Signal processing methods for EEG data classification

Imperial Users onl

A Brain-Computer Interface based on Colour Dependent Visual Attention

In this thesis we designed a specific visual protocol for a new application in the brain-computer interface field. We evaluated how coloured stimuli affect brain activity in health

Detecting error related negativity using EEG potentials generated during simulated brain computer interaction

2014 Summer.Includes bibliographical references.Error related negativity (ERN) is one of the components of the Event-Related Potential (ERP) observed during stimulus based tasks. In order to improve the performance of a brain computing interface (BCI) system, it is important to capture the ERN, classify the trials as correct or incorrect and feed this information back to the system. The objective of this study was to investigate techniques to detect presence of ERN in trials. In this thesis, features based on averaged ERP recordings were used to classify incorrect from correct actions. One feature selection technique coupled with four classification methods were used and compared in this work. Data were obtained from healthy subjects who performed an interaction experiment and the presence of ERN indicating incorrect responses was studied. Using suitable classifiers trained on data recorded earlier, the average recognition rate of correct and erroneous trials was reported and analyzed. The significance of selecting a subset of features to reduce the data dimensionality and to improve the classification performance was explored and discussed. We obtained success rates as high as 72% using a highly compact feature subset

A Novel P300 speller with motor imagery embedded in a traditional oddball paradigm.

A Brain Computer Interface (BCI) provides a means, to control external devices, through the electrical activity of the brain, bypassing motor movement. Recent years have seen an increase in the application of P300 cognitive potential as a control and/or communication signal for the motor restoration in paralyzed patients, such as those in the later stages of ALS (Amyotrophic lateral sclerosis). Although many of these patients are in locked-in state i.e. where motor control is not possible, their cognition is known to remain intact. The P300 speller paradigm explored in this study relying on this cognition represented by the P300 peak potential in EEG (Electroencephalography) signals to restore communication. The conventional visual oddball paradigms used to elicit P300 potential may not be the optimum choice due to their need for precise eye-gazing, which may be challenge for many patients. This study introduces a novel paradigm with motor imagery as a secondary after-stimulus task in a traditional visual oddball paradigm for P300 Speller application. We observed increased P300 peak amplitude as well as the event-related desynchronization (ERD) associated with motor imagery in six healthy novice subjects. Acceptable detection accuracy was obtained in the five-trial averaged signals from 250 ms to 750 ms after the visual stimulation, whereby the early visual evoked potentials were excluded from classification. As an enhancement, efforts are being made to assess implementation by motor imagery embedded in an auditory oddball paradigm which would minimize the need for eye-gazing further. We can conclude from the results of this study that the proposed paradigm with motor imagery embedded in a traditional visual oddball paradigm might be a feasible option for communication restoration in paralyzed patients

Towards smarter Brain Computer Interface (BCI): study of electroencephalographic signal processing and classification techniques toward the use of intelligent and adaptive BCI

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-07-202

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy

Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration

One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy