Brain-computer interfaces are an emerging technology that could provide channels for communication and control to severely disabled people suffering from locked-in syndrome. It has been found that motor imagery can be detected and classified from EEG signals. The motivation of the present work was to compare several algorithms for motor imagery classification in EEG signals as well as to test several novel algorithms. The algorithms tested included the popular method of common spatial patterns (CSP) spatial filtering followed by linear discriminant analysis (LDA) classification of log-variance features (CSP+LDA). A second set of algorithms used classification based on concepts from Riemannian geometry. The basic idea of these methods is that sample spatial covariance matrices (SCMs) of EEG epochs belong to the Riemannian manifold of symmetric positive-definite (SPD) matrices and that the tangent space at any SPD matrix on the manifold is a finite-dimensional Euclidean space. Riemannian classification methods tested included minimum distance to Riemannian mean (MDRM), tangent space LDA (TSLDA), and Fisher geodesic filtering followed by MDRM classification (FGDA). The novel algorithms aimed to combine the CSP method with the Riemannian geometry methods. CSP spatial filtering was performed prior to sample SCM calculation and subsequent classification using Riemannian methods. The novel algorithms were found to improve classification accuracy as well as reduce the computational costs of Riemannian classification methods for binary, synchronous classification on BCI competition IV dataset 2a. / Master of Science

Syftet med uppsatsen är att ge en uppdaterad översikt av området BCI (Brain Computer Interface) och undersöka vad som hänt sedan begreppet introducerades i forskningssammanhang; vilka praktiska resultat forskningen lett till och vilka tillämpningar som tillkommit. Metoden som företrädesvis används är litteraturstudie som tecknar bakgrund och enkät. Därefter följer en diskussion där utmaningar för framtiden, potential och tillämpningar i BCI-tekniken behandlas utifrån ett neuropsykologiskt perspektiv. Kommer BCI-tekniken att implementeras på samma sätt som radio, TV och telekommunikationer i samhället och vilka etiska och tekniska problem finns idag. För att skildra allmänhetens uppfattning om BCI genomfördes en webbaserad enkätundersökning (survey) i form av pilotstudie (n=32) som syftar till att ge en indikation på attityder och hur allmänhetens opinion med avseende på tillämpningar i samtiden och jämförelser med avseende på teknisk bakgrund.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The present work proposes an algorithm to detect and identify the artifact signals produced by the concrete gestural actions of jaw clench and eyebrows raising in the electroencephalography (EEG) signal. Artifacts are signals that manifest in the EEG signal but do not come from the brain but from other sources such as flickering, electrical noise, muscle movements, breathing, and heartbeat. The proposed algorithm makes use of concepts and knowledge in the field of signal processing, such as signal energy, zero crossings, and block processing, to correctly classify the aforementioned artifact signals. The algorithm showed a 90% detection accuracy when evaluated in independent ten-second registers in which the gestural events of interest were induced, then the samples were processed, and the detection was performed. The detection and identification of these devices can be used as commands in a brain–computer interface (BCI) of various applications, such as games, control systems of some type of hardware of special benefit for disabled people, such as a chair wheel, a robot or mechanical arm, a computer pointer control interface, an Internet of things (IoT) control or some communication system. / Revisión por pares

No description available.

Objective: Brain-computer interface (BCI) technologies have been the subject of study for the past decades to help restore functions for people with severe motor disabilities and to improve their quality of life. BCI research can be generally categorized by control signals (invasive/non-invasive) or applications (e.g. neuroprosthetics/brain-actuated wheelchairs), and efforts have been devoted to better understand the characteristics and possible uses of brain signals. The purpose of this research is to explore the feasibility of a non-invasive BCI system with the combination of unique sensorimotor-rhythm (SMR) features. Specifically, a 2D virtual wheelchair control BCI is implemented to extend the application of previously designed 2D cursor control BCI, and the feasibility of the prototype is tested in electroencephalography (EEG) experiments; guidance on enhancing system performance is provided by a simulation incorporating intelligent control approaches under different EEG decoding accuracies; pattern recognition methods are explored to provide optimized classification results; and a hybrid BCI system is built to enhance the usability of the wheelchair BCI system. Methods: In the virtual wheelchair control study, a creative and user friendly control strategy was proposed, and a paradigm was designed in Matlab, providing a virtual environment for control experiments; five subjects performed physical/imagined left/right hand movements or non-control tasks to control the virtual wheelchair to move forward, turn left/right or stop; 2-step classification methods were employed and the performance was evaluated by hit rate and control time. Feature analysis and time-frequency analysis were conducted to examine the spatial, temporal and frequency properties of the utilized SMR features, i.e. event-related desynchronization (ERD) and post-movement event-related synchronization (ERS). The simulation incorporated intelligent control methods, and evaluated navigation and positioning performance with/without obstacles under different EEG decoding accuracies, to better guide optimization. Classification methods were explored considering different feature sets, tuned classifier parameters and the simulation results, and a recommendation was provided to the proposed system. In the steady state visual evoked potential (SSVEP) system for hybrid BCI study, a paradigm was designed, and an electric circuit system was built to provide visual stimulus, involving SSVEP as another type of signal being used to drive the EEG BCI system. Experiments were conducted and classification methods were explored to evaluate the system performance. Results: ERD was observed on both hemispheres during hand's movement or motor imagery; ERS was observed on the contralateral hemisphere after movement or motor imagery stopped; five subjects participated in the continuous 2D virtual wheelchair control study and 4 of them hit the target with 100% hit rate in their best set with motor imagery. The simulation results indicated that the average hit rate with 10 obstacles can get above 95% for pass-door tests and above 70% for positioning tests, with EEG decoding accuracies of 70% for Non-Idle signals and 80% for idle signals. Classification methods showed that with properly tuned parameters, an average of about 70%-80% decoding accuracy for all the classifiers could be reached, which reached the requirements set by the simulation test. Initial test on the SSVEP BCI system exhibited high classification accuracy, which may extend the usability of the wheelchair system to a larger population when finally combined with ERD/ERS BCI system. Conclusion: This research investigated the feasibility of using both ERD and ERS associated with natural hand's motor imagery, aiming to implement practical BCI systems for the end users in the rehabilitation stage. The simulation with intelligent controls provided guides and requirements for EEG decoding accuracies, based on which pattern recognition methods were explored; properly selected features and adjusted parameters enabled the classifiers to exhibit optimal performance, suitable for the proposed system. Finally, to enlarge the population for which the wheelchair BCI system could benefit for, a SSVEP system for hybrid BCI was designed and tested. These systems provide a non-invasive, practical approach for BCI users in controlling assistive devices such as a virtual wheelchair, in terms of ease of use, adequate speed, and sufficient control accuracy.

This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive electroencephalography (EEG) in order to control a discrete two-dimensional cursor movement for a potential multi-dimensional Brain-Computer interface (BCI). Five naïve subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the hand moved for both motor execution and motor imagery. Feature analysis showed that EEG beta band activity in the contralateral hemisphere over the motor cortex provided the best detection of either sustained or ceased movement of the right or left hand. The offline classification of four motor tasks (sustain or cease to move right or left hand) provided 10-fold cross-validation accuracy as high as 88% for motor execution and 73% for motor imagery. The subjects participating in experiments with physical movement were able to complete the online game with motor execution at the average accuracy of 85.5±4.65%; Subjects participating in motor imagery study also completed the game successfully. The proposed BCI provides a new practical multi-dimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training.

This thesis focuses on assistive and rehabilitation technology for restoring the function of the hand. It presents three different approaches to assistive technology: one in the form of an orthosis, one in the form of a brain-computer interface combined with functional electrical stimulation and finally one totally aiming at rehabilitating the nervous system by restoring brain function using the concept of neuroplasticity. The thesis also includes an epidemiological study based on statistics from the Swedish Hospital Discharge Register and a review on different methods for assessment of hand function. A novel invention of an orthosis in form of a light weight glove, the SEM (Soft Extra Muscle) glove, is introduced and described in detail. The SEM glove is constructed for improving the grasping capability of a human independently of the particular task being performed. A key feature is that a controlling and strengthening effect is achieved without the need for an external mechanical structure in the form of an exoskeleton. The glove is activated by input from tactile sensors in its fingertips and palm. The sensors react when the applied force is larger than 0.2 N and feed a microcontroller of DC motors. These pull lines, which are attached to the fingers of the glove and thus work as artificial tendons. A clinical study on the feasibility of the SEM glove to improve hand function on a group of patients with varying degree of disability has been made. Assessments included passive and active range of finger motion, flexor muscle strength according to the Medical Research Council (MRC) 0-5 scale, grip strength using the Grippit hand dynamometer, fine motor skills according to the Nine Hole Peg test and hand function in common activities by use of the Sollerman test. Participants rated the potential benefit on a Visual Analogue Scale. A prototype for a system for combining BCI (Brain-Computer Interface) and FES (Functional Electrical Stimulation) is described. The system is intended to be used during the first period of recovery from a TBI (Traumatic Brain Injury) or stroke that have led to paresis in the hand, before deciding on a permanent system, thus allowing the patients to get a quick start on the motor relearning. The system contains EEG recording electrodes, a control unit and a power unit. Initially the patients will practice controlling the movement of a robotic hand and then move on to controlling pulses being sent to stimulus electrodes placed on the paretic muscle. An innovative electrophysiological device for rehabilitation of brain lesions is presented, consisting of a portable headset with electrodes on both sides adapted on the localization of treatment area. The purpose is to receive the outgoing signal from the healthy side of the brain and transfer that signal to the injured and surrounding area of the remote side, thereby having the potential to facilitate the reactivation of the injured brain tissue. The device consists of a control unit as well as a power unit to activate the circuit electronics for amplifying, filtering, AD-converting, multiplexing and switching the outgoing electric signals to the most optimal ingoing signal for treatment of the injured and surrounding area. / <p>QC 20130403</p>

Individuals who suffer from amyotrophic lateral sclerosis (ALS) have a loss of motor control and possibly the loss of speech. A brain-computer interface (BCI) provides a means for communication through nonmuscular control. Visual BCIs have shown the highest potential when compared to other modalities; nonetheless, visual attention concepts are largely ignored during the development of BCI paradigms. Additionally, individual performance differences and personal preference are not considered in paradigm development. The traditional method to discover the best paradigm for the individual user is trial and error. Visual attention research and personal preference provide the building blocks and guidelines to develop a successful paradigm. This study is an examination of a BCI-based visual attention assessment in an ALS sample. This assessment takes into account the individual’s visual attention characteristics, performance, and personal preference to select a paradigm. The resulting paradigm is optimized to the individual and then tested online against the traditional row-column paradigm. The optimal paradigm had superior performance and preference scores over row-column. These results show that the BCI needs to be calibrated to individual differences in order to obtain the best paradigm for an end user.

Amyotrophic lateral sclerosis and other neurodegenerative disorders can cause individuals to lose control of their muscles until they are unable to move or communicate. The development of brain-computer interface (BCI) technology has provided these individuals with an alternative method of communication that does not require muscle movement. Recent research has shown the impact psychological factors have on BCI performance and has highlighted the need for further research. Working memory is one psychological factor that could influence BCI performance. The purpose of the present study is to evaluate the relationship between working memory and brain-computer interface performance. The results indicate that both working memory and general intelligence are significant predictors of BCI performance. This suggests that working memory training could be used to improve performance on a BCI task.

The effects of neurodegenerative diseases such as amyotrophic-lateral sclerosis (ALS) eventually render those suffering from the illness unable to communicate, leaving their cognitive function relatively unharmed and causing them to be “locked-in” to their own body. With this primary function compromised there has been an increased need for assistive communication methods such as brain-computer interfaces (BCIs). Unlike several augmentative or alternative communication methods (AACs), BCIs do not require any muscular control, which makes this method ideal for people with ALS. The wealth of BCI research focuses mainly on increasing BCI performance through improving stimulus processing and manipulating paradigms. Recent research has suggested a need for studies focused on harnessing psychological qualities of BCI users, such as motivation, mood, emotion, and depression, in order to increase BCI performance through working with the user. The present studies address important issues related to P300-BCI performance: 1) the impact of mood, emotion, motivation, and depression on BCI performance were examined independently; and 2) pleasant, unpleasant, and neutral emotions were induced in order to determine the influence of emotion on BCI performance. By exploring psychological mechanisms that influence BCI performance, further insight can be gained on the best methods for improving BCI performance and increasing the number of potential BCI users. The results from Study 1 did not reveal a significant relationship between any of the four psychological factors and BCI performance. Since previous research has found a significant impact of motivation and mood on BCI performance, it may be the case that these factors only impact performance for some individuals. As this is the first study to directly investigate the impact of emotion and depression on BCI performance, future research should continue to explore these relationships. The results from Study 2 were inconclusive for the pleasant condition, since it appears the pleasant emotion manipulation was unsuccessful. The findings indicate that unpleasant emotions do not have a significant impact on BCI performance. This result is promising since it indicates that individuals should still be able to use the BCI system to communicate, even when they are experiencing unpleasant emotions. Future research should further explore the impact of pleasant emotions on BCI performance.