Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: Brain-Computer Interface (BCI) monitors brain activity by using signals such as EEG, EcOG, and MEG, and attempts to bridge the gap between thoughts and actions by providing control to physical devices that range from wheelchairs to computers. A crucial process for a BCI system is feature extraction, and many studies have been undertaken to find relevant information from a set of input signals. This thesis investigated feature extraction from EEG signals using two different approaches. Wavelet packet decomposition was used to extract information from the signals in their frequency domain, and cepstral analysis was used to search for relevant information in the cepstral domain. A BCI was implemented to evaluate the two approaches, and three classification techniques contributed to finding the effectiveness of each feature type. Data containing two-class motor imagery was used for testing, and the BCI was compared to some of the other systems currently available. Results indicate that both approaches investigated were effective in producing separable features, and, with further work, can be used for the classification of trials based on a paradigm exploiting motor imagery as a means of control. / AFRIKAANSE OPSOMMING: ’n Brein-Rekenaar Koppelvlak (BRK) monitor brein aktiwiteit deur gebruik te maak van seine soos EEG, EcOG, en MEG. Dit poog om die gaping tussen gedagtes en fisiese aksies te oorbrug deur beheer aan toestelle soos rolstoele en rekenaars te verskaf. ’n Noodsaaklike proses vir ’n BRK is die ontginning van toepaslike inligting uit inset-seine, wat kan help om tussen verskillende gedagtes te onderskei. Vele studies is al onderneem oor hoe om sulke inligting te vind. Hierdie tesis ondersoek die ontginning van kenmerk-vektore in EEG-seine deur twee verskillende benaderings. Die eerste hiervan is golfies pakkie ontleding, ’n metode wat gebruik word om die sein in die frekwensie gebied voor te stel. Die tweede benadering gebruik kepstrale analise en soek vir toepaslike inligting in die kepstrale domein. ’n BRK is geïmplementeer om beide metodes te evalueer. Die toetsdata wat gebruik is, het bestaan uit twee-klas motoriese verbeelde bewegings, en drie klassifikasie-tegnieke was gebruik om die doeltreffendheid van die twee metodes te evalueer. Die BRK is vergelyk met ander stelsels wat tans beskikbaar is, en resultate dui daarop dat beide metodes doeltreffend was. Met verdere navorsing besit hulle dus die potensiaal om gebruik te word in stelsels wat gebruik maak van motoriese verbeelde bewegings om fisiese toestelle te beheer.

Une interface cerveau-ordinateur permet d'interagir avec un système, comme un système d'écriture, uniquement par l'activité cérébrale. Un des phénomènes neurophysiologiques permettant cette interaction est le potentiel évoqué cognitif P300, lequel correspond à une modification du signal 300 ms après la présentation d'une information attendue. Cette petite réaction cérébrale est difficile à observer par électroencéphalographie car le signal est bruité. Dans cette thèse, de nouvelles techniques basées sur la théorie des ondelettes sont développées pour améliorer la détection des P300 en utilisant des mesures de similarité entre les canaux électroencéphalographiques. Une technique présentée dans cette thèse débruite les signaux en considérant simultanément la phase des signaux. Nous avons également étendu cette approche pour étudier la localisation du P300 dans le but de sélectionner automatiquement la fenêtre temporelle à étudier et faciliter la détection / Brain-Computer Interfaces (BCI) are control and communication systems which were initially developed for people with disabilities. The idea behind BCI is to translate the brain activity into commands for a computer application or other devices, such as a spelling system. The most popular technique to record brain signals is the electroencephalography (EEG), from which Event-Related Potentials (ERPs) can be detected and used in BCI systems. Despite the BCI popularity, it is generally difficult to work with brain signals, because the recordings contains also noise and artifacts, and because the brain components amplitudes are very small compared to the whole ongoing EEG activity. This thesis presents new techniques based on wavelet theory to improve BCI systems using signals' similarity. The first one denoises the signals in the wavelet domain simultaneously. The second one combines the information provided by the signals to localize the ERP in time by removing useless information

Orientadores: Gabriela Castellano, Romis Ribeiro de Faissol Attux / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin / Made available in DSpace on 2018-09-06T19:40:58Z (GMT). No. of bitstreams: 1 StefanoFilho_CarlosAlberto_M.pdf: 6581881 bytes, checksum: fb23f8cb938a72e69a97b2bf2ff14cab (MD5) Previous issue date: 2016 / Resumo: Interfaces cérebro-computador (BCIs, brain-computer interfaces) são sistemas cuja finalidade é fornecer um canal de comunicação direto entre o cérebro e um dispositivo externo, como um computador, uma prótese ou uma cadeira de rodas. Por não utilizarem as vias fisiológicas convencionais, BCIs podem constituir importantes tecnologias assistivas para pessoas que sofreram algum tipo de lesão e, por isso, tiveram sua interação com o ambiente externo comprometida. Os sinais cerebrais a serem extraídos para utilização nestes sistemas devem ser gerados mediante estratégias específicas. Nesta dissertação, trabalhamos com a estratégia de imaginação motora (MI, motor imagery), e extraímos a resposta cerebral correspondente a partir de dados de eletroencefalografia (EEG). Os objetivos do trabalho foram caracterizar as redes cerebrais funcionais oriundas das tarefas de MI das mãos e explorar a viabilidade de utilizar métricas da teoria de grafos para a classificação dos padrões mentais, gerados por esta estratégia, de usuários de um sistema BCI. Para isto, fez-se a hipótese de que as alterações no espectro de frequências dos sinais de eletroencefalografia devidas à MI das mãos deveria, de alguma forma, se refletir nos grafos construídos para representar as interações cerebrais corticais durante estas tarefas. Em termos de classificação, diferentes conjuntos de pares de eletrodos foram testados, assim como diferentes classificadores (análise de discriminantes lineares ¿ LDA, máquina de vetores de suporte ¿ SVM ¿ linear e polinomial). Os três classificadores testados tiveram desempenho similar na maioria dos casos. A taxa média de classificação para todos os voluntários considerando a melhor combinação de eletrodos e classificador foi de 78%, sendo que alguns voluntários tiveram taxas de acerto individuais de até 92%. Ainda assim, a metodologia empregada até o momento possui várias limitações, sendo a principal como encontrar os pares ótimos de eletrodos, que variam entre voluntários e aquisições; além do problema da realização online da análise / Abstract: Brain-computer interfaces (BCIs) are systems that aim to provide a direct communication channel between the brain and an external device, such as a computer, a prosthesis or a wheelchair. Since BCIs do not use the conventional physiological pathways, they can constitute important assistive technologies for people with lesions that compromised their interaction with the external environment. Brain signals to be extracted for these systems must be generated according to specific strategies. In this dissertation, we worked with the motor imagery (MI) strategy, and we extracted the corresponding cerebral response from electroencephalography (EEG) data. Our goals were to characterize the functional brain networks originating from hands¿ MI and investigate the feasibility of using metrics from graph theory for the classification of mental patterns, generated by this strategy, of BCI users. We hypothesized that frequency alterations in the EEG spectra due to MI should reflect themselves, in some manner, in the graphs representing cortical interactions during these tasks. For data classification, different sets of electrode pairs were tested, as well as different classifiers (linear discriminant analysis ¿ LDA, and both linear and polynomial support vector machines ¿ SVMs). All three classifiers tested performed similarly in most cases. The mean classification rate over subjects, considering the best electrode set and classifier, was 78%, while some subjects achieved individual hit rates of up to 92%. Still, the employed methodology has yet some limitations, being the main one how to find the optimum electrode pairs¿ sets, which vary among subjects and among acquisitions; in addition to the problem of performing an online analysis / Mestrado / Física / Mestre em Física / 165742/2014-3 / 1423625/2014 / CNPQ / CAPES

Driver fatigue is a state of reduced mental alertness which impairs the performance of a range of cognitive and psychomotor tasks, including driving. According to the National Highway Traffic Safety Administration, driver fatigue was responsible for 72,000 accidents that lead to more than 800 deaths in 2015. A reliable method of driver fatigue detection is needed to prevent such accidents. There has been a great deal of research into studying driver fatigue via electroencephalography (EEG) to analyze brain wave data. These research works have produced three competing EEG data-based ratios that have the potential to detect driver fatigue. Research has shown these three ratios trend downward as fatigue increases. However, no empirical research has been conducted to determine whether drivers begin to feel fatigue at a certain Percent Change from an alert state to a fatigue state in one or more of these ratios. If a Percent Change could be identified for which drivers begin to feel fatigue, then it could be used as a method of fatigue detection in real-time system. This research focuses on answering this question by collecting brain wave data via an EEG device over a 60-minute driving session for 10 University of North Florida (UNF) students. A frequency distribution and cluster analysis was done to identify a common Percent Change for the participants who experienced fatigue. The results of the analysis were compared to a subset of users who did not experience fatigue to validate the findings. The project was approved by the UNF IRB on Nov. 1, 2016 (reference number 475514-4).

M. Tech. Electrical Engineering / Determines whether Hidden Markov models (HMM) can be used to classify steady state visual evoked electroencephalogram signals in a BCI system. This is for the purpose of aiding disabled people in driving a wheelchair.

Made available in DSpace on 2014-12-17T14:56:05Z (GMT). No. of bitstreams: 1 AndreFB_DISSERT.pdf: 2147554 bytes, checksum: 3ed5f0d06e3b072597f2eae69b7d1ca2 (MD5) Previous issue date: 2012-02-24 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature / As interfaces c?rebro-computador (ICC) t?m como objetivo estabelecer uma via de comunica??o com o sistema nervoso central (SNC) que seja independente das vias padr?o (nervos, m?sculos), visando o controle de algum dispositivo. O objetivo principal da presente pesquisa ? desenvolver uma ICC off-line que separe os diferentes padr?es de EEG resultantes de tarefas puramente mentais realizadas por um sujeito experimental, comparando a efic?cia de diferentes abordagens de pr?-processamento do sinal. Tamb?m foram testadas diferentes abordagens de classifica??o: todos contra todos, um contra um e uma abordagem hier?rquica de classifica??o. N?o foram encontradas t?cnicas de pr?-processamento que melhorem os resultados do sistema. Al?m disso, a abordagem hier?rquica sugerida mostrou-se capaz de produzir resultados acima do padr?o esperado pela literatura

Orientador: Prof. Dr. Diogo Coutinho Soriano / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Engenharia Biomédica, 2018. / A busca por materiais funcionais que possam desempenhar reparo e/ou regeneracao de Uma interface cérebro-computador (BCI) consiste em um sistema que busca extrair informações da atividade do sistema nervoso central e traduzi-las em comandos de saída, os quais podem eventualmente ser usados para controle de dispositivos assistivos. Mais do que contribuir para o controle de tecnologias assistivas ou reabilitação de pessoas com severas limitações, um sistema BCI pode contribuir para uma melhor compreensão do funcionamento cerebral e dos complexos mecanismos de cognição na medida em que se busca avaliar as variáveis mais relevantes para a eficiente decodificação de tarefas mentais. Entre as possíveis formas de se estudar o funcionamento cerebral destaca-se a quantificação da conectividade funcional, a qual visa estabelecer a similaridade observacional entre diferentes regiões cerebrais. Tal estratégia tem sido utilizada na caracterização e diagnóstico de patologias de grande relevância como depressão, Parkinson, Alzheimer, distúrbios de atenção, entre outras. Tendo isso em vista, este trabalho visou estudar o desempenho de decodificação de tarefas mentais a partir de métricas de grafos (grau, coeficiente de agregação, centralidade de intermediação e centralidade de autovetor) obtidas pela avaliação da conectividade funcional no contexto de sinais eletroencefalográficos na execução de paradigmas clássicos de sistemas BCI definidos pela imagética motora e os potenciais visualmente evocados em regime permanente (SSVEP). Além da análise comparativa entre tais métricas, o presente trabalho apresenta um estudo em relação ao desempenho de decodificação quando diferentes métodos de estimação da matriz de adjacência - forma de representação da conectividade funcional ¿ são utilizados, os quais abrangem as medidas de similaridade definidas pela correlação de Pearson, de Spearman e contagem de recorrência espaço-temporal (STR), sendo a última uma proposta original desta dissertação. Como resultado, para os sinais relacionados à BCIs baseadas em imaginação de movimentos, a STR obteve o melhor desempenho considerando todos os sujeitos e classes, mostrando-se uma possível abordagem para extração de características no contexto de sistemas BCI baseadas em imagética de tarefas. Para os sinais relacionados ao paradigma SSVEP, a decodificação baseada na conectividade funcional alcançou desempenhos satisfatórios, porém inferiores aos da análise em frequência classicamente utilizada neste contexto. / Brain-computer interface (BCI) consists of a system that aims to extract information from the activity of central nervous system and translate it into output commands, which can eventually be used to control assistive devices. More than contributing to the control of assistive technologies or rehabilitation of people with severe limitations, a BCI can also contribute to a better understanding of brain functioning and the complex mechanisms of cognition when evaluating the most relevant variables for the efficient decoding of mental tasks. Among the possible ways to study brain functioning, the functional connectivity quantification deserves careful attention, since it aims to establish the observational similarity between different brain regions. Such strategy has been used in the characterization and diagnosis of pathologies of great relevance such as depression, Parkinson, Alzheimer, attention disorders, among others. This work aimed to study the performance of decoding mental tasks from graph metrics (degree, clustering coefficient, betweenness centrality and eigenvector centrality) obtained by evaluation of functional connectivity in the context of electroencephalographic signals in the execution of classic BCI paradigms defined by motor imagery and steady state visually evoked potentials (SSVEP). In addition to the comparative analysis of such metrics, this work also presents a study regarding decoding performance when using different methods of adjacency matrix estimation - a functional connectivity representation - which include similarity measures defined by the correlation of Pearson, Spearman and Space-Time Recurrence counting (STR), being the latter an original proposal of this work. As main results, for signals related to motor imagery BCI, STR obtained the best performance considering all the subjects and classes, stablishing a possible approach for feature extraction in the context of motor imagery BCIs. For signals related to the SSVEP paradigm, decoding based on functional connectivity achieved satisfactory performance, but lower than the spectral analysis, classically used in this context.

Orientador: Prof. Dr. Ricardo Suyama / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, Santo André, 2018. / Uma interface cerebro-computador (BCI) e projetada para que se consiga, de modo efetivo, fornecer uma via alternativa de comunicacao entre o cerebro do usuario e o computador. Sinais captados por meio de eletrodos, tipicamente posicionados no escalpo do individuo, sao previamente processados para que haja eliminacao de ruidos externos. A partir dai, diversas tecnicas para processamento de sinais sao utilizadas para posteriormente classificar os sinais registrados e realizar a traducao do estado mental do usuario em um comando especifico a ser executado pelo computador. No presente trabalho sao utilizadas tecnicas de representacao esparsa dos sinais para a extracao de caracteristicas relevantes para classificacao dos mesmos, com intuito de aumentar a robustez e melhorar o desempenho do sistema. Para a extracao de sinais esparsos, foram utilizados algoritmos de criacao de dicionarios, a partir dos quais e possivel obter uma representacao esparsa para todo o subespaco de sinal. No trabalho foram utilizados 5 diferentes algoritmos de criacao de dicionario: Metodo de direcoes otimas (MOD), K-SVD, RLS-DLA, LS-DLA e Aprendizado de dicionario Online (ODL). A classificacao dos sinais foi realizada com o metodo de .. vizinhos mais proximos (k - NN). Os resultados obtidos com a abordagem de representacao esparsa foram comparados com os resultados do BCI Competition IV dataset 2a. Para o primeiro colocado da competicao foi obtido, em termos do coeficiente kappa, uma acuracia de 0.57 enquanto que no trabalho utilizando os metodos esparsos, obteve-se, em coeficiente kappa, uma acuracia de 0.90. Em comparacao obteve-se um ganho de 0.33 de acuracia, onde se deduz que o uso de sinais esparsos pode ser benefico para o dificil problema de se projetar uma interface cerebro computador. / A brain computer interface (BCI) is designed to effectively translate commands thought by human individuals into commands that a computer can effectively understand. Electrical impulses generated from the brain sculp are recorded from a device called an electroencephalograph and are preprocessed for elimination of external noise. From there, several techniques for signal processing are used to later classify the signals obtained by the electroencephalograph. In this work, techniques for sparse representation of signals are used for feature extraction, in order to increase robustness and system performance. For the extraction of sparse signals, five different dictionary learning algorithms were used, being able to produce a basis capable of represensing the entire signal subspace. In this work, 5 different dictionary learning algorithms were used: Method of Optimal Directions (MOD), K-SVD, Recursive Least Square Dictionary Learning (RLS-DLA), Least Square Dictionary Learning (LS-DLA) and Online Dictionary Learning (ODL). For the classification task, the k-NN method was used. The simulation results obtained with this approach were compared with the best BCI Competition IV dataset 2a results. For the first place in the competition, an accuracy of 0.57 was obtained, in terms of the kappa coefficient, whereas in the work using the sparse methods, a kappa coefficient of 0.90 was obtainned, improving accuracy in 0.33 accuracy was obtained, which indicates that the use of sparse signals may be beneficial to the difficult problem of designing a brain computer interface.

Aujourd’hui, les modèles computationnels de l'apprentissage et de la prise de décision chez l'homme se sont raffinés et complexifiés pour prendre la forme de modèles génératifs des données psychophysiologiques de plus en plus réalistes d’un point de vue neurobiologique et biophysique. Dans le même temps, le nouveau champ de recherche des interfaces cerveau-machine (ICM) s’est développé de manière exponentielle. L'objectif principal de cette thèse était d'explorer comment le paradigme de l'électrophysiologie temps-réel peut contribuer à élucider les processus d'apprentissage et de prise de décision perceptive chez l’homme. Au niveau expérimental, j'ai étudié les décisions perceptives somatosensorielles grâce à des tâches de discrimination de fréquence tactile. En particulier, j'ai montré comment un contexte sensoriel implicite peut influencer nos décisions. Grâce à la magnétoencéphalographie (MEG), j'ai pu étudier les mécanismes neuronaux qui sous-tendent cette adaptation perceptive. L’ensemble de ces résultats renforce l'hypothèse de la construction implicite d’un a priori ou d'une référence interne au cours de l'expérience. Aux niveaux théoriques et méthodologiques, j'ai proposé une vue générique de la façon dont l'électrophysiologie temps-réel pourrait être utilisée pour optimiser les tests d'hypothèses, en adaptant le dessin expérimental en ligne. J'ai pu fournir une première validation de cette démarche adaptative pour maximiser l'efficacité du dessin expérimental au niveau individuel. Ce travail révèle des perspectives en neurosciences fondamentales et cliniques ainsi que pour les ICM / Today, psychological as well as physiological models of perceptual learning and decision-making processes have recently become more biologically plausible, leading to more realistic (and more complex) generative models of psychophysiological observations. In parallel, the young but exponentially growing field of Brain-Computer Interfaces (BCI) provides new tools and methods to analyze (mostly) electrophysiological data online. The main objective of this PhD thesis was to explore how the BCI paradigm could help for a better understanding of perceptual learning and decision making processes in humans. At the empirical level, I studied decisions based on tactile stimuli, namely somatosensory frequency discrimination. More specifically, I showed how an implicit sensory context biases our decisions. Using magnetoencephalography (MEG), I was able to decipher some of the neural correlates of those perceptual adaptive mechanisms. These findings support the hypothesis that an internal perceptual-reference builds up along the course of the experiment. At the theoretical and methodological levels, I propose a generic view and method of how real-time electrophysiology could be used to optimize hypothesis testing, by adapting the experimental design online. I demonstrated the validity of this online adaptive design optimization (ADO) approach to maximize design efficiency at the individual level. I also discussed the implications of this work for basic and clinical neuroscience as well as BCI itself

Les interfaces cerveau-ordinateur (ou BCI en anglais pour Brain-Computer Interfaces) mettent en place depuis le système nerveux central un circuit artificiel secondaire qui remplace l’utilisation des nerfs périphériques, permettant entre autres à des personnes ayant une déficience motrice grave d’interagir, uniquement à l’aide de leur activité cérébrale, avec différents types d’applications, tels qu’un système d’écriture, une neuro-prothèse, un fauteuil roulant motorisé ou un bras robotique. Une technique répandue au sein des BCI pour enregistrer l’activité cérébrale est l’électroencéphalographie (EEG), étant donné que contrairement à d’autres techniques d’imagerie, elle est non invasif et peu coûteuse. En outre, l’imagination motrice (MI), c’est-à-dire les oscillations des neurones du cortex moteur générées lorsque les sujets imaginent effectuer un mouvement sans réellement l’accomplir, est appropriée car détectable dans l’EEG et liée à l’activité motrice pour concevoir des interfaces comme des neuro-prothèses non assujetties à des stimuli. Cependant, même si des progrès importants ont été réalisés au cours des dernières années, un contrôle 3D complet reste un objectif à atteindre. Afin d’explorer de nouvelles solutions pour surmonter les limitations existantes, nous présentons une approche multiclasses qui considère la détection des imaginations motrices combinées. Le paradigme proposé comprend l’utilisation de la main gauche, de la main droite, et des deux pieds ensemble. Ainsi, par combinaison, huit commandes peuvent être fournies pour diriger un bras robotisé comprenant quatorze mouvements différents qui offrent un contrôle 3D complet. À cette fin, un système de commutation entre trois modes (déplacement du bras, du poignet ou des doigts) a été conçu et permet de gérer les différentes actions en utilisant une même commande. Ce système a été mis en oeuvre sur la plate-forme OpenViBE. En outre, pour l’extraction de caractéristiques une nouvelle approche de traitement d’information fournie par les capteurs a été développée sur la base de l’emplacement spécifique des sources d’activité liées aux parties du corps considérées. Cette approche permet de regrouper au sein d’une seule classe les différentes actions pour lesquelles le même membre est engagé, d’une manière que la tâche multiclasses originale se transforme en un problème équivalent impliquant une série de modèles de classification binaires. Cette approche permet d’utiliser l’algorithme de Common Spatial pattern (CSP) dont la capacité à discriminer des rythmes sensorimoteurs a été largement montrée mais qui présente l’inconvénient d’être applicable uniquement pour différencier deux classes. Nous avons donc également contribué à une nouvelle stratégie qui combine un ensemble de CSP et la géométrie riemannienne. Ainsi des caractéristiques plus discriminantes peuvent être obtenues comme les distances séparant les données des centres des classes considérées. Ces stratégies ont été appliquées sur trois nouvelles approches de classification qui ont été comparées à des méthodes de discrimination multiclasses classiques en utilisant les signaux EEG d’un groupe de sujets sains naïfs, montrant ainsi que les alternatives proposées permettent non seulement d’améliorer l’existant, mais aussi de réduire la complexité de la classification / Brain-Computer Interfaces (BCIs) replace the natural nervous system outputs by artificial ones that do not require the use of peripheral nerves, allowing people with severe motor impairments to interact, only by using their brain activity, with different types of applications, such as spellers, neuroprostheses, wheelchairs, or among others robotics devices. A very popular technique to record signals for BCI implementation purposes consists of electroencephalography (EEG), since in contrast with other alternatives, it is noninvasive and inexpensive. In addition, due to the potentiality of Motor Imagery (MI, i.e., brain oscillations that are generated when subjects imagine themselves performing a movement without actually accomplishing it) to generate suitable patterns for scheming self-paced paradigms, such combination has become a common solution for BCI neuroprostheses design. However, even though important progress has been made in the last years, full 3D control is an unaccomplished objective. In order to explore new solutions for overcoming the existing limitations, we present a multiclass approach that considers the detection of combined motor imageries, (i.e., two or more body parts used at the same time). The proposed paradigm includes the use of the left hand, right hand, and both feet together, from which eight commands are provided to direct a robotic arm comprising fourteen different movements that afford a full 3D control. To this end, an innovative switching-mode scheme that allows managing different actions by using the same command was designed and implemented on the OpenViBE platform. Furthermore, for feature extraction a novel signal processing scheme has been developed based on the specific location of the activity sources that are related to the considered body parts. This insight allows grouping together within a single class those conditions for which the same limb is engaged, in a manner that the original multiclass task is transformed into an equivalent problem involving a series of binary classification models. Such approach allows using the Common Spatial Pattern (CSP) algorithm; which has been shown to be powerful at discriminating sensorimotor rhythms, but has the drawback of being suitable only to differentiate between two classes. Based on this perspective we also have contributed with a new strategy that combines together the CSP algorithm and Riemannian geometry. In which the CSP projected trials are mapped into the Riemannian manifold, from where more discriminative features can be obtained as the distances separating the input data from the considered class means. These strategies were applied on three new classification approaches that have been compared to classical multiclass methods by using the EEG signals from a group of naive healthy subjects, showing that the proposed alternatives not only outperform the existing schema, but also reduce the complexity of the classification task