Numerous EEG-based brain-computer interface (BCI) systems that are being developed focus on novel feature extraction\nalgorithms, classification methods and combining existing approaches to create hybrid BCIs. Several recent studies demonstrated\nvarious advantages of hybrid BCI systems in terms of an improved accuracy or number of commands available for the user. But\nstill, BCI systems are far from realization for daily use. Having high performance with less number of channels is one of the\nchallenging issues that persists, especially with hybrid BCI systems, where multiple channels are necessary to record information\nfrom two or more EEG signal components. Therefore, this work proposes a single-channel (C3 or C4) hybrid BCI system that\ncombines motor imagery (MI) and steady-state visually evoked potential (SSVEP) approaches. This study demonstrates that\nbesides MI features, SSVEP features can also be captured from C3 or C4 channel. The results show that due to rich feature\ninformation (MI and SSVEP) at these channels, the proposed hybrid BCI system outperforms both MI- and SSVEP-based\nsystems having an average classification accuracy of 85.6 �± 7.7% in a two-class task.
Loading....