An EEG-based brain-computer interface for automatic sleep stage classification

In this paper, an EEG-based brain-computer-interface (BCI) system used for automatic sleep stage classification was proposed. After wavelet de-noising, thirteen features, including spectral and statistical analysis of single-channel EEG signal, were extracted for each epoch. Support Vector Machine was employed to classify four states (Awake/ Light Sleep/ Slow Wave Sleep/ Rapid Eye Movement). Two experiments involving ten recordings from Sleep-EDF Database and the other three healthy subjects in our laboratory were conducted to validate our method and BCI system. For the former, the overall accuracy and kappa coefficient has achieved 86.82% and 0.83 of Fpz-Cz channel, respectively (86.01% and 0.81 for Pz-Oz channel, respectively). For the latter, the overall accuracy and kappa coefficient has achieved 81.30% and 0.73, respectively. The experimental outcomes validate the superior of the proposed method in accuracy.