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Two Different Approaches of Feature Extraction for Classifying the EEG Signals

Abstract : The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain.The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.
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Pari Jahankhani, Juan Lara, Aurora Pérez, Juan Valente. Two Different Approaches of Feature Extraction for Classifying the EEG Signals. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.229-239, ⟨10.1007/978-3-642-23957-1_26⟩. ⟨hal-01571354⟩

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