Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements

Abstract

Electroencephalography (EEG) signals were analyzed in many research applications as a channel of communication between humans and computers. EEG signals associated with imagined fists and feet movements were filtered and processed using wavelet transform analysis for feature extraction. The proposed work used Neural Networks (NNs) as a classifier that enables the classification of imagined movements into either fists or feet. Wavelet families such as Daubechies, Symlets, and Coiflets wavelets were used to analyze the extracted events and then different feature extraction measures were calculated for three detail levels of the wavelet coefficients. Intensive NN training and testing experiments were carried out and different network configurations were compared. The optimum classification performance of 89.11% was achieved with a NN classifier of 20 hidden layers while using the Mean Absolute Value (MAV) of the Coiflets wavelet coefficients as inputs to NN. The proposed system showed a good performance that enables controlling computer applications via imagined fists and feet movements.