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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.

There have been substantial improvements in the area of rehabilitation robotics in the recent past. However, these advances are inaccessible to a large number of people with disabilities who are in most need of such assistance. This group includes people who are in a severely paralyzed state, that they are completely "locked-in" in their own bodies. Such persons usually retain full cognitive abilities, but have no voluntary muscle control. For these persons, a Brain Computer Interface (BCI) is often the only way to communicate with the outside world and/or control an assistive device. One major drawback to BCI devices is their low information transfer rate, which can take as long as 30 seconds to select a single command. This can result in mental fatigue to the user, specially if it necessary to make multiple selections over the BCI to complete a single task. Therefore, P300 based BCI control is not efficient for controlling a assistive robotic device such as a robotic arm. To address this shortcoming, a novel vision based Brain Robot Interface (BRI) is presented in this thesis. This visual user interface allows for selecting an object from an unstructured environment and then performing an action on the selected object using a robotic arm mounted to a power wheelchair. As issuing commands through BCI is slow, this system was designed to allow a user to perform a complete task via a BCI using an autonomous robotic system while issuing as few commands as possible. Furthermore, the new visual interface allows the user to perform the task without losing concentration on the stimuli or the task. In our interface, a scene image is captured by a camera mounted on the wheelchair, from which, a dynamically sized non-uniform stimulus grid is created using edge information. Dynamically sized grids improve object selection efficiency. Oddball paradigm and P300 Event Related Potentials (ERP) are used to select stimuli, where the stimuli being each cell in the grid. Once selected, object segmentation and matching is used to identify the object. Then the user, using BRI, chooses an action to be performed on the object by the wheelchair mounted robotic arm (WMRA). Tests on 8 healthy human subjects validated the functionality of the system. An average accuracy of 85.56% was achieved for stimuli selection over all subjects. With the proposed system, it took the users an average of 5 commands to perform a task on an object. The system will eventually be useful for completely paralyzed or locked-in patients for performing activities of daily living (ADL) tasks.

This research set out to develop a novel technique to decompose Electroencephalograph (EEG) signal into sets of constituent peaks in order to better describe the underlying nature of these signals. It began with the question; can a localised, single stimulation of sensory nervous tissue in the body be detected in the brain? Flash Visual Evoked Potential (VEP) tests were carried out on 3 participants by presenting a flash and recording the response in the occipital region of the cortex. By focussing on analysis techniques that retain a perspective across different domains � temporal (time), spectral (frequency/scale) and epoch (multiple events) � useful information was detected across multiple domains, which is not possible in single domain transform techniques. A comprehensive set of algorithms to decompose evoked potential data into sets of peaks was developed and tested using wavelet transform singularity detection methods. The set of extracted peaks then forms the basis for a subsequent clustering analysis which identifies sets of localised peaks that contribute the most towards the standard evoked response. The technique is quite novel as no closely similar work in research has been identified. New and valuable insights into the nature of an evoked potential signal have been identified. Although the number of stimuli required to calculate an Evoked Potential response has not been reduced, the amount of data contributing to this response has been effectively reduced by 75%. Therefore better examination of a small subset of the evoked potential data is possible. Furthermore, the response has been meaningfully decomposed into a small number (circa 20) of constituent peaksets that are defined in terms of the peak shape (time location, peak width and peak height) and number of peaks within the peak set. The question of why some evoked potential components appear more strongly than others is probed by this technique. Delineation between individual peak sizes and how often they occur is for the first time possible and this representation helps to provide an understanding of how particular evoked potentials components are made up. A major advantage of this techniques is the there are no pre-conditions, constraints or limitations. These techniques are highly relevant to all evoked potential modalities and other brain signal response applications � such as in brain-computer interface applications. Overall, a novel evoked potential technique has been described and tested. The results provide new insights into the nature of evoked potential peaks with potential application across various evoked potential modalities.

A key issue in understanding how the brain functions is the ability to correlate functional information with anatomical localisation. Functional information can be provided by a variety of techniques like positron emission tomography (PET), functional MRI (fMRI), electroencephalography (EEG), magnetoencephalography (MEG) or transcranial magnetic stimulation (TMS). All these methods provide different, but complementary, information about the functional areas of the brain. PET and fMRI provide spatially accurate picture of brain regions involved in a given task. TMS permits to infer the contribution of the stimulated brain area to the task under investigation. EEG and MEG, which reflects brain activity directly, have temporal accuracy of the order of a millisecond. TMS, EEG and MEG are offset by their low spatial resolution. In this thesis, we propose two methods to improve the spatial accuracy of method based on TMS and EEG. The first part of this thesis presents an automatic method to improve the localisation of TMS points. The method enables real-time visualisation and registration of TMS evoked responses and MRI. A MF digitiser is used to sample approximately 200 points on the subject's head following a specific digitisation pattern. Registration is obtained by minimising the RMS point to surface distance, computed efficiently using the Euclidean distance transform. Functional maps are created from TMS evoked responses projected onto the brain surface previously segmented from MRI. The second part presents the possibilities to set up a brain-computer interface (BCI) based on reconstructed sources of EEG activity and the parameters to adjust. Reconstructed sources could improve the EEG spatial accuracy as well as add biophysical information on the origin of the signal. Both informations could improve the BCI classification step. Eight BCIs are built to enable comparison between electrode-based and reconstructed source-based BCIs. Tests on detection of laterality of upcoming hand movement demonstrate the interest of reconstructed sources.

A brain-computer interface (BCI) allows individuals to communicate through the modulation of regional brain activity. Clinical near-infrared spectroscopy (NIRS) is used to monitor changes in cerebral blood oxygenation due to functional activation. It was hypothesized that visually-cued emotional induction tasks can elicit detectable activity in the prefrontal cortex. Data were collected from eleven participants as they performed positively and negatively-valenced emotional induction tasks. Baseline and activation trials were classified offline with accuracies from 75.0-96.7% after applying a feature selection algorithm to determine optimal performance parameters for each participant. Feature selection identified common discriminatory features across participants and relationships between performance parameters. Additionally, classification accuracy was used to quantify NIRS hemodynamic response latency. Significant increases in classification rates were found as early as 2.5 s after initial stimulus presentation. These results suggest the potential application of emotional induction as a NIRS-BCI control paradigm.

Near-infrared spectroscopy (NIRS) in an imaging technique that has gained much attention in brain-computer interfaces (BCIs). Previous NIRS-BCI studies have primarily employed temporal features, derived from the time course of hemodynamic activity, despite potential value contained in the spatial attributes of a response. In an initial offline study, we investigated the value of using joint spatial-temporal pattern classification with dynamic NIR topograms to differentiate intentional cortical activation from rest. With the inclusion of spatiotemporal features, we demonstrated a significant increase in achievable classification accuracies from those obtained using temporal features alone (p < 10-4). In a second study, we evaluated the feasibility of implementing joint spatial-temporal pattern classification in an online system. We developed an online system-paced NIRS-BCI, and were able to differentiate two cortical states with high accuracy (77.4±10.5%). Collectively, these findings demonstrate the value of including spatiotemporal features in the classification of functional NIRS data for BCI applications.

A brain-computer interface (BCI) allows individuals to communicate through the modulation of regional brain activity. Clinical near-infrared spectroscopy (NIRS) is used to monitor changes in cerebral blood oxygenation due to functional activation. It was hypothesized that visually-cued emotional induction tasks can elicit detectable activity in the prefrontal cortex. Data were collected from eleven participants as they performed positively and negatively-valenced emotional induction tasks. Baseline and activation trials were classified offline with accuracies from 75.0-96.7% after applying a feature selection algorithm to determine optimal performance parameters for each participant. Feature selection identified common discriminatory features across participants and relationships between performance parameters. Additionally, classification accuracy was used to quantify NIRS hemodynamic response latency. Significant increases in classification rates were found as early as 2.5 s after initial stimulus presentation. These results suggest the potential application of emotional induction as a NIRS-BCI control paradigm.