Motor imagery-based brain-computer interface by implementing a frequency band selection

Les interfícies cervell-ordinador basades en imaginacions motores (MI-BCI) són una promesa per a revolucionar la manera com els humans interactuen amb les màquines o el programari, realitzant accions només amb el pensament. Els pacients que pateixen discapacitats de moviment crítiques, com l'esclerosi lateral amiotròfica (ALS) o la tetraplegia, podrien utilitzar aquesta tecnologia per controlar una cadira de rodes, pròtesis robòtiques o qualsevol altre dispositiu que els permeti interactuar de manera independent amb el seu entorn. L'objectiu d'aquest projecte és ajudar les comunitats afectades per aquests trastorns amb el desenvolupament d'un mètode que sigui capaç de detectar, amb la màxima precisió possible, la intenció d'executar moviments (sense que es produeixin) en les extremitats superiors del cos. Això es farà mitjançant senyals adquirits amb un electroencefalograma (EEG), el seu condicionament i processament, i la seva posterior classificació amb models d'intel·ligència artificial. A més, es dissenyarà un filtre de senyal digital per mantenir les bandes de freqüència més característiques de cada individu i augmentar significativament l’exactitud del sistema. Després d'extreure les característiques estadístiques, freqüencials i espacials més discriminatòries, va ser possible obtenir una exactitud del 88% en les dades de validació a l'hora de detectar si un participant estava imaginant un moviment de la mà esquerra o de la dreta. A més, es va utilitzar una xarxa neuronal convolucional (CNN) per distingir si el participant estava imaginant un moviment o no, la qual cosa va aconseguir una exactitud del 78% i una precisió del 90%. Aquests resultats es verificaran mitjançant la implementació d'una simulació en temps real amb l'ús d'un braç robòtic.Las interfaces cerebro-computadora basadas en imaginaciones motoras (MI-BCI) son una promesa para revolucionar la forma en que los humanos interactúan con las máquinas o el software, realizando acciones con tan solo pensar en ellas. Los pacientes que sufren discapacidades críticas del movimiento, como la esclerosis lateral amiotrófica (ALS) o la tetraplejia, podrían usar esta tecnología para controlar una silla de ruedas, prótesis robóticas o cualquier otro dispositivo que les permita interactuar de manera independiente con su entorno. El objetivo de este proyecto es ayudar a las comunidades afectadas por estos trastornos con el desarrollo de un método que sea capaz de detectar, con la mayor precisión posible, la intención de ejecutar movimientos (sin que se produzcan) en las extremidades superiores del cuerpo. Esto se hará mediante señales adquiridas con un electroencefalograma (EEG), su acondicionamiento y procesamiento, y su posterior clasificación con modelos de inteligencia artificial. Además, se diseñará un filtro de señal digital para mantener las bandas de frecuencia más características de cada individuo y aumentar significativamente la exactitud del sistema. Después de extraer características estadísticas, frecuenciales y espaciales discriminatorias, fue posible obtener una exactitud del 88% en los datos de validación a la hora de detectar si un participante estaba imaginando un movimiento con la mano izquierda o con la derecha. Además, se utilizó una red neural convolucional (CNN) para distinguir si el participante estaba imaginando un movimiento o no, lo que logró un 78% de exactitud y un 90% de precisión. Estos resultados se verificarán implementando una simulación en tiempo real con el uso de un brazo robótico.Motor Imagery-based Brain-Computer Interfaces (MI-BCI) are a promise to revolutionize the way humans interact with machinery or software, performing actions by just thinking about them. Patients suffering from critical movement disabilities, such as amyotrophic lateral sclerosis (ALS) or tetraplegia, could use this technology to control a wheelchair, robotic prostheses, or any other device that could let them interact independently with their surroundings. The focus of this project is to aid communities affected by these disorders with the development of a method that is capable of detecting, as accurately as possible, the intention to execute movements (without them occurring) in the upper extremities of the body. This will be done through signals acquired with an electroencephalogram (EEG), their conditioning and processing, and their subsequent classification with artificial intelligence models. In addition, a digital signal filter will be designed to keep the most characteristic frequency bands of each individual and increase accuracy significantly. After extracting discriminative statistical, frequential, and spatial features, it was possible to obtain an 88% accuracy on validation data when it came to detecting whether a participant was imagining a left-hand or a right-hand movement. Furthermore, a Convolutional Neural Network (CNN) was used to distinguish if the participant was imagining a movement or not, which achieved a 78% accuracy and a 90% precision. These results will be verified by implementing a real-time simulation with the usage of a robotic arm

Brain wave classification using long short - term memory based OPTICAL predictor

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL

EEG-Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

The paper aims at detecting on-line cognitive failures in driving by decoding the EEG signals acquired during visual alertness, motor-planning and motor-execution phases of the driver. Visual alertness of the driver is detected by classifying the pre-processed EEG signals obtained from his pre-frontal and frontal lobes into two classes: alert and non-alert. Motor-planning performed by the driver using the pre-processed parietal signals is classified into four classes: braking, acceleration, steering control and no operation. Cognitive failures in motor-planning are determined by comparing the classified motor-planning class of the driver with the ground truth class obtained from the co-pilot through a hand-held rotary switch. Lastly, failure in motor execution is detected, when the time-delay between the onset of motor imagination and the EMG response exceeds a predefined duration. The most important aspect of the present research lies in cognitive failure classification during the planning phase. The complexity in subjective plan classification arises due to possible overlap of signal features involved in braking, acceleration and steering control. A specialized interval/general type-2 fuzzy set induced neural classifier is employed to eliminate the uncertainty in classification of motor-planning. Experiments undertaken reveal that the proposed neuro-fuzzy classifier outperforms traditional techniques in presence of external disturbances to the driver. Decoding of visual alertness and motor-execution are performed with kernelized support vector machine classifiers. An analysis reveals that at a driving speed of 64 km/hr, the lead-time is over 600 milliseconds, which offer a safe distance of 10.66 meters

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network

High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application

Using brain-computer interaction and multimodal virtual-reality for augmenting stroke neurorehabilitation

Every year millions of people suffer from stroke resulting to initial paralysis, slow motor recovery and chronic conditions that require continuous reha bilitation and therapy. The increasing socio-economical and psychological impact of stroke makes it necessary to find new approaches to minimize its sequels, as well as novel tools for effective, low cost and personalized reha bilitation. The integration of current ICT approaches and Virtual Reality (VR) training (based on exercise therapies) has shown significant improve ments. Moreover, recent studies have shown that through mental practice and neurofeedback the task performance is improved. To date, detailed in formation on which neurofeedback strategies lead to successful functional recovery is not available while very little is known about how to optimally utilize neurofeedback paradigms in stroke rehabilitation. Based on the cur rent limitations, the target of this project is to investigate and develop a novel upper-limb rehabilitation system with the use of novel ICT technolo gies including Brain-Computer Interfaces (BCI’s), and VR systems. Here, through a set of studies, we illustrate the design of the RehabNet frame work and its focus on integrative motor and cognitive therapy based on VR scenarios. Moreover, we broadened the inclusion criteria for low mobility pa tients, through the development of neurofeedback tools with the utilization of Brain-Computer Interfaces while investigating the effects of a brain-to-VR interaction.Todos os anos, milho˜es de pessoas sofrem de AVC, resultando em paral isia inicial, recupera¸ca˜o motora lenta e condic¸˜oes cr´onicas que requerem re abilita¸ca˜o e terapia cont´ınuas. O impacto socioecon´omico e psicol´ogico do AVC torna premente encontrar novas abordagens para minimizar as seque las decorrentes, bem como desenvolver ferramentas de reabilita¸ca˜o, efetivas, de baixo custo e personalizadas. A integra¸c˜ao das atuais abordagens das Tecnologias da Informa¸ca˜o e da Comunica¸ca˜o (TIC) e treino com Realidade Virtual (RV), com base em terapias por exerc´ıcios, tem mostrado melhorias significativas. Estudos recentes mostram, ainda, que a performance nas tare fas ´e melhorada atrav´es da pra´tica mental e do neurofeedback. At´e a` data, na˜o existem informac¸˜oes detalhadas sobre quais as estrat´egias de neurofeed back que levam a uma recupera¸ca˜o funcional bem-sucedida. De igual modo, pouco se sabe acerca de como utilizar, de forma otimizada, o paradigma de neurofeedback na recupera¸c˜ao de AVC. Face a tal, o objetivo deste projeto ´e investigar e desenvolver um novo sistema de reabilita¸ca˜o de membros supe riores, recorrendo ao uso de novas TIC, incluindo sistemas como a Interface C´erebro-Computador (ICC) e RV. Atrav´es de um conjunto de estudos, ilus tramos o design do framework RehabNet e o seu foco numa terapia motora e cognitiva, integrativa, baseada em cen´arios de RV. Adicionalmente, ampli amos os crit´erios de inclus˜ao para pacientes com baixa mobilidade, atrav´es do desenvolvimento de ferramentas de neurofeedback com a utilizac¸˜ao de ICC, ao mesmo que investigando os efeitos de uma interac¸˜ao c´erebro-para-RV

Електроенцефалографски сигнали за управљање рачунарским интерфејсом у неурорехабилитацији

Мозак-рачунар интерфејс (МоРИ) системи могу искористити карактеристичне промене мождане активности корисника као контролне сигнале уређаја (рачунара). Различити ментални задаци или спољашњи стимулуси (визуелни, аудитивни или соматосензорни) индукују промене које су кодиране у спонтаној неуралној активности. Генерисане промене се могу идентификовати мерењем можданих сигнала који представљају директну или индиректну меру електричне активности мозга...Brain Computer Interface (BCI) systems can use characteristic brain neural alterations as control signals of the device/computer. Various mental tasks or external stimulation (visual, auditory or somatosensory) induce changes which are embedded in the spontaneous neural activity. Generated changes can be extracted and identified from the brain-signal recordings that represent the (direct or indirect) measure of electrical neural activity..