Classifying EEG Signals in Single-Channel SSVEP-based BCIs through Support Vector Machine

Electroencephalography (EEG) headsets are wearable computing devices capable of recording electrical activity of the brain. These devices play a key role in the Brain-Computer Interfaces (BCIs) systems, i.e., systems capable of acquiring, processing and classifying EEG signals in order to control external devices such as wireless prosthetics. In spite of their crucial role, the current EEG headsets are very uncomfortable being composed of many wet electrodes. Hence, single-channel BCIs with dry electrodes are emerging like wearable devices more accepted by users. Unfortunately, this kind of device typically provides weaker and noisier signal that makes more challenging the classification task. This work is aimed at improving the quality of the classification of EEG signals, and in particular of Steady-State Visual Evoked Potentials (SSVEP), captured by single-channel EEG devices by using an evolutionary algorithm-based optimized version of Support Vector Machine (SVM). As shown by experimental results, the proposed approach improves on the state-of-the-art methods in terms of accuracy.