Cette thèse porte sur la conception et l'évaluation de systèmes interactifs utilisant des interfaces cerveau-machine (BCI pour Brain-Computer Interfaces). Ce type d'interfaces s'est développé dans les années récentes tout d'abord dans le domaine du handicap, afin de fournir aux grands handicapés des moyens d'interaction et de communication, et plus récemment dans d'autres domaines comme celui des jeux vidéo. Néanmoins, la plupart des travaux ont porté sur l'identification des signaux du cerveau susceptibles de porter une information utile, et sur les traitements nécessaires à l'extraction de cette information. Peu de travaux ont porté sur les aspects d'utilisabilité et de prise en compte des facteurs humains dans l'ensemble du système interactif. Cette thèse se concentre sur les systèmes basées sur SSVEP (steady-state visually evoked potentials), et se propose d'étudier l'ensemble du système interactif cerveau-machine, selon les critères de l'interaction homme-machine (IHM). Plus précisément, les points étudiés portent sur la demande cognitive, la frustration de l'utilisateur, les conditions de calibration, et les BCI hybrides. / This PhD deals with the conception and evaluation of interactive systems based on Brain-Computer Interfaces (BCI). This type of interfaces has developed in recent years, first in the domain of handicaps, in order to provide disabled people means of interaction and communication, and more recently in other fields as video games. However, most of the research so far focused on the identification of cerebral pattern carrying useful information, a on signal processing for the detection of these patterns. Less attention has been given to usability aspects. This PhD focuses on interactive systems based on Steady-State Visually Evoked Potentials (SSVEP), and aims at considering the interactive system as a whole, using the concepts of Human-Computer Interaction. More precisely, a focus is made on cognitive demand, user frustration, calibration conditions, and hybrid BCIs.

Les interfaces cerveau machine (BCI pour Brain Computer Interfaces) non invasives permettent à leur utilisateur de contrôler une machine par la pensée. Ce dernier doit porter un dispositif d'acquisition de signaux électroencéphalographiques (EEG), qui sont dotés d'un rapport signal sur bruit assez faible ; à ceci s'ajoute l’importante variabilité tant à travers les sessions d'utilisation qu’à travers les utilisateurs. Par conséquent, la calibration du BCI est souvent nécessaire avant son utilisation. Cette thèse étudie les sources de cette variabilité, dans le but d'explorer, concevoir, et implémenter des méthodes d'autocalibration. Nous étudions la variabilité des potentiels évoqués, particulièrement une composante tardive appelée P300. Nous nous penchons sur trois méthodes d’apprentissage par transfert : la Géométrie Riemannienne, le Transport Optimal, et l’apprentissage ensembliste. Nous proposons un modèle de l'EEG qui tient compte de la variabilité. Les paramètres résultants de nos analyses nous servent à calibrer ce modèle et à simuler une base de données, qui nous sert à évaluer la performance des méthodes d’apprentissage par transfert. Puis ces méthodes sont combinées et appliquées à des données expérimentales. Nous proposons une méthode de classification basée sur le Transport Optimal dont nous évaluons la performance. Ensuite, nous introduisons un marqueur de séparabilité qui nous permet de combiner Géométrie Riemannienne, Transport Optimal et apprentissage ensembliste. La combinaison de plusieurs méthodes d’apprentissage par transfert nous permet d’obtenir un classifieur qui s’affranchit des différentes sources de variabilité des signaux EEG. / Non-invasive Brain Computer Interfaces (BCIs) allow a user to control a machine using only their brain activity. The BCI system acquires electroencephalographic (EEG) signals, characterized by a low signal-to-noise ratio and an important variability both across sessions and across users. Typically, the BCI system is calibrated before each use, in a process during which the user has to perform a predefined task. This thesis studies of the sources of this variability, with the aim of exploring, designing, and implementing zero-calibration methods. We review the variability of the event related potentials (ERP), focusing mostly on a late component known as the P300. This allows us to quantify the sources of EEG signal variability. Our solution to tackle this variability is to focus on adaptive machine learning methods. We focus on three transfer learning methods: Riemannian Geometry, Optimal Transport, and Ensemble Learning. We propose a model of the EEG takes variability into account. The parameters resulting from our analyses allow us to calibrate this model in a set of simulations, which we use to evaluate the performance of the aforementioned transfer learning methods. These methods are combined and applied to experimental data. We first propose a classification method based on Optimal Transport. Then, we introduce a separability marker which we use to combine Riemannian Geometry, Optimal Transport and Ensemble Learning. Our results demonstrate that the combination of several transfer learning methods produces a classifier that efficiently handles multiple sources of EEG signal variability.

Bakgrund: Stroke drabbar miljontals människor världen över varje år och medför ofta ensidiga motoriska nedsättningar som allvarligt reducerar förmågan till självständighet i vardagen. Fysioterapin efter stroke sker därför vanligen genom uppgiftsorienterad träning riktad mot att rehabilitera den motoriska förmågan på den affekterade sidan så att patienten kan återgå till ett självständigt liv. Men processen ställer stora krav på patienten som inte alltid kan förväntas uppnå bästa resultat med sin rehabilitering. Därför forskas det alltmer på innovativa teknologiska hjälpmedel med potential att assistera strokepatient såväl som fysioterapeut i rehabiliteringen. Exoskelett och hjärndatorgränssnitt (BCI) är två sådana hjälpmedel som undersöktes i denna studie. Syfte: Studien hade syftet att sammanställa det vetenskapliga stödet för tillämpning av BCI-styrda exoskelett (BCI-Exo) vid rehabilitering av motorisk arm- och handfunktion efter stroke i dess subakuta samt kroniska fas. Metod: Litteratursökningar utfördes i databaserna PEDRO, PUBMED, AMED och CINAHL vilket gav 22 träffar som efter granskning och sållning resulterade i att fyra artiklar inkluderades i studien. Resultat: Samtliga studier redovisade statistiskt signifikanta förbättringar av motorisk handfunktion i interventionsgruppen jämfört med kontrollgruppen utifrån de utfallsmått som tillämpades. Konklusion: Resultatet indikerade att BCI-Exo kan främja återhämtning och neuroplasticitet för strokepatienter oavsett vilken fas de infinner sig i. Dock är teknologin fortfarande relativt ny varvid fler studier behöver utföras för att bättre specificera och förstå för- och nackdelar jämfört med konventionella behandlingsmetoder. / Background: Stroke affects millions of people around the world each year and often results in unilateral motor impairments that severely reduce the ability for independence in everyday life. Physiotherapy after stroke is therefore usually performed through task-oriented training aimed at rehabilitating the motor functional ability of the affected side so that the patient can return to an independent life. But the process places great demands on the patient who cannot always be expected to achieve the best results from their rehabilitation. Therefore, innovative technologies are increasingly being researched with the potential to assist stroke patients as well as physical therapists in the rehabilitation process. Exoskeletons and brain-computer interfaces (BCI) are two such rehabilitative tools that were investigated in this study. Objective: The study aimed to compile the scientific support for the use of BCI-controlled exoskeletons (BCI-Exo) in motor functional arm and hand rehabilitation after stroke in its subacute and chronic phase. Method: Literature searches were conducted in the databases PEDRO, PUBMED, AMED and CINAHL, which resulted in 22 hits which, after review and screening, resulted in four articles being included in the study. Results: All studies reported statistically significant improvements regarding motor function in the hemiplegic hand in the intervention group compared to the control group based on the outcome measures used. Conclusion: The results indicated that BCI-Exo can promote recovery and neuroplasticity after stroke regardless of its phase. However, the technology is still in its early stages and more studies need to be performed to better specify and understand the advantages and disadvantages compared to conventional treatment methods.

L’objectif de cette thèse est d’étudier l’utilisation d’Interfaces Cerveau-Ordinateur (ICOs) au sein de la Réalité Virtuelle (RV) et de la Réalité Augmentée (RA). Notre but est d’évaluer la compatibilité entre les systèmes basés sur une ICO et la RV/RA, de concevoir de nouveaux outils pour la visualisation de l’activité cérébrale basée sur la RV/RA, et finalement de proposer de nouveaux usages pour les ICOs, plus particulièrement en combinaison avec des vêtements intelligents. Afin de réaliser ces objectifs, nous avons tout d’abord réalisé une étude de faisabilité concernant l’association entre une ICO et la RV. Notre objectif était d’étudier l’influence de l’activité motrice sur une ICO. Nous avons conçu un système similaire à un jeu vidéo, servant comme support à une étude utilisateur montrant que l’ICO peut être utilisée avec succès, même lorsque les participants exécutent une activité musculaire exigeante. Dans un second temps, nous avons également proposé des outils de visualisation de l’activité cérébrale basés sur la RV/RA. Notre premier système nommé «Mind-Mirror» superpose un cerveau virtuel représentant l’activité cérébrale d’un utilisateur à l’image de celui-ci dans un miroir. Une étude utilisateur a montré qu’aucune perte significative de performance de l’ICO n’a été constatée, même avec une complexité additionnelle due à l’affichage basé sur la RA. Notre seconde contribution se nomme «Mind-Window» et étend les possibilités du Mind-Mirror en permettant plusieurs points de vue sur un même enregistrement d’activité cérébrale en utilisant des tablettes. Notre dernière contribution se nomme «Mind-Inside» et permet aux utilisateurs de visualiser en RV leur activité cérébrale en temps réel tout en étant immergés dans un cerveau virtuel. Enfin, nous avons étudié comment les ICOs et la RV/RA peuvent être appliquées au domaine des vêtements intelligents. Nous avons mis en place une plateforme d’expérimentation consistant en une cabine d’essayage virtuelle intégrant une ICO et permettant aux utilisateurs de porter des vêtements intelligents virtuels en RA. Poursuivant ces travaux, nous avons également conçu une «cape d’invisibilité» inspirée par l’univers de Harry Potter. Cette cape virtuelle permet aux utilisateurs de se camoufler en RA en utilisant leur état mental. Une étude utilisateur sur le contrôle de l’effet a mis en avant l’amélioration de l’expérience utilisateur et «l’impression d’avoir un superpouvoir». / The objective of this PhD thesis is to study the use of Brain- Computer Interfaces (BCIs) within Virtual Reality (VR) and Augmented Reality (AR). Our goal is to evaluate the compatibility between systems based on a BCI and VR/AR, to design new tools for the visualization of the brain activity based on VR/AR, and finally to propose new uses for the BCIs, and especially in combination with smart clothes. In order to fulfil these objectives, we have first designed and performed a feasibility study concerning the combination of a BCI and VR. Our objective was to study the influence of motor activity on a BCI. We have designed a system similar to a video game, serving as a base for a user study showing that this BCI can be successfully used, even when participants are performing a demanding muscular activity. We have also proposed three visualization tools for the brain activity based on VR/AR. Our first system called the «Mind- Mirror» which enables the visualization of our own brain activity «inside our own head» by superimposition. A user study has shown that no significant drop in BCI performance occurred, even with the additional complexity due to our AR-based display. Our second contribution is called «Mind- Window» and extends the Mind-Mirror’s possibilities by enabling one or multiple users to visualize the brain activity of another person as if her skull was transparent. Our last contribution is called «Mind-Inside» and allows users to visualize their brain activity in real-time while being immersed in a virtual brain. Finally, we have studied how BCIs and the VR/AR can be applied to smart clothes. We have designed an experimental platform comprising a dressing room integrating a BCI. Following this work, we proposed an «invisibility cloak» inspired by the Harry Potter universe. This virtual cloak allows users to camouflage themselves in AR using their mental state. Results from a preliminary study based on a simple videogame inspired by the Harry Potter universe could notably show that, compared to a standard control made with a keyboard, controlling the optical camouflage directly with the BCI could enhance the user experience and the feeling of «having a super-power».

Event-related brain potentials (ERPs) in electroencephalography are manifestations at the scalp of neural activity that is triggered by, and is involved in, the processing of specific events. This chapter focuses on braincomputer interfaces (BCIs) that use P300, an endogenous ERP component. The P300 is a positive potential that occurs over central-parietal scalp 250- 700 msec after a rare event occurs in the context of the oddball paradigm. This paradigm has three essential attributes: a subject is presented with a series of events (i.e., stimuli), each of which falls into one of two classes; the events that fall into one of the classes are less frequent than those that fall into the other class; and the subject performs a task that requires classifying each event into one of the two classes.

Objective: To assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months. Methods: Of 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine (21%) could not use the BCI. Of the other 28, 27 (men, age 28-79 years) (64%) had the BCI placed in their homes, and they and their caregivers were trained to use it. Use data were collected by Internet. Periodic visits evaluated BCI benefit and burden and quality of life. Results: Over subsequent months, 12 (29% of the original 42) left the study because of death or rapid disease progression and 6 (14%) left because of decreased interest. Fourteen (33%) completed training and used the BCI independently, mainly for communication. Technical problems were rare. Patient and caregiver ratings indicated that BCI benefit exceeded burden. Quality of life remained stable. Of those not lost to the disease, half completed the study; all but 1 patient kept the BCI for further use. Conclusion: The Wadsworth BCI home system can function reliably and usefully when operated by patients in their homes. BCIs that support communication are at present most suitable for people who are severely disabled but are otherwise in stable health. Improvements in BCI convenience and performance, including some now underway, should increase the number of people who find them useful and the extent to which they are used.

Thesis (MEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: Muscular dystrophy, spinal cord injury, or amyotrophic lateral sclerosis (ALS) are injuries and disorders that disrupts the neuromuscular channels of the human body thus prohibiting the brain from controlling the body. Brain computer interface (BCI) allows individuals to bypass the neuromuscular channels and interact with the environment using the brain. The system relies on the user manipulating his neural activity in order to control an external device. Electroencephalography (EEG) is a cheap, non-invasive, real time acquisition device used in BCI applications to record neural activity. However, noise, known as artifacts, can contaminate the recording, thus distorting the true neural activity. Eye blinks are a common source of artifacts present in EEG recordings. Due to its large amplitude it greatly distorts the EEG data making it difficult to interpret data for BCI applications. This study proposes a new combination of techniques to detect and correct eye blink artifacts to improve the quality of EEG for BCI applications. Independent component analysis (ICA) is used to separate the EEG signals into independent source components. The source component containing eye blink artifacts are corrected by detecting each eye blink within the source component and using a trained wavelet neural network (WNN) to correct only a segment of the source component containing the eye blink artifact. Afterwards, the EEG is reconstructed without distorting or removing the source component. The results show a 91.1% detection rate and a 97.9% correction rate for all detected eye blinks. Furthermore for channels located over the frontal lobe, eye blink artifacts are corrected preserving the neural activity. The novel combination overall reduces EEG information lost, when compared to existing literature, and is a step towards improving EEG pre-processing in order to provide cleaner EEG data for BCI applications. / AFRIKAANSE OPSOMMING: Spierdistrofie, ’n rugmurgbesering, of amiotrofiese laterale sklerose (ALS) is beserings en steurnisse wat die neuromuskulêre kanale van die menslike liggaam ontwrig en dus verhoed dat die brein die liggaam beheer. ’n Breinrekenaarkoppelvlak laat toe dat die neuromuskulêre kanale omlei word en op die omgewing reageer deur die brein. Die BCI-stelsel vertrou op die gebruiker wat sy eie senuwee-aktiwiteit manipuleer om sodoende ’n eksterne toestel te beheer. Elektro-enkefalografie (EEG) is ’n goedkoop, nie-indringende, intydse dataverkrygingstoestel wat gebruik word in BCI toepassings. Nie net senuwee aktiwiteit nie, maar ook geraas , bekend as artefakte word opgeneem, wat dus die ware senuwee aktiwiteit versteur. Oogknip artefakte is een van die algemene artefakte wat teenwoordig is in EEG opnames. Die groot omvang van hierdie artefakte verwring die EEG data wat dit moeilik maak om die data te ontleed vir BCI toepassings. Die studie stel ’n nuwe kombinasie tegnieke voor wat oogknip artefakte waarneem en regstel om sodoende die kwaliteit van ’n EEG vir BCI toepassings te verbeter. Onafhanklike onderdeel analise (Independent component analysis (ICA)) word gebruik om die EEG seine te skei na onafhanklike bron-komponente. Die bronkomponent wat oogknip artefakte bevat word reggestel binne die komponent en gebruik ’n ervare/geoefende golfsenuwee-netwerk om slegs ’n deel van die komponent wat die oogknip artefak bevat reg te stel. Daarna word die EEG hervorm sonder verwringing of om die bron-komponent te verwyder. Die resultate toon ’n 91.1% opsporingskoers en ’n 97.9% regstellingskoers vir alle waarneembare oogknippe. Oogknip artefakte in kanale op die voorste lob word reggestel en behou die senuwee aktiwiteit wat die oorhoofse EEG kwaliteit vir BCI toepassings verhoog.

Several technologies are being made available for the blind and the visually impaired with the use of infrared and sonar sensors, Radio Frequency Identification, GPS, Wi-Fi among others. Current technologies utilizing microprocessors increase the device's power consumption. In this project, a Verilog Hardware Language (VHDL) designed handheld device that autonomously guides a visually impaired user through an obstacle free path is proposed. The goal is to minimize power consumption by not using the usual microcontroller and replacing it with components that can increase its speed. Utilizing six infrared sensors, the handheld device is modeled after current technologies which use IR and sonar sensors which are reviewed in this project. By using behavioral modeling, an algorithm for obstacle avoidance and the generation of the obstacle free path is reduced using a K-map and implemented using a multiplexer. / by Ernesto Cividanes. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

As Interfaces Cérebro-Computador (do inglês Brain-Computer Interfaces BCI) são sistemas que visam permitir a interação entre usuários e máquinas por meio do monitoramento das atividades cerebrais. Sistemas de BCI são considerados como uma alternativa para que pessoas com perda severa ou total do controle motor, tais como as que sofrem de Esclerose Lateral Amiotrófica, possam contar com algum controle sobre o ambiente externo. Para mapear intenções individuais em operações de máquina, os sistemas de BCI empregam um conjunto de etapas que envolvem a captura e pré-processamento dos sinais cerebrais, a extração e seleção de suas características mais relevantes e a classificação das intenções. O projeto e a implementação de sistemas de BCI viáveis ainda são questões em aberto devido aos grandes desafios encontrados em cada uma de suas etapas. Esta lacuna motivou este trabalho de mestrado o qual apresenta uma avaliação dos principais extratores de características utilizados para classificar ensaios de imagética motora, cujos dados foram obtidos por meio de eletroencefalografia (EEG) e apresentam influências de artefatos, mais precisamente daqueles produzidos por interferências provenientes de atividades oculares (monitoradas por eletrooculografia EOG). Foram considerados sinais coletados pela BCI Competition IV-2b, os quais contêm informações sobre três canais de EEG e três outros de EOG. Como primeira etapa, foi realizado o pré-processamento desses canais utilizando a técnica de Análise de Componentes Independentes (ICA) em conjunto com um limiar de correlação para a remoção de componentes associados a artefatos oculares. Posteriormente, foram avaliadas diferentes abordagens para a extração de características, a mencionar: i) Árvore Diádica de Bandas de Frequências (ADBF); ii) Padrões Espaciais Comuns (CSP); iii) Padrões Espectro-Espaciais Comuns (CSSP); iv) Padrões Esparsos Espectro-Espaciais Comuns (CSSSP); v) CSP com banco de filtros (FBCSP); vi) CSSP com banco de filtros (FBCSSP); e, finalmente, vii) CSSSP com banco de filtros (FBCSSSP). Contudo, como essas técnicas podem produzir espaços de exemplos com alta dimensionalidade, considerou-se, também, a técnica de Seleção de Características baseada em Informação Mútua (MIFS) para escolher os atributos mais relevantes para o conjunto de dados adotado na etapa de classificação. Finalmente, as Máquinas de Vetores de Suporte (SVM) foram utilizadas para a classificação das intenções de usuários. Experimentos permitem concluir que os resultados do CSSSP e FBCSSSP são equiparáveis àqueles produzidos pelo estado da arte, considerando o teste de significância estatística de Wilcoxon bilateral com confiança de 0, 95. Apesar disso o CSSSP tem sido negligenciado pela área devido ao fato de sua parametrização ser considerada complexa, algo que foi automatizado neste trabalho. Essa automatização reduziu custos computacionais envolvidos na adaptação das abordagens para indivíduos específicos. Ademais, conclui-se que os extratores de características FBCSP, CSSP, CSSSP, FBCSSP e FBCSSSP não necessitam da etapa de remoção de artefatos oculares, pois efetuam filtragens por meio de modelos autoregressivos. / Brain-Computer Interfaces (BCI) employ brain imaging to enable human-machine interaction without physical control. BCIs are an alternative so that people suffering from severe or complete loss of motor control, like those with Amyotrophic Lateral Sclerosis (ALS), may have some interaction with the external environment. To transform individual intentions onto machine operations, BCIs rely on a series of steps that include brain signal acquisition and preprocessing, feature extraction, selection and classification. A viable BCI implementation is still an open question due to the great challenges involved in each one of these steps. This gap motivated this work, which presents an evaluation of themain feature extractors used to classify Motor Imagery trials, whose data were obtained through Electroencephalography (EEG) influenced by ocular activity, monitored by Electrooculography (EOG). In this sense, signals acquired by BCI Competition IV-2b, were considered. As first step the preprocessing was performed through Independent Component Analysis (ICA) together with a correlation threshold to identify components associated with ocular artifacts. Afterwards, different feature extraction approaches were evaluated: i) Frequency Subband Dyadic Three; ii) Common Spatial Patterns (CSP); iii) Common Spectral-Spatial Patterns (CSSP); iv) Common Sparse Spectral-Spatial Patterns (CSSSP); v) Filter Bank Common Spatial Patterns (FBCSP); vi) Filter Bank Common Sectral-Spatial Patterns (FBCSSP); and, finally, vii) Filter Bank Sparse Spectral- Spatial Patterns (FBCSSSP). These techniques tend to produce high-dimensional spaces, so a Mutual Information-based Feature Selection was considered to select signal attributes. Finally, Support Vector Machines were trained to tackle the Motor Imagery classification. Experimental results allow to conclude that CSSSP and FBCSSSP are statistically equivalent the state of the art, when two-sided Wilcoxon test with 0, 95 confidence is considered. Nevertheless, CSSSP has been neglected by this area due to its complex parametrization, which is addressed in this work using an automatic approach. This automation reduced computational costs involved in adapting the BCI system to specific individuals. In addition, the FBCSP, CSSP, CSSSP, FBCSSP and FBCSSSP confirm to be robust to artifacts as they implicitly filter the signals through autoregressive models.

Esta dissertação de mestrado tem por objetivo analisar os conjuntos-K, uma hierarquia de redes neurais biologicamente mais plausíveis, e aplicá-los ao problema de classificação de imagética motora através do eletroencefalograma (EEG). A imagética motora consiste no ato de processar um movimento motor da memória humana de longo tempo para a memória de curto prazo. A imagética motora deixa um rastro no sinal do EEG que torna possível a identificação e classificação dos diferentes movimentos motores. A tarefa de classificação de imagética motora através do EEG é reconhecida como complexa devido à não linearidade e quantidade de ruído da série temporal do EEG e da pequena quantidade de dados disponíveis para aprendizagem. Os conjuntos-K são um modelo conexionista que simula o comportamento dinâmico e caótico de populações de neurônios do cérebro e foram modelados com base em observações do sistema olfatório feitas por Walter Freeman. Os conjuntos-K já foram aplicados em diversos domínios de classificação diferentes, incluindo EEG, tendo demonstrado bons resultados. Devido às características da classificação de imagética motora, levantou-se a hipótese de que a aplicação dos conjuntos-K na tarefa pudesse prover bons resultados. Um simulador para os conjuntos-K foi construído para a realização dos experimentos. Não foi possível validar a hipótese levantada no trabalho, dado que os resultados dos experimentos realizados com conjuntos-K e imagética motora não apresentaram melhorias significativas para a tarefa nas comparações realizadas. / This dissertation aims to examine the K-sets, a hierarchy of biologically plausible neural networks, and apply them to the problem of motor imagery classification through electroencephalogram (EEG). Motor imagery is the act of processing a motor movement from long-term to short-term memory. Motor imagery leaves a trail in the EEG signal, which makes possible the identification and classification of different motor movements. Motor imagery classification is a complex problem due to non-linearity of the EEG time series, low signal-to-noise ratio, and the small amount of data typically available for learning. K-sets are a connectionist model that simulates the dynamic and chaotic behavior of populations of neurons in the brain, modeled based on observations of the olfactory system by Walter Freeman. K-sets have already been used in several different classification domains, including EEG, showing good results. Due to the characteristics of motor imagery classification, a hypothesis that the application of K-sets in the task could provide good results was raised. A simulator for K-sets was created for the experiments. Unfortunately, the hypothesis could not be validated, as the results of the conducted experiments with K-sets and motor imagery showed no significant improvements in comparison in the task performed.