Exploiting EEG-extracted eye movements for a hybrid SSVEP home automation system

Abstract

Detection of eye movements using standard EEG channels can allow for the development of a hybrid BCI (hBCi) system without requiring additional hardware for eye gaze tracking. This work proposes a hierarchical classification structure to classify eye movements into eight different classes, covering both horizontal and vertical eye movements, at two different gaze angles in each of four directions. Results show that the highest eye movement classification was obtained with frontal EEG channels, achieving an accuracy of 98.47% for two directions, 74.38% with four directions and 58.31% with eight directions. Eye movements can also be classified reliably in four directions using occipital electrodes with an accuracy of 47.60% which increases to around 80% if three frontal channels are also included. The latter result was used to develop a hybrid SSVEP home automation system which exploits the EEG-extracted eye movement information. Results show that a sequential hBCI gave an average accuracy of 82.5% when compared to the 69.17% obtained with a standard SSVEP based BCI system.