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Spatial and spatio-temporal filtering based on common spatial patterns and Max-SNR for detection of P300 component

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Rizi F. S. , Abootalebi V. , Sadeghi M. T.

Biocybernetics and Biomedical Engineering

2017

tom Vol. 37, no. 3

365--372

Recent advances in brain-computer interfaces (BCIs) have developed a new arena for designing systems to help disabled persons to communicate with the surrounding environment. P300 speller is one of the most famous BCI systems choosing the characters from a virtual keyboard through the analysis of EEG signals. P300 detection is an important processing step of these systems. The accuracy of P300 detection highly depends on the feature extraction method. In this study, the maximum signal to noise ratio (Max-SNR) has been used for feature extraction, which rarely applied in this area. This study presents a novel feature extraction technique, named spatio-temporal Max-SNR (ST.Max-SNR). Unlike the standard Max-SNR which only uses spatial patterns of a signal, the proposed method, separately consider the spatial and temporal patterns of the signal to enhance the accuracy of feature extraction. Due to the similarity of the common spatial pattern (CSP) and the Max-SNR algorithms, the performance of this technique and its extension, common Spatio- temporal pattern (CSTP), has been compared with the proposed method. Then, the LDA and SWLDA classifiers are used for classification of the features. Our experimental results show that the Max-SNR based spatio-temporal features lead to an average classification accuracy of 94.4 percent suggesting the best performance.

Comparison of performance of different feature extraction methods in detection of P300

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Amini Z. , Abootalebi V. , Sadeghi M. T.

Biocybernetics and Biomedical Engineering

2013

tom Vol. 33, no. 1

3--20

The aim of this paper is to design a pattern recognition based system to detect the P300 component in the EEG trials. This system has two main blocks, feature extraction and classification. In the feature extraction block, in addition to morphological features, some new features including intelligent segmentation, common spatial pattern (CSP) and combined features (CSP + Segmentation) have also been used. Two criteria were used for the feature evaluation. Firstly, a t-test has been applied. Secondly, each of these four groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Afterwards, the best set of features was selected by using Stepwise Linear Discriminant Analysis (SWLDA). In the classification phase, the LDA was used as a linear classifier. The algorithm described here was tested with dataset II from the BCI competition 2005. In this research, the best result for the P300 detection was 97.4% .This result has proven to be more accurate than the results of previous works carried out in this filed.

Selection of an efficient feature space for EEG-based mental task discrimination

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Noshadi S. , Abootalebi V. , Sadeghi M. T. , Shahvazian M. S.

Biocybernetics and Biomedical Engineering

2014

tom Vol. 34, no. 3

159--168

The aim of this paper is to contribute toward exploring an optimal feature space for discriminating mental tasks. Empirical mode decomposition (EMD) algorithm seems useful for designing such a feature space. The adjustment of nonlinear and non-stationary properties of the EEG signals with this algorithm and the successful application of this approach together biomedical signal processing problems encourage us to examine a variety of statistical and spectral measures within the EMD space as the adapted features. In this sense, as a measure of complexity, the Lempel–Ziv algorithm is utilized within the frame-work of the EMD algorithm. A modified form of the Lempel–Ziv complexity algorithm is then proposed. The features derived from the modified algorithm outperform the other features individually. By combining the modified Lempel–Ziv features with the other adopted features, in average, 97.78% classification accuracy is achieved for different subjects. It is concluded that the EMD–LZ kernel allows for achieving of better performances in classifying mental tasks than the results obtained with other methods.