Estimating vigilance from EEG using manifold clustering guided by instantaneous lapse rate

Vigilance decrement happens in prolonged and monotonous tasks such as driving, therefore efficient estimation of vigilance using machine learning becomes a growing research field in road safety. However, the ground truth of vigilance level is often unknown. To address the estimation of brain states with unknown ground truth, we proposed an unsupervised manifold clustering method guided by task performance, namely instantaneous lapse rate, without directly using any artificially labels, using electroencephalogram (EEG) as data source. The proposed algorithm utilizes information from both data structure and task performance, which is especially suitable for applications with unknown ground truth. Future research directions include using advanced manifold clustering algorithms to increase the robustness towards the high nonlinearity in the EEG feature space and the embedded space, as well as allowing the mapping from multiple clusters to one vigilance level.