Reza Foodeh; Vahid Shalchyan; Mohammad Reza Daliri
Volume 10, Issue 3 , October 2016, , Pages 267-277
Abstract
Extracting discriminative features is a crucial step in brain-computer interfaces (BCIs) that could affect directly on the classification performance. Common spatial patterns (CSP) is a commonly used algorithm for such propose in motor imagery based BCI systems. CPS tries to extract the most appropriate ...
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Extracting discriminative features is a crucial step in brain-computer interfaces (BCIs) that could affect directly on the classification performance. Common spatial patterns (CSP) is a commonly used algorithm for such propose in motor imagery based BCI systems. CPS tries to extract the most appropriate spatial patterns in the electroencephalogram (EEG) signals to discriminate different motor imagery classes. Before applying CSP, Usually EEG signals are filtered out in 8-30 Hz to capture event related desynchronization (ERD) specific frequency rhythms called mu and beta bands. However, this frequency band could be highly subject specific. Therefore, optimizing spectral and spatial filters jointly could improve the classification accuracy. In this paper, we proposed a novel learning algorithm to derive spatial and spectral filters simultaneously using an evolutionary learning algorithm called particle swarm optimization (PSO). Furthermore, we utilized mutual information between extracted features and class labels as a cost function in the learning algorithm. Our simulations on BCI competition IV, dataset 1 reveals that the proposed method significantly outperforms the conventional CSP and filter bank CSP (FBCSP) with two different filter bank architectures.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mehdi Abdossalehi; Ali Motie Nasrabadi; Seyed Mohammad Firouzabadi
Volume 7, Issue 2 , June 2013, , Pages 143-153
Abstract
In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving ...
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In this study, electroencephalogram (EEG) signals have been analyzed in positive, negative and neutral emotions. Here it is supposed that the brain has different independent sources during an emotional activity which will be extractable by Independent Component Analysis (ICA) algorithm. For resolving the illposeness problem of extracted components by ICA algorithm, first these sources were sorted by Shannon entropy and then the features of Katz fractal dimension and the first local minimum of the mutual information based on the time delay (tau) have been extracted for representing determinism. The results show that the determinism ratio of the sorted sources has significant difference during the time in three emotional states: positive, negative and neutral. The determinism ratio increases in neutral, negative and positive emotional states, respectively.
