas próprias ondas
cerebrais?
Dissertation presented to the Institute of Phy-sics Gleb Wataghin (IFGW) of the University of Campinas (UNICAMP) in partial fulfillment of the requirements for the degree of Master in Physics, in the area of Applied Physics.
Dissertação apresentada ao Instituto de Física Gleb Wataghin da Universidade Estadual de Campinas como parte dos requisitos exigidos para a obtenção do título de Mestre Física, na área de Física Aplicada.
Supervisora/Orientadora: Gabriela Castellano Este exemplar corresponde à versão final da dissertação defendida pelo aluno Lucas Toffoli de Menezes, e orientada pela Profa. Dra. Gabriela Castellano.
CAMPINAS 2019
Biblioteca do Instituto de Física Gleb Wataghin Lucimeire de Oliveira Silva da Rocha - CRB 8/9174
Menezes, Lucas Toffoli de,
M524o MenOn the efficacy of neurofeedback training : can subjects learn to regulate their own brain waves? / Lucas Toffoli de Menezes. – Campinas, SP : [s.n.], 2019.
MenOrientador: Gabriela Castellano.
MenDissertação (mestrado) – Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin.
Men1. Neurofeedback. 2. Eletroencefalografia. 3. Condicionamento operante. I. Castellano, Gabriela, 1970-. II. Universidade Estadual de Campinas. Instituto de Física Gleb Wataghin. III. Título.
Informações para Biblioteca Digital
Título em outro idioma: Sobre a eficácia do treinamento de neurofeedback : podem
indivíduos controlar suas próprias ondas cerebrais?
Palavras-chave em inglês:
Neurofeedback
Electroencephalography Operant conditioning
Área de concentração: Física Aplicada Titulação: Mestre em Física
Banca examinadora:
Gabriela Castellano [Orientador] Romis Ribeiro de Faissol Attux Esther Miyuki Nakamura Palacios
Data de defesa: 28-06-2019
Programa de Pós-Graduação: Física
Identificação e informações acadêmicas do(a) aluno(a)
- ORCID do autor: https://orcid.org/0000-0002-2580-6057 - Currículo Lattes do autor: http://lattes.cnpq.br/1210661347426391
INSTITUTO DE FÍSICA “GLEB WATAGHIN”, DA UNIVERSIDADE ESTADUAL DE CAMPINAS, EM 28 / 06 / 2019.
COMISSÃO JULGADORA:
- Profa. Dra. Gabriela Castellano – Orientadora – DRCC/IFGW/UNICAMP
- Prof. Dr. Romis Ribeiro de Faissol Attux – FEEC/UNICAMP
- Profa. Dra. Ester Miyuki Nakamura Palacios – CB/UES
OBS.: Informo que as assinaturas dos respectivos professores membros da banca
constam na ata de defesa já juntada no processo vida acadêmica do aluno.
CAMPINAS
2019
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
O treinamento de neurofeedback (NFB) vem sendo usado como uma ferramenta de aprimo-ramento cognitivo tanto para pacientes clínicos como para indivíduos saudáveis que almejam melhorar suas performances em tarefas específicas. A abordagem deste treinamento consiste em expor sujeitos à sua própria atividade cerebral em tempo real através de uma simples interface. Então, eles são recompensados sempre que a resposta corresponde a um objetivo específico, predeterminado, seguindo um método de aprendizado denominado condicionamento operante. Escolhendo uma resposta sabidamente relacionada a alguma habilidade cognitiva, a técnica utiliza-se deste sistema de recompensa com o objetivo de reforçar esta resposta, com a intenção de aprimorar a capacidade cognitiva atrelada a ela. O presente trabalho verificou se participantes eram capazes de adquirir controle de suas ondas cerebrais ao longo do trei-namento. Voluntários estudantes foram alocados aleatoriamente em dois grupos: NFB-ativo ou NFB-placebo. Os participantes do primeiro grupo interagiram com seus sinais durante as sessões de NFB, enquanto aqueles do grupo placebo realizaram o mesmo procedimento, mas interagiram com sinais falsos. A performance dos participantes foi medida como a diferença do sinal alcançada a cada sessão, em relação a um período de repouso previamente executado. Todos aqueles do grupo ativo exibiram uma melhora na sua performance ao longo do treina-mento, mesmo as mais sutis. Isto significa que houve uma tendência entre aqueles do grupo ativo de executar melhor a interação nos estágios finais do treinamento, uma característica não observada no grupo placebo (teste de Wilcoxon, p = 0.0028). Aqueles do grupo placebo, ao contrário, exibiram tendências variadas, o que reflete as variações naturais dessas medidas quando a retroalimentação está desligada. Medidas longitudinais foram feitas rastreando o si-nal durante as sessões, tanto nos períodos de tarefa quanto em repouso, não exibiram variações significativas. Apesar disso, a tendência daqueles do grupo ativo de aprimorar suas habilidades de interação com os sinais cerebrais, medidas através da supracitada performance, pode ser interpretada como um processo de aprendizagem. Analisando nossos dados, parece ser possível regular a atividade cerebral mediante retroalimentação. Além disso, como não foram observa-dos efeitos longitudinais, avaliações devem ser feitas em relação a eficácia do treinamento NFB em, de fato, regular o comportamento humano.
Neurofeedback (NFB) training has been used as a tool to enhance cognitive abilities for both clinical patients and healthy subjects aiming to perform better at specific tasks. This cognitive training approach exposes the trainees to their own brain activity in real time through a simple interface. Then, they are rewarded whenever they present the response that matches a specific, predetermined goal, following a learning method called operant conditioning. By choosing a response that is known to be related to some cognitive ability, the technique uses this reward system to reinforce that response, aiming the enhancement of the underlying cognitive capacity as well. The present work verified whether participants were indeed able to gain control over their own brain waves along with the training. Student volunteers were randomly assigned to two groups: active- and placebo-NFB. Participants in the first group interacted with their signals for around seven to ten NFB sessions, while those in the placebo group did the same procedure but interacted with fake signals. Performance was measured as the same-day changes in signal while performing the NFB task, in relation to a previous resting-state measure. All of those in the active-group exhibited improvement of performance, even the slightest ones. It means that there was a tendency of those in the active group to perform better at the final stages of the training, a characteristic not observed in the placebo-group (Wilcoxon’s rank test, p = 0.0028). Those in the placebo group, instead, exhibited a variety of tendencies, which reflects the natural variations of this measures when the feedback is off. Measures of long-term effects, by following the signals over the sessions, exhibited no tendencies for both groups, either by tracking task periods or resting-state ones. Despite that non-significance, the trend between those in the active group at improving their ability of interaction with brain signals, as measured by the above-mentioned performance, can be interpreted as a process of learning. Analyzing our data, it seems to be possible to regulate brain waves via feedback. Moreover, since long-term effects were not observed, further assessments should be made in regard to NFB training efficacy at actually changing human behavior.
2.1 Anisotropic structure of pyramidal cells in three different regions of the cortex. The surface of the brain is at the top of each slice and, although composed by
different types of neurons, all regions present this oriented structure. . . 22
2.2 Closer look at the dipoles forming while pyramidal neurons fire close to the
cor-tex. An EEG electrode is, then, capable of measuring these potentials generated
by this bulk of neurons located below the electrode. . . 23
3.1 g.USBamp USB Biosignal Amplifier. . . 27
3.2 International 10-20 system (extended). Electrodes in yellow represent the sites
measured during the present study. In green, the reference and ground electrodes
placed on the mastoids (behind the earlobes). . . 28
3.3 NFB-software graphical interface built to perform an NFB-training procedure.
In red, bars that achieved the goal (of down-regulation) and, in dark-red, those that did not. New bars, representing the EEG feature being trained, appear at
the right side of the plot. Scores were updated every time a red bar was presented. 29
3.4 Go/No-Go test interface. The letters P and R were presented in one of the four
squares, one at a time, and participants were asked to respond to the target: in the first part, the target was the letter P and, in the second, it was the R. Since 80% of the letters were Ps at both parts, each part represented a different measure of the ADHD symptoms spectra: the first dealt with the hyperactivity
spectrum while the second one dealt with the attention-deficit segment. . . 31
3.5 A single NFB session described schematically. During tasks, participants
inter-acted with their signals aiming to regulate them second-by-second (colored bars
in Fig. 3.3) compared to the previous resting period (horizontal line in Fig. 3.3). 33
3.6 Typical EEG signals. The figure shows a 16-channel measurement during ten
seconds. The scale, in 𝜇V, is shown at the bottom-right. Typical artifacts can be seen, mainly in the first rows that represent more frontal electrode positions.
Near the 213th second, for example, there are peaks in several time series at the
same time. Given that they are present only at those frontal electrodes, they are
probably due to an eye-blinking artifact. . . 34
3.7 DTFT of the signal shown in Fig. 3.6. It is possible to see a peak around 10 Hz
at the transformed signal, for all components, which is reasonable, since it is the
most prominent frequency in a standard EEG signal. . . 34
4.1 3D image representing the scalp of one subject. Different colors mean different
gaps between task and rest measures of power over a specific frequency band. Blue regions represent a reduction in power during the task, in relation to the
Session 0.1 and the second row from Session 0.2. . . 38
4.3 Fisher LDA values for the difference between rsNDP and tNDP, for every
at-tribute. The largest Fisher value indicates the best electrode/band combination to differentiate task from rest. In this example, the best attribute for
discrimi-nating task and rest was the power of alpha band measured by C5 electrode. . . 39
4.4 Per-group-averaged power during NFB-task periods normalized by the
first-session one. There were no significant differences between both distributions. Besides that, mean values of power of those in the active group were higher than
of those in the placebo group, for all sessions . . . 41
4.5 Performance during NFB-task periods normalized by the first-session NFB-task
for all participants. Plots in orange represent the active-NFB participants while
those in black represent the placebo-NFB ones. . . 42
4.6 Performance during task periods in comparison with the first-session
NFB-task averaged for responders and placebos. The first group called responders were gathered by observing which subjects responded to the training by actually enhancing their attribute-power measure. For those, a higher slope indicates a more pronounced change in the measurement than those allocated in the placebo
group. . . 43
4.7 Evolution of baseline across sessions averaged for all participants. There were
no significant variations of this measure through time, which means that
partic-ipants did not transformed their baseline signals due to NFB intervention. . . . 44
4.8 Evolution of baseline across sessions for each participant. The variations
ob-served did not exhibit any group-specific behavior between those that received
feedback and those that received a fake one. . . 45
4.9 Performance during NFB-task periods in comparison with the same-session
resting-state period. Although there seems like a small variation, the distribution
of slopes behind these means yielded a significant difference between groups
(Wilcoxon rank test, p = 0.0028) . . . 46
4.10 Performance during task periods in comparison with the first-session NFB-task for all participants. Plots in orange represent the active-NFB participants
1 Introduction 12
1.1 Historical background. . . 13
1.2 Applications . . . 13
1.2.1 Clinics . . . 13
1.2.2 Cognitive enhancement on healthy subjects. . . 18
1.2.3 Peak-performance athletes . . . 20
1.3 Objectives . . . 20
2 Theoretical foundations 21 2.1 NFB . . . 21
2.2 EEG . . . 21
2.2.1 Origin of the signal . . . 21
2.2.2 Fourier analysis of discrete signals . . . 23
3 Methodology 27 3.1 Equipment . . . 27 3.2 NFB software . . . 27 3.3 Subjects . . . 30 3.4 NFB protocol . . . 30 3.5 Data Analysis . . . 32 3.5.1 EEG-signal processing . . . 32 3.5.2 Outcome analysis . . . 35 3.5.3 Training-efficacy evaluation . . . 35
4 Results and Discussions 37 4.1 Session Zero . . . 37
4.2 NFB training . . . 39
5 Conclusions and Prospects 48 5.1 Regarding results . . . 48
5.2 Practical issues and possible solutions . . . 49
5.2.1 Regularity . . . 49
5.2.2 Playability . . . 49
5.2.3 Classification methods . . . 49
5.3 Future research . . . 50
A Consent term 54
Chapter 1
Introduction
Neurofeedback (NFB) training is a technique that trains individuals to regulate their own brain activity. By continuously extracting measurements from their brains – usually via electroen-cephalography (EEG), functional Magnetic Resonance Imaging (fMRI), or functional Near-Infrared Spectroscopy (fNIRS) –, and feeding them with this information in real time, individ-uals are required to change their signals towards a predetermined goal.
At each period of time, the extracted measurement is compared to a baseline one and dis-played through a computer interface. Subjects interact with it aiming to up- or down-regulate the signal, depending on the protocol. They are rewarded during periods at which the goal is reached; they can also be punished when it is not. This feedback loop allows individuals to self-regulate brain activity through a learning method called operant conditioning; more specif-ically, this method rewards individuals whenever they achieve their goal aiming to reinforce the response that led to it. By choosing specific brain-activity measurements known to be related to some cognitive or motor task, the goal is to enhance these responses, consequently enhancing the ability to perform the task itself.
The set of possible tasks explored by this technique is very broad, ranging from clinical to experimental realms; from monitoring and regulating levels of relaxation to magnifying the presentation of a motor-related signal that controls a prosthesis, from treating seizures to en-hancing the abilities of professional athletes, from relieving the symptoms of depression to enabling locked-in persons to communicate.
While still controversial, this technique seems to have effects on behavior that have yet to be explored. The following presents a review of NFB-training studies and also describes a study performed by the author, his advisor, and others on healthy subjects during a NFB-training intervention.
1.1
Historical background
Since it was first discovered in the 1960s by the Japanese physician Joe Kamiya, the ability to self-regulate brain activity has been explored by a variety of researchers throughout the world. Back then, Kamiya conducted studies using EEG sensors paired with a reward system to determine if individuals could learn to modify their own brain patterns.
In a study published in 1970, Kamiya and Nowlis revealed their results. [1] The majority of its twenty-six participants were able to gain control over the amount of alpha (around 10 Hz) activity recorded at the parietal region of the brain. This was done by only (i) giving participants feedback for the amount of alpha being measured, (ii) requiring them to produce more or less of it, i.e., giving them a goal, and (iii) rewarding them whenever they reached their goal. The data analysis procedure was to count how many seconds – out of 120 – alpha activity was above (or below) certain threshold if the participants were required to increase (or to de-crease) it. The threshold was extracted from a 2-minute baseline condition previously recorded while participants were asked to "relax". Subjects were able to produce around 51% more (and 26% less) alpha activity with eyes open and 35% more (and 72% less) alpha activity with eyes closed on average when required to increase (or to decrease) it. All participants succeeded in producing more alpha activity during the so-called "on" condition – when participants were asked to increase it – and vice-versa, in the "off" condition.
During the 1980s, NFB was sidelined by science, mainly due to flawed studies and clinical overstatements [2]. The subsequent decades, though, brought this issue back into the main-stream of science community. As the ability of brains to physically change themselves has been discovered, on events such as neuroplasticity and neurogenesis, doubts about the potential of this technique have been vanished. However, there are still some barriers to overcome, as stud-ies fail to consent on protocols: number of sessions, randomized trials, and control groups, for instance, are all features that systematically fail to agree between studies and make it hard to find which specificities of the training are affecting behavior.
1.2
Applications
1.2.1
Clinics
Once some unusual brain activity from a person that has a cognitive disorder is recognized, teaching that brain to behave like a healthy one is a very straightforward NFB-training protocol. For example, investigations of spectral activity of the EEG of patients with attention-deficit and hyperactivity disorder (ADHD) have consistently reported an abnormal abundance of slow frequencies such as theta (4-7 Hz), specially over frontal areas, and reduced power in faster
bands, such as alpha and beta (8-12 Hz and 12-22 Hz, respectively). [2] The procedure is, then, to extract an EEG parameter – for example, the amount of theta activity in the frontal lobes – and train that person to regulate it – in this example, to present less of these slow waves. To do so, a baseline measurement previously extracted during a resting period (thresh-old) is constantly presented while showing the present, real-time measurement of that same EEG parameter. Then, the patient is requested to stay below that baseline measurement for as long as he/she can, aiming to present less and less of these slow waves. As the sessions go on, the patient learns to have more sophisticated control over that measurement and the expected behavioral outcome is the reduction of related symptoms.
In a study from 2009, Gevensleben et al. reported that children with ADHD benefited from NFB training. [3] In a randomized controlled clinical trial, they performed and compared two different types of cognitive training: NFB and a so-called attention skills training (AST). One-hundred-and-two children with ADHD were randomly assigned to one of the two groups. There were no group differences in regard to demographic, psychological and clinical variables. Also, all participants fulfilled DSM-IV criteria for ADHD, which is a diagnostic assessment of this disorder.
NFB training consisted of two 18-session blocks (one block of slow cortical potentials (SCP) and another block of theta/beta training).1 Children allocated in the AST group, performed a
computerized training where children interacted with computer animations but did not received feedback from any neural sources.2 Both trainings consisted of computer-game-like tasks that
demanded attention, development of strategies for focusing, and practice of those strategies at home. Assessments of parents and teachers regarding their children were done at three points in time (pre-, intermediate-, and post-training). In all of them, NFB outperformed AST train-ing for both parents’ and teachers’ scores. More specifically, on the sub-scale level of one of the assessments, improvements of inattention and hyperactivity/impulsivity were significantly larger (25-30%) in NFB group than in the AST group (10%). Another assessment was the responder rate, which measures how many children responded to each treatment. Responders were chosen to be the ones that improved symptoms on the primary scale by up to 25%. For the NFB group, 51.7% of children achieved that threshold, while in the AST group, 28.6% did. Egner et al. (2001) presented another approach. They measured the amplitudes of an evoked potential called P300. This potential emerges mainly in parietal regions and is affected
1For the SCP training, participants were asked to direct a ball on the screen either upwards or downwards,
while the theta/beta training consisted of two bars, one on the left of the screen (theta bar that was supposed to be down-regulated) and another on the right (beta bar that was supposed to be up-regulated).
2The AST training was based on ‘Skillies’ (Auer-Verlag, Donauwörth, Germany), an award-winning German
learning software, which primarily exercises visual and auditory perception, vigilance, sustained attention, and reactivity.
by subjective stimulus occurrence probability. 3 This phenomena has "been conceptualized as
stemming from neuronal systems serving to update biologically relevant stimulus environment information in working memory." [4] It is also known that children with ADHD exhibit smaller P300 amplitudes than controls, so this is a relevant measure for this particular disorder. They assessed the amplitudes of P300 before and after the training, and also the errors executed during a cognitive task. They saw that there was a direct correlation between enhancement of the so-called sensorimotor-rhythm band (SMR, 12-15 Hz) and reduction of errors during the task; also, they saw that this and the enhancement of the beta1 band (15-18 Hz) was both associated with significant increases in P300 amplitudes during an auditory oddball task. Ac-cording to the authors, "these relations were interpreted as stemming from band-specific effects on perceptual and motor aspects of attention measures." [4] Therefore, the authors presented a efficient way of enhancing a response that is known to be deficient in ADHD patients and the related behavior of improving performance on an attention task was also verified by the decrease of errors during the task for most participants of the study.
NFB has also been evaluated against other cognitive-training techniques and standard phar-macological treatments. In a comparison between NFB training and the standard pharma-cotherapy (methylphenidate administration) in children with ADHD, Lutzenberger et al. (2003) performed a study with two groups where children were assigned into by parents’ choice. They concluded that, although both groups exhibited improvement of ADHD symptoms, there were no significant differences between them, i.e., NFB had the same performance as the standard treatment. Since NFB has no known side effects, it is clear that drug administration should be avoided in this case, mainly because they were dealing children. [2] This result exposes the importance of investigating this technique as a potential tool for dealing with such disorders.
Another study [5] also compared drug administration with NFB training in relieving symp-toms of ADHD, but they got different results. In this work, the authors randomly assigned thirty-two medication-naïve ADHD patients from a neuropsychiatric clinic to two groups: 30-session NFB intervention or drug treatment. They assessed the efficacy of both interventions through parent and teacher forms, which was considered the primary outcome measure, in addition to quantitative-EEG (QEEG) and event-related potentials (ERPs). They found that the primary outcome (i.e., parents and teachers evaluations) favored medication instead of NFB. This result was verified with the ERPs, more specifically, the P300 amplitude during a Go/No-Go attention task. As mentioned earlier, patients with ADHD tend to present dimin-ished amplitude of this ERP. In this work, 8 out of 12 of those who had a clinically relevant medication effect presented a augmented P300 amplitude, while those that did not respond to medication nor those in the NFB group presented P300-amplitude differences after the
inter-3That is, whenever you have a low probability of something to occur, when it does happen, this characteristic
vention. Also, QEEG spectral power did not change in either group.
Although several improvements in ADHD symptoms were reported on these previously men-tioned, they lack assessments of the training effects on the measured signal. They did not assess if participants were actually able to gain control over their signals after the sessions were done. Nor if participants were exhibiting more pronounced amplitudes of waves over the range of frequencies under training. Their results were only based on parents and teachers assessments of ADHD symptoms, or in secondary EEG measures, which are valuable but do not verify the main characteristic of the training, which is whether participants learn to gain control over their EEG signals as NFB training unfolds or not.
Other studies did perform such a measure. Janssen et al. (2016) trained thirty-eight children with diagnosed ADHD in an intervention that had roughly 30 sessions per individual on aver-age. The protocol was a theta-beta ratio (𝜃/𝛽) down-regulation training, which is a common protocol for ADHD patients. During the training, the approach is to decrease the amplitude of 𝜃/𝛽, following the evidence that such patients presents more slow waves (such as theta) and less fast waves (such as beta). To overcome this deficit, they trained these individuals to either increase beta or to decrease theta. They evaluated at each session if participants were able do so. They saw that a great part of participants gained some control over their frequency spec-trum, mainly by increasing beta (theta remained unchanged throughout the sessions.) There "was a linear increase in participants’ mean training level, highest obtained training level, and the number of earning credits." [6] Although this was a compelling result towards NFB efficacy, there were no correlations between control of the signal and behavioral outcomes. The authors said that "a lack of behavioral correlates may indicate insufficient transfer to daily functioning, or to confounding reinforcement of electromyographic activity." [6]
Another powerful use of NFB is to aid the usage of an external device controlled by the brain. Individuals that have lost the ability to control some part of their body, either because of a disease, such as stroke or amyotrophic lateral sclerosis (ALS), or because they have lost a limb, brain-computer interfaces (BCIs) can help them recover their mobility using external, robotic prostheses controlled by the brain. The idea is to extract signals from their brains and feed the prostheses with that input in order to execute movements. This can be an arduous task for patients, mainly within the first contacts with the prostheses. Therefore, to help in-dividuals at exhibiting the desired brain activity that moves the prosthesis in a determined manner, a feedback loop can be used to feed them with the accuracy at which their brain waves are exhibiting the required responses. As the training procedure develops, the individual learns to exhibit fine-tuned responses that control the device properly.
NFB training can play a role at enabling locked-in patients to communicate. As ALS de-velops, it withdraws patients the ability to control any muscle of their body, blocking their communication with the outside world. So they are aware of the outside and inner worlds, but
cannot express it with any movement. A study published back in 1999 showed that two ALS patients, who were unable to use a muscle-driven communication device (locked-in stage), were able to control their slow cortical potentials (SCP) in a way that provided them the ability to write words and sentences. By dividing the alphabet in half in every step until only one letter remains, both patients acquired the ability to enhance their SCPs amplitude in order to choose the half that contained the desired letter. They learned it by receiving feedback of the amount of SCPs during a long number of NFB sessions. By the end of the training, both subjects acquired the ability to write sentences, an accomplishment done by no other locked-in individual, at the time of the article, in 1999. [7]
Citing another counter example of improvements after an intervention of NFB training, a study performed the training with 25 patients of insomnia (randomly assigned into two groups: genuine NFB and sham-NFB), and verified that there were no differences in outcome for those allocated in the active group in relation to those that performed sham-NFB training. although both groups exhibited enhance perception of sleep quality. They suggested that "the observed improvements were due to unspecific factors such as experiencing trust and receiving care and empathy from experimenters." [8] So, they related the subjective improvements described by the participants as being placebo-derived. Also, "objective [EEG] measures that potentially reflected mechanisms underlying the efficacy of [NFB] such as spectral [EEG] measures and sleep spindle parameters remained unchanged following 12 training sessions." [8] This follows by the fact that objective EEG-measurements of sleep remained unchanged for both groups, which puts the feedback itself in question, since there were no differences between those who received the true feedback and those who received the placebo one.
A nice commentary on this study was performed by Thibault et al. (2017). They reviewed some issues on the research sphere of NFB training, such as the remark that few studies have included the necessary control groups and experimental designs to directly test NFB efficacy. They argue that Schabus et al. (2017) indeed performed well designed, randomized, double-blind, sham-controlled trials. According to the authors, another issue usually observed in a variety of studies is that they "rarely probe whether participants master control over brain activity." The study being observed did perform this measurement, and concluded that those in the genuine group achieved the desired response of amplifying a subset of brain signals during training, but this was independent of behavioral improvement.
The question then is, which measurement is more relevant? Participants of the insomnia study reported improvements on sleep quality. The authors point out that "[t]he positive sub-jective outcomes Schabus et al. observed might appear sufficient to advocate for neurofeedback; after all, the sleep complaints, which led individuals to seek help, subsided." [9] On the other hand, these improvements were not accompanied by enhanced objective EEG-measurements of sleep quality after the training. Leaving subjects with the impression of better sleep quality
not accompanied by healthy EEG-measurements during sleep can lead to long-term negative effects. [9]
1.2.2
Cognitive enhancement on healthy subjects
A variety of studies have reported the ability of healthy subjects to gain control of their own brain waves. Also, a significant amount of these studies describe enhancements in aspects of cognition due to this practice. The following paragraphs describe some examples of successes and failures in protocols that aimed the enhancement of cognitive abilities on healthy subjects. This subsection is highlighted because it is closely related to the subject of the present docu-ment. Also, more details were added about the methodology and specific results in order to provide ways of comparison with the research performed by us and described here.
Egner and Gruzelier published a study in 2001 revealing that healthy students trained by NFB procedures similar to those applied to children with ADHD were able to manifest better performance on an attention test called Go/No-Go test, which is also used to measure symp-toms of ADHD. During the training, participants were given feedback about how well they were performing at: (i) increasing activity over SMR (12-15 Hz) and beta1 (15-18 Hz) of the EEG spectrum, while (ii) decreasing activity of theta (4-7 Hz) and high beta (22-30 Hz). Whenever this response was reached they were given reward in the form of points. The entire training lasted ten sessions, twice a week, and after that, comparison between pre- and post-training measures showed that participants significantly reduced errors in the attention test. Moreover, that reduction could be predicted by a linear regression model based on success at learning to increase SMR and beta1 activity. [4]
By training to down-regulate alpha oscillations, a group of researchers saw that those who decreased their alpha power over the parietal region exhibited lower mind-wandering behavior during a specific task, in comparison with another group that surpassed the same procedure but with fake feedback (placebo-NFB). Also, they performed fMRI measurements before and after the intervention and observed an amplified connectivity in the dorsal anterior cingulate, which is a region that relates to alertness. They proposed that this enhanced connectivity, "in-directly induced by NFB, could be representative of enhanced tonic alertness/error monitoring demands in order to maintain task-set and attentional engagement." [10]
Another study described an upper-alpha (UA) NFB-training on healthy subjects [11]. En-hancement of this frequency band has already been correlated with higher performance on cog-nitive tasks. In this study, the authors assessed each participants Individual Alpha Frequency (IAF), a peak which varies around 10 Hz between subjects. By measuring the frequency of the peak for each participant, the lower and upper bands were individually determined. These
measures were done over P3, P4, Pz, O1, and O2 electrodes, according to the 10/10 system4;
these locations were called feedback locations. The study revealed an "[...] enhancement [of the UA] during the active tasks which [was] independent of other frequency bands. UA was also enhanced in the passive state but independence could not be obtained in lower alpha band. Finally, significant improvement in working memory was obtained with regard to a control group." [12]. In summary, they measured cognitive improvement for those that went through an UA-NFB intervention, in comparison with a group of participants that went through the same process, but received sham feedback. Also, those that went through the real intervention were able to enhance their UA amplitudes without interference with other frequency bands, what demonstrates the efficacy of this particular training on the regulation of this frequency band through feedback.
One study aiming to verify if NFB could enhance intelligence was performed by Keiser et al. (2010). High-frequency oscillations, such as gamma (usually >30 Hz), have already been correlated with intelligence in previous studies. Therefore, the training was set to up-regulate gamma power while imposing that this up-regulation was accompanied by no increase in beta power. The opposite was done with the other group (increase beta power while do not increase gamma). They saw that participants in the first group were able to enhance the gamma power and that this increase was followed by an enhancement of intelligence, in comparison with the second group, that presented no differences regarding intelligence. This led to a confirmation that magnified gamma power is related to intelligence, and also, that it is possible to amplify this feature through NFB leading to an augmented intelligence level of healthy participants following the training. [13]
NFB for improving sleep parameters and their impact on memory performance was also investigated. On one study, researchers performed a SMR-conditioning, which aims to regulate SMR rhythm via NFB. SMR is known to be abundant during light non-rapid eye movement (NREM) sleep. They saw that "robust and significant SMR amplitude changes from early to late conditioning sessions confirmed the success of the [training]. Furthermore, and most inter-estingly, these EEG changes transferred into sleep [...] and even improved immediate memory retrieval after learning." This study showed a significant improvement on sleep scales for the experimental group, in comparison with a control group that trained other frequency ranges and exhibited no further improvements on sleep scales, which led to an understanding that SMR-NFB has an impact on some characteristics of sleep and can be used as an alternative
4Originally referred to as 10/20 system, it is an internationally accepted electrode-position method for
determine specific locations of scalp EEG-electrodes. This name was defined after the procedure at which this method define scalp locations: distances between adjacent electrodes are either 10% or 20% of the front-back and right-left distance of the skull, measured from nasion to inion, in the first case, and between ears over the top of the head, in the last. The 10/10 system allocates more electrodes in between adjacent electrodes from the 10/20 system. Nowadays, there are even more electrode positions, but the nomenclature remains usually 10/20 (or even extended 10/20) system.
approach for those who seek a better night’s sleep. [13]
1.2.3
Peak-performance athletes
Peak-performance athletes tend to seek ways to improve their skills even further, sometimes in innovative ways. The gap in performance between these competitors tends to be very small, so the slightest improvement can provide the advantage necessary for the victory.
In a study published in 2014, Kao et al. showed that a single-session of NFB could enhance the performance of professional golf players. The training was based on reducing the amount of the so-called frontal midline theta (𝐹 𝑚𝜃). All three professional players involved in the study increased their putting score and reduced its standard deviation (that means that they were more stable throughout the plays). They concluded that "reduced resting 𝐹 𝑚𝜃 power may be indicative of superior tonic sustained attention, leading to improved putting performance." [14]
1.3
Objectives
The aim of this work was to develop an EEG-based neurofeedback system and assess if healthy subjects could learn to regulate their own brain waves. We wanted to verify if (and how) participants were gaining control of their signals along the sessions. To do so, an NFB software was developed under the MATLAB platform; it measures, processes, extracts a feature of the signal, and presents it through an interface to the participants in real-time, so they can interact with and learn to control it. This feature was individually selected during an attention task that determined which aspect of the signal was related to this cognitive ability for each participant (in comparison with a baseline, resting period). During a training session, participants were asked to amplify those features by "selecting the mental strategies that lead to more points".
Our objective was to understand at what level participants gain control over their waves by simply observing how well they performed at each training session and by evaluating this performance throughout the sessions. By comparing those allocated in the so-called active-NFB group with those in the placebo-active-NFB group, we evaluated the role of feedback itself on the regulation of brain activity.
Chapter 2
Theoretical foundations
2.1
NFB
As previously mentioned, NFB works by continuously extracting some signal of subjects’ brains and requiring them to control it in order to achieve some predetermined goal. When subjects achieve the response that leads to the goal, they are rewarded in some form (such as points or pleasant sounds) in order to show the participants if their mental strategies are leading to the required response. This form of learning mechanism is called operant conditioning, and it has been used for a variety of training techniques since it was first described by B.F. Skinner. He said that "through operant conditioning, an individual makes an association between a partic-ular behavior and a consequence." [15]
Advances of computational neuroscience have allowed the development of ways to under-stand the underlying mechanisms enabling trainees to modulate their own EEG frequencies. One recent study, published in 2018 by Davelaar, developed a computation-theoretic approach for the mechanisms of NFB. There are studies that suggest a big role of striatum – a part of the brain that is thought to play a role in value-based decision making [16]. Another crucial region of the brain that relates to the training is the frontal region since it is responsible for searching the appropriate mental strategy that enables trainees to select the appropriate mental representations that lead to changes in their signals.
2.2
EEG
2.2.1
Origin of the signal
The arrangement of pyramidal neurons - cortical cells that are the main constituents of the outer layers of the cortex - is highly anisotropic: they are oriented perpendicularly to the
surface of the brain (Fig. 2.1)1. When these cells start to communicate with each other in
this bulk, they form current dipoles at their synaptic gaps that are magnified by the oriented structure of these neurons. The cortex of the brain can be separated anatomically by two
Figure 2.1: Anisotropic structure of pyramidal cells in three different regions of the cortex. The surface of the brain is at the top of each slice and, although composed by different types of neurons,
all regions present this oriented structure.
types of structures: gyri and sulci. The gyri are the regions that are more exposed to the outside whereas the sulci are those river-like structures presented throughout the cortex. The activity of this aligned arrangement of pyramidal cells in the gyri is known to be the major contributor of the measured EEG signal since it has a preferential direction at which the individual contributions sum up. The ones located at the sulci are believed to vanish since each region has its counterpart in the opposite direction. In summary, the dominant theory is that these pyramidal cells’ synaptic potentials form current dipoles and that these latter contribute to the dynamics of currents that is measured by an EEG equipment (Fig. 2.2). [17] It is important to note that not only the magnitude of independent current dipoles contribute to the measured signal, but also their temporal synchronization across space. It is possible that changes in synchronization alone are responsible to enhance scalp surface signals. In a study published in 2012, Musall et al. measured both EEG signals and local field potentials (LFP), which is a measure done inside the cortex, next to a few (hundreads or even thousands of) neurons, also called intracortical measurement. They showed that "trial-by-trial fluctuations
1Drawing by Santiago Ramón y Cajal, known to be the father of modern neuroscience for its discovery of
the neuronal structure of the brain, i.e., the non-continuity of nervous structures, observed to be constituted by discrete entities, called neurons.
Figure 2.2: Closer look at the dipoles forming while pyramidal neurons fire close to the cortex. An EEG electrode is, then, capable of measuring these potentials generated by this bulk of neurons
located below the electrode.
in EEG power could be explained by a linear combination of LFP power and interelectrode temporal synchrony. [...] Taken together, [their] results demonstrate that neural synchrony can modulate EEG signals independently of amplitude changes in neural activity." [18] This study directly adressed the synchrony between pyramidal neurons’ firing as one of the most relevant contributions to EEG signals. The authors achieved this by measuring the activity on the visual cortex of monkeys by both placing an EEG-electrode on their scalp and by surgically implanting intracortical electrodes below the area at which the EEG electrode was recording activity. They saw that there were significant correlations between trial-by-trial fluctuations in LFP power, spatial coherence and EEG power measures, which made them conclude that those were the major contributors to the scalp-measured EEG signal.
2.2.2
Fourier analysis of discrete signals
As an informal definition of spectral estimation, Stoica and Moses (2005) define its essence: "From a finite record of a stationary data sequence [discrete signal], estimate how the total power is distributed over frequency." [19] The analysis of the frequency content of signals has been vastly used as an approach to extract information from a time series, and it has been
broadly applied to EEG signal analysis.
An EEG signal can be considered as random since it cannot be determined exactly by the previous data available, but only be specified in statistical terms of averages. Although being considered that way, we will start with the presentation of the theory of deterministic signals, and then we will approach the problem of randomness.
The Discrete-Time Fourier Transform (DTFT) of a discrete, deterministic signal 𝑦(𝑡) is defined as 𝑌 (𝜔) = ∞ ∑︁ 𝑡=−∞ 𝑦(𝑡)𝑒−𝑖𝜔𝑡 (2.2.1)
Its inverse is then
𝑦(𝑡) = 1 2𝜋
∫︁ 𝜋
−𝜋𝑌 (𝜔)𝑒
𝑖𝜔𝑡𝑑𝜔 (2.2.2)
By defining an entity called Energy Spectral Density as follows
𝑆(𝜔) = |𝑌 (𝜔)|2 (2.2.3)
A straightfoward calculation gives
1 2𝜋 ∫︁ 𝜋 −𝜋 𝑆(𝜔)𝑑𝜔 = 1 2𝜋 ∫︁ 𝜋 −𝜋 ∞ ∑︁ 𝑡=−∞ ∞ ∑︁ 𝑠=−∞ 𝑦(𝑡)𝑦*(𝑠)𝑒−𝑖𝜔(𝑡−𝑠)𝑑𝜔 = ∞ ∑︁ 𝑡=−∞ ∞ ∑︁ 𝑠=−∞ 𝑦(𝑡)𝑦*(𝑠) [︂ 1 2𝜋 ∫︁ 𝜋 −𝜋 𝑒−𝑖𝜔(𝑡−𝑠)𝑑𝑤 ]︂ ⏟ ⏞ 𝛿𝑙,𝑠 = ∞ ∑︁ 𝑡=−∞ |𝑦(𝑡)|2 (2.2.4)
Which can be rewritten as
∞ ∑︁ 𝑡=−∞ |𝑦(𝑡)|2 = 1 2𝜋 ∫︁ 𝜋 −𝜋 𝑆(𝜔)𝑑𝜔 (2.2.5)
Expression2.2.5 is identified as Parseval’s Theorem. It shows that the quantity 𝑆(𝜔) repre-sents the distribution of sequence energy as a function of frequency. For that reason it is called energy spectral density. The interpretation of this can be thought as follows: Eq. 2.2.2
"repre-sents the sequence {y(t)} as a weighted ’sum’ (actually, an integral) of orthonormal sequences {︁ 1 √ 2𝜋𝑒 𝑖𝜔𝑡}︁(𝜔 ∈ [−∞, ∞]), with weighting √1 2𝜋𝑌 (𝜔) [...] Therefore, 1 √
2𝜋|𝑌 (𝜔)| shows how much (or how little) of the sequence {y(t)} can be ’explained’ by the orthonormal sequence{︁√1
2𝜋𝑒
𝑖𝜔𝑡}︁
for some given value of 𝜔." [19] The transformed signal can be called the frequency content of the original signal.
By defining 𝜌(𝑘) = ∞ ∑︁ 𝑡=−∞ 𝑦(𝑡)𝑦*(𝑡 − 𝑘) (2.2.6)
It is possible to verify that
∞ ∑︁ 𝑘=−∞ 𝜌(𝑘)𝑒−𝑖𝜔𝑘 = ∞ ∑︁ 𝑘=−∞ ∞ ∑︁ 𝑡=−∞ 𝑦(𝑡)𝑦*(𝑡 − 𝑘)𝑒−𝑖𝜔𝑡𝑒𝑖𝜔(𝑡−𝑘) (2.2.7) = [︃ ∞ ∑︁ 𝑡=−∞ 𝑦(𝑡)𝑒−𝑖𝜔𝑡 ]︃ [︃ ∞ ∑︁ 𝑠=−∞ 𝑦(𝑠)𝑒−𝑖𝜔𝑠 ]︃* (2.2.8) = 𝑆(𝜔) (2.2.9)
This shows that 𝑆(𝜔) can be obtained as the DTFT of the autocorrelation (Eq. 2.2.6) of the finite-energy sequence {𝑦(𝑡)}.
This previous approach to deterministic signals will be extended now to random sequences. As briefly described earlier, a random sequence is one that cannot be characterized in the future using the available data previously recorded. This leads to the fact that realizations of a random signal, viewed as discrete-time sequences, do not have finite energy; in fact, they have finite average power and, therefore, can be characterized by an average power spectral density (PSD), as an analogy to the previously defined energy spectral density for the deterministic case.
As we deal with randomness, we begin to treat physical quantities as expected values. So, the discrete-time signal {𝑦(𝑡); 𝑡 = 0, ±1, ±2, ...} is assumed to be a sequence of random variables with zero mean:
𝐸{𝑦(𝑡)} = 0 for all t (2.2.10)
Then, the autocovariance sequence is defined as
The assumptions described in Eqs. 2.2.10 and 2.2.11 imply that {𝑦(𝑡)} is a second-order stationary sequence and an EEG signal fits this definition.
The PSD of a stationary signal is defined as the DTFT of the covariance sequence:
𝜑(𝜔) = ∞
∑︁
𝑘=−∞
𝑟(𝑘)𝑒−𝑖𝜔𝑘 (2.2.12)
Note that this definition is similar to the definition of 𝑆(𝜔) for the deterministic case. The inverse form, which recovers {𝑟(𝑘)} from a given 𝜑(𝜔), is
𝑟(𝑘) = 1 2𝜋
∫︁ 𝜋
−𝜋𝜑(𝜔)𝑒
𝑖𝜔𝑘𝑑𝜔 (2.2.13)
From Eq. 2.2.13, we obtain
𝑟(0) = 1 2𝜋
∫︁ 𝜋
−𝜋𝜑(𝜔)𝑑𝜔 (2.2.14)
Since 𝑟(0) = 𝐸{|𝑦(𝑡)|2} measures the (average) power of {𝑦(𝑡)}, the equality 2.2.14 shows that 𝜑(𝜔) can indeed be named PSD, as it represents the distribution of the (average) signal power over frequencies. [19]
By integrating 𝜑(𝜔) over some specific frequency range (for example, from 8 to 12 Hz, also called alpha band), we can estimate the power of that specific range. That is the major usage of this technique as we implemented it computationally in order to extract the frequency content of the EEG signals analyzed in the present work.
Chapter 3
Methodology
3.1
Equipment
The EEG equipment used on this work was a g.tec’s g.USBamp USB Biosignal Amplifier (Fig.
3.1). It is a dry-electrode equipment that measures electric potentials at the scalp’s surface at a sampling rate of 256 Hz. Usually, the electrodes are positioned in some determined places on the scalp, following the 10-20 extended system (Fig. 3.2).
Figure 3.1: g.USBamp USB Biosignal Amplifier.
3.2
NFB software
A NFB-software was developed for this work. It (i) extracts the input signal from the EEG-equipment, (ii) processes it, by first rejecting movement and eye-blinking artifacts1,
transform-1The script FORCe [20] was used on this step. It uses state-of-the-art methods for online artifact rejection,
Figure 3.2: International 10-20 system (extended). Electrodes in yellow represent the sites measured during the present study. In green, the reference and ground electrodes placed on the
mastoids (behind the earlobes).
ing the signal via Fourier analysis2 and estimating the percentage of power over each frequency
band, and (iii) feeds back to the user, via the interface, its performance, including the thresh-old (goal) and the EEG measure in real time. The software was created by the experimenters using MATLAB’s graphical-user-interface (GUI) tool for its interface, and the usual MATLAB scripting tool for its underlying online processing, ranging from pre-processing through feature extraction. The interface exhibits (feeds back), in real time, the participants’ attribute power as a bar graph. (This "power" measure shown was actually the percentage of power of their individual (frequency band, electrode position) pairs, in relation to the whole 4-30 Hz spectra, which was the range investigated in this work; see 3.4 below.)
For each second, this process loops and participants are then able to interact with their own signal in real time. In Fig. 3.3, the bars representing the average power of the alpha frequency band for channel 5 are displayed, second by second, consecutively. In this particular example, the participant was required to decrease this measurement, as can be seen in the bottom-right corner of the interface ("Training Type: down", meaning down-regulation of the attribute’s power). Hence, each bar below the threshold line is colored in red, demonstrating that the goal was achieved in that particular second of data. Also, they gained one point for each bar that reached the goal. The duration of the session is also being displayed (in this frame, 44 seconds
had already passed, and the participant scored 20, i.e., out of the first 44 seconds, the goal was achieved 20 times).
Figure 3.3: NFB-software graphical interface built to perform an NFB-training procedure. In red, bars that achieved the goal (of down-regulation) and, in dark-red, those that did not. New bars,
representing the EEG feature being trained, appear at the right side of the plot. Scores were updated every time a red bar was presented.
An step usually performed in processing EEG signals is called artifact rejection. Since the equipment is extremely sensitive, due to its burden of measuring the brain through the scalp, any muscle movements or eye-blinks interfere on the measurement and have to be removed from the original signal before the next steps of processing and feature extraction continue. Thus, an automated online artifact-removal method called FORCe [20] was used in this work to remove these artifacts. This script combines wavelet decomposition, independent component analysis (ICA), and thresholding. This was executed in real time, which sacrifices accuracy for the sake of real-time artifact removal. Despite this limitation, this method outperformed state-of-the-art online artifact-removal methods, as the authors showed in their paper. [20] This tool performed all the steps to clean 1 second of signal in around 500 ms, which was sufficient for the task in hand. Adding some extra few milliseconds for extracting the feature of the cleaned signal – i.e., performing the DTFT and summing the values inside the frequency band –, the software was able to feedback to the participant with a delay of 1 second approximately. Then, each bar in the plot has information of the one-second period that ended 1 second before that bar was shown. For example, in Fig. 3.3, the last bar on the right refers to the 44th-second bar, so it has information of signal’s band power collected between the 42nd and 43rd second of data.
This was sufficient to call it real-time feedback.
3.3
Subjects
We recruited twenty-seven subjects, from which eighteen (age = 26.8 ± 5.0 years, seven women) continued to the final stages of the 10-sessions NFB-training (i.e., did at least seven sessions). All of them were, in some way, connected to the university campus (undergraduate and graduate students). They were recruited via social media in the official university’s social-media page. Inclusion criteria were: (i) participants over the age of 18, and (ii) without diagnosed cognitive disorders. The study was approved by the University’s Ethics Committee3
and all participants signed a consent term4for their participation (see Appendices section (5.3)).
They were aware that the experiment included a placebo group and that the chance of being assigned to it was 50%. Then, they were randomly allocated to one of two groups: active- or placebo-NFB (from which 7 and 11 individuals, respectively, completed the training in terms of number of sessions.) There were no significant differences between groups regarding age and gender (Fisher’s exact test, p = 0.147), although there were 14.3% and 54.7% of women in the active- and placebo-NFB group, respectively.
3.4
NFB protocol
Session Zero
The present study’s strategy was to account for natural variations between individuals’ brain activity during attention tasks. In order to implement this individuality, each participant performed an attention-demanding task called Go/No-Go test, while EEG measures were being done. This test presents a series of letters P and R, one at a time, in one of four positions (Fig.
3.4). In the first part, participants were asked to respond with the mouse click whenever they saw the letter P (target) and not to respond when the letter R was presented. 80% of the letters presented were Ps, so this part dealt with the hyperactivity spectrum of the ADHD5. After a
short relaxing time (usually around 30 seconds, but each participant was free to rest for any time), the second part began. This time, participants had to respond to the letter R instead. Since the rate of Ps remained the same, this part dealt with the attentional spectra. Offline analysis, then, compared this signal to a previous, two-minute baseline measurement extracted
3"Comitê de Ética em Pesquisa da Unicamp – Campus Campinas", in Portuguese. 4"Termo de Consentimento Livre e Esclarecido", in Portuguese.
5Remembering that this test is used as a psychological assessment of ADHD. That’s why they address such
types of measures. In the present work, the test was only used in order to evoke an attentive state in the participants.
immediately before this task. Therefore, each participant’s response due to the emergence of an attentive state was computed individually.
Figure 3.4: Go/No-Go test interface. The letters P and R were presented in one of the four squares, one at a time, and participants were asked to respond to the target: in the first part, the
target was the letter P and, in the second, it was the R. Since 80% of the letters were Ps at both parts, each part represented a different measure of the ADHD symptoms spectra: the first dealt with
the hyperactivity spectrum while the second one dealt with the attention-deficit segment.
The analyzed features of the signal were related to its frequency content. The range of frequencies observed (4 to 30 Hz) was separated in five frequency bands – theta (4-7 Hz), alpha (8-12 Hz), SMR (12-15 Hz), low beta (12-20 Hz), and high beta (20-30 Hz). For each individual, we calculated the feature that best discriminated attention from rest by performing a Fisher Linear Discriminant Analysis (LDA) regarding the distributions of powers at both conditions. This filter ranks the pairs (frequency band, electrode position) that best distinguish these distributions (rest vs. task). It does so by ranking the pairs that (i) maximize the difference between means and (ii) minimize the difference between variances of these distributions of power. By selecting the feature that scored the best on this rank for each participant, the training was personalized.
NFB sessions
Participants included in this study were called for a 10-sessions NFB protocol. They were free to choose between two and three sessions per week. In practice, they went through 7 to 10 sessions of NFB training (mean ± SD = 9.00 ± 1.13) and the rate of sessions per week was varied, and latter neglected, due to some practical issues discussed in Chapter4. Each training session was divided into three 2-minute resting periods – before, between, and after task periods
– and two 4-minute task periods, when participants interacted with their own signals (via the interface shown in Fig. 3.5). Before each session, participants sat in a comfortable chair and the EEG cap was set. Then, the software was fed by the experimenter concerning their individual attributes selected in Session Zero – the pairs (frequency band, electrode position). Next, a verification of the electrodes’ impedances was made and the session began only if all of them were below 5 kΩ. This measure verifies how good is the contact between electrode and scalp. If they were not below this threshold, the cap was adjusted until this requirement was fulfilled, and hence the NFB software was able to run.
Explicitly, each session started with a first resting-state period, when a message was pre-sented to the participants asking them to stay at rest6. During this period, the software
extracted the power of the participant’s attribute by the second, and then averaged this 120-values distribution. This quantity was then presented as a horizontal line at the main plot of the interface (see Fig. 3.3). This line sets the participant’s goal; each one of them attempted to surpass or stay below it, depending on the protocol. Then, at each second a bar appeared on the screen consecutively, indicating the value of that same quantity in real time7. (Also, see
Fig. 3.3.) Participants were then asked to up- or down-regulate the bars, reaching their pre-determined goal whenever they could, by simply trying different thoughts or mental strategies and observing the behavior of the bars. They were encouraged to verify whether their mental strategies were having the desired effect8. Participants scored one point for each successful
bar, as a way to highlight the successes. Immediately after the four minutes of NFB training, the previous rest/task sequence was repeated, followed by a final 2-minute resting-state period. The scheme in Fig. 3.5 shows what a single session was like.
3.5
Data Analysis
3.5.1
EEG-signal processing
An EEG measurement typically looks like the one shown in Fig. 3.6. It is composed of several time series of data representing the variation of the electric potential measured by each of the electrodes that are in contact with the human scalp. Typically, it is very difficult to make any statement about the characteristics of an EEG time series by itself, only by looking at this sequence of power measures over time; from this perspective, it looks like a random signal from this perspective, since it is not clear, from earlier data, how the electric potential will behave
6In Portuguese, "Aguarde em estado de repouso" 7In the sense already discussed in Section 3.2
8They were minimally helped with phrases such as: "You can try different things like relaxing, focusing,
thinking about something, but always looking at the bars to see if it’s working." Also, they were recalled that their particular feature had some relation with their attention/concentration levels, since they were selected for being the most prominent feature of the signal during attention-demanding tasks.
Figure 3.5: A single NFB session described schematically. During tasks, participants interacted with their signals aiming to regulate them second-by-second (colored bars in Fig. 3.3) compared to
the previous resting period (horizontal line in Fig. 3.3).
subsequently. The usual approach is to state statistical quantities about these signals. One typical approach is to investigate the signal’s frequency content, by changing its domain from time to frequency, through Fourier transformation of data (see Section 2.2.2), which leads to the so-called power spectra density (PSD) of a particular segment of the EEG. Computationally speaking, DTFTs were performed on the digital EEG-signals using MATLAB tools, in order to analyze the frequency content of segments of the signal. (See typical Fourier-transformed EEG-signals in Fig. 3.7.) It was expected that the PSDs of segments of data of distinct conditions (NFB task and resting state) would be different, at least for some frequency ranges and electrode positions. In order to evaluate this dissimilarity between conditions, standard EEG analysis of PSDs was performed, separating them in different frequency bands, computing the power of those bands and analyzing their behavior.
The estimation of these frequency-band powers was implemented by separating the PSDs in typically analyzed frequency bands (𝛼, 𝛽, 𝜃, etc.; see Section 3.4) and computing the power over each of them, for all electrodes. These measures, called attributes, were normalized by the power of the whole 4-30 Hz range. These regularized attributes assume values between 0 and 1, and were interpreted as the percentage of the frequency content that was lying on each of those particular frequency ranges. This facilitates the comparison between the content of signals from different conditions, and this is the straightforward approach performed by most EEG studies and also by the present one.
Figure 3.6: Typical EEG signals. The figure shows a 16-channel measurement during ten seconds. The scale, in 𝜇V, is shown at the bottom-right. Typical artifacts can be seen, mainly in the first rows that represent more frontal electrode positions. Near the 213th second, for example, there are
peaks in several time series at the same time. Given that they are present only at those frontal electrodes, they are probably due to an eye-blinking artifact.
Figure 3.7: DTFT of the signal shown in Fig. 3.6. It is possible to see a peak around 10 Hz at the transformed signal, for all components, which is reasonable, since it is the most prominent frequency
3.5.2
Outcome analysis
Session Zero
The previous analysis of the signal during the Go/No-Go test (task) was performed and compared with a previously recorded resting-state period. The power of each attribute was computed by the second and the normalized distribution of power (NDP) was separated in resting-state (rsNDP) or task periods (tNDP). From each condition’s NDP, its average and variance was computed. The one-by-one comparison, executed by Fisher’s LDA, led to the choice of best attribute for each participant, which would be used in the training afterwards.
NFB sessions (online signal analysis)
NFB sessions began with a 2-minute resting-state period, while the software computed the attribute by the second. Then, the rsNDP was averaged and presented as a horizontal line at the main plot at the interface (threshold). The session, then, began by consecutively showing the real-time normalized power of the attribute as a bar appearing at the right side of the previous one.
3.5.3
Training-efficacy evaluation
Tracking attribute’s power
From all sessions, the average power of task periods for the attribute under NFB intervention was computed.This 10-valued measure was then normalized by the first-session’s average power. Tracking the attributes session by session generated curves related to the attribute’s task powers across sessions. Finally, we performed linear regression on the data to extract features such as inclination, which could represent correlation between number of sessions and attribute’s power during NFB-tasks. These slopes were extracted for each subject, and then averaged between groups.
Resting-state evaluation was performed similarly, by averaging attribute’s power of resting-state periods, normalizing by the first session, and tracking these values across sessions. The curve for each subject was generated, and then divided into groups and averaged for each group. The aim was to evaluate long-term effects of the training, by examining if there was an enhancement of the signal’s baseline itself.
Performance measure
The performance was maybe the most significant measure of this work, because it gives a clue for the present work’s title. Participants, while performing a NFB task, were interacting with
the previous resting-state period (via threshold; the aim of each task period was to surpass the power of the previous resting-state period, by putting as many bars above (or below) the threshold as possible). Therefore, the performance measure consisted of a comparison between the mean tNDP and the mean rsNDP, by dividing those quantities. The result is a positive number that corresponds to how much the participants were able to enhance their signal towards their predetermined goal by overcoming the threshold as a goal and gaining points as a reward (this was called performance). If the number is bigger than 1, the performance was good; the opposite means the contrary.
For each training session there was a value for the performance. Evaluation of the global performance was done by plotting the session performance curve and extracting its slope, to verify whether participants were enhancing their performance (of beating the threshold) across sessions. Then, the distribution of slopes for each group was analyzed as well as its group average.
Chapter 4
Results and Discussions
4.1
Session Zero
Examining each individual’s NDPs at both conditions (tNDP and rsNDP), involving all pos-sible attributes, i.e., all pairs (frequency band, electrode position) led to figures such as Fig. 4.1, which exhibits the differences in power for one particular subject and frequency band for all sixteen electrode sites. These graphics provided a clearer view of the variations between task and rest over the scalp. In this figure, blue regions represent where power decreased during the task, in relation to the previous resting-state period, while red ones represent the opposite. In this example, low-beta power is presented and has decreased mainly over frontal regions, while in other regions, such as around Fz and P5, this power actually increased during the task.
Each participant presented a different response while performing this task. Also, the same participant presented different responses for different days. In order to consider this natural deviation, each participant performed this attention task twice, on different days. Analysis of the differences in power were made for both subsessions (Session 0.1 and 0.2) and resulted in visualizations such as Fig. 4.2. For this subject in particular, there were great similarities be-tween both subsessions, in regard to the variations of power over the frequency spectra, which can be seen by the resemblance between headplots of each column. For example, there was a significant reduction of alpha power in both subsessions, virtually over the entire scalp, while there was a significant increase of more rapid waves, such as low beta and high beta, also over the entire scalp, but mainly in parietal sites. If there were no great similarities between both subsessions, regarding the frequency spectra, then the day at which the participant scored more points on the Go/No-Go test was considered (if the participant scored higher, then he/she was probably more focused on the task on that day, and the signal might better represent his/her attention capacity).
