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  • Adapting subject-independent task-specific EEG feature masks using PSO

    27 October 2017

    Dimension reduction is an important step toward asynchronous EEG based BCI systems, with EA based Feature/ Electrode Reduction (FR/ER) methods showing significant potential for this purpose. A PSO based approach can reduce 99% of the EEG data in this manner while demonstrating generalizability through the use of 3 new subsets of features/electrodes that are selected based on the best performing subset on the validation set, the best performing subset on the testing set, and the most commonly used features/electrodes in the swarm. This study is focused on applying the subsets generated from 4 subjects on a 5th one. Two schemes for this are implemented based on i) extracting separate subsets of feature/electrodes for each subject (out of 4 subjects) and combining the final products together for use with the 5th subject, and ii) concatenating the preprocessed EEG data of 4 subjects together and extracting the desired subset with PSO for use with the 5th subject. The results indicate the feasibility of generating subsets of feature/electrode indexes that are task specific and can be used on new subjects. © 2012 IEEE.

  • Evolutionary feature selection and electrode reduction for EEG classification

    18 January 2018

    EEG signals usually have a high dimensionality which makes it difficult for classifiers to learn the difference of various classes in the underlying pattern in the signal. This paper investigates several evolutionary algorithms used to reduce the dimensionality of the data. The study presents electrode and feature reduction methods based on Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Evolution-based methods are used to generate a set of indexes presenting either electrode seats or feature points that maximizes the output of a weak classifier. The results are interpreted based on the dimensionality reduction achieved, the significance of the lost accuracy, and the possibility of improving the accuracy by passing the chosen electrode/feature sets to alternative classifiers. © 2012 IEEE.

  • Dimension reduction in EEG data using particle swarm optimization

    27 October 2017

    EEG data contains high-dimensional data that requires considerable computational power for distinguishing different classes. Dimension reduction is commonly used to reduces the necessary training time of the classifiers with some degree of accuracy lost. The dimension reduction is usually performed on either feature or electrode space. In this study, a new dimension reduction method that reduce the number of electrodes and features using variations of Particle Swarm Optimization (PSO) is used. The variation is in terms of parameter adjustment and adding a mutation operator to the PSO. The results are assessed based on the dimension reduction percentage, the potential of selected electrodes and the degree of performance lost. An Extreme Learning Machine (ELM) is used as the primary classifier to evaluate the sets of electrodes and features selected by PSO. Two alternative classifiers such as Polynomial SVM and Perceptron are used for further evaluation of the reduced dimension data. The results indicate the potential of variations of PSO for reducing up to 99% of the data with minimal performance lost. © 2012 IEEE.

  • The use of evolutionary algorithm-based methods in EEG based BCI systems

    26 April 2018

    Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a system that uses human brain-waves recorded from the scalp as a means for providing a new communication channel by which people with limited physical communication capability can effect control over devices such as moving a mouse and typing characters. Evolutionary approaches have the potential to improve the performance of such system through providing a better sub-set of electrodes or features, reducing the required training time of the classifiers, reducing the noise to signal ratio, and so on. This chapter provides a survey on some of the commonly used EA methods in EEG study. © 2013, IGI Global.

  • Cross subject mental work load classification from electroencephalographic signals with automatic artifact rejection and muscle pruning

    3 April 2018

    � Springer International Publishing AG 2016. Purpose of this study was to understand the effect of automatic muscle pruning of electroencephalograph on cognitive work load prediction. Pruning was achieved using an automatic Independent Component Analysis (ICA) based component classification. Initially, raw data from EEG recording was used for prediction, this result was then compared with mental work load prediction results from muscle-pruned EEG data. This study used Support Vector Machine (SVM) with Linear Kernel for cognitive work load prediction from EEG data. Initial part of the study was to learn a classification model from the whole data, whereas the second part was to learn the model from a set of subjects and predict the mental work load for an unseen subject by the model. The experimental results show that an accuracy of nearly 100% is possible with ICA and automatic pruning based pre-processing. Cross subject prediction significantly improved from a mean accuracy of 54% to 69% for an unseen subject with the pre-processing.

  • A comparative study of the detection of direct causal influence with bivariate and multivariate measures for EEG

    27 October 2017

    © 2014 IEEE. In this paper, we perform the first comparison of a large variety of connectivity measures in detecting causal effects among observed interacting systems based on their statistical significance. Well-known measures estimating direction and strength interdependence between time series are compared: information theoretic measures, model- based multivariate measures in the frequency domain, and the time domain, and phase-based measures. At the same time the phase locking index is used to consider phase relationship between signals, where the phase locking value implies that the response is delayed with respect to drive at some frequency. The performance of measures is tested on simulated data from three systems: three coupled Hénon maps; a multivariate autoregressive (MVAR) model with and without EEG as an exogenous input; and simulated EEG. No measure was consistently superior. Measures that model the data as MVAR perform well when the data are drawn from that model. Frequency domain measures perform well when the data have a clearly defined band of interest. When neither of these is true, information theoretic measures perform well.

  • EEG source analysis of data from paralysed subjects

    26 April 2018

    © 2015 SPIE. One of the limitations of Encephalography (EEG) data is its quality, as it is usually contaminated with electric signal from muscle. This research intends to study results of two EEG source analysis methods applied to scalp recordings taken in paralysis and in normal conditions during the performance of a cognitive task. The aim is to determinate which types of analysis are appropriate for dealing with EEG data containing myogenic components. The data used are the scalp recordings of six subjects in normal conditions and during paralysis while performing different cognitive tasks including the oddball task which is the object of this research. The data were pre-processed by filtering it and correcting artefact, then, epochs of one second long for targets and distractors were extracted. Distributed source analysis was performed in BESA Research 6.0, using its results and information from the literature, 9 ideal locations for source dipoles were identified. The nine dipoles were used to perform discrete source analysis, fitting them to the averaged epochs for obtaining source waveforms. The results were statistically analysed comparing the outcomes before and after the subjects were paralysed. Finally, frequency analysis was performed for better explain the results. The findings were that distributed source analysis could produce confounded results for EEG contaminated with myogenic signals, conversely, statistical analysis of the results from discrete source analysis showed that this method could help for dealing with EEG data contaminated with muscle electrical signal.

  • Detection of coupling with linear and nonlinear synchronization measures for EEG

    27 October 2017

    There has been extensive research aimed at measuring synchronization to study the relationships between complex time series, such as electroencephalography (EEG). We compare six synchronization measures: the linear measures of cross-correlation, coherence and partial coherence, and three nonlinear similarity measures, namely correntropy, phase index and mutual information. We apply these measures to simulated data (unidirectionally coupled Hénon maps) to test the detection of nonlinear and nonstationary interdependence, including in the presence of noise, and to simulated EEG. No measure fails, none is the clear winner, all measures have advantages and disadvantages. 'Best measure' depends on the research aims and data. The tests selected here for EEG research recommend correntropy as the preferred measure. © 2014 IEEE.