Reservoir computing approaches to microsleep detection
Stephen J. Weddell, Sudhanshu S. D. P. Ayyagari, Richard D. Jones · Journal of Neural Engineering · 2020
Abstract Objective. The detection of microsleeps in a wide range of professionals working in high-risk occupations is very important to workplace safety. A microsleep classifier is presented that employs a reservoir computing (RC) methodology. Specifically, echo state networks (ESN) are used to enhance previous benchmark performances on microsleep detection. Approach. A clustered design using a novel ESN-based leaky integrator is presented. The effectiveness of this design lies with the simplicity of using a fine-grained architecture, containing up to 8 neurons per cluster, to capture individualized state dynamics and achieve optimal performance. This is the first study to have implemented and evaluated EEG-based microsleep detection using RC models for the detection of microsleeps from the EEG. Main results. Microsleep state detection was achieved using a cascaded ESN classifier with leaky-integrator neurons employing 60 principal components from 544 power spectral features. This resulted in a leave-one-subject-out average detection in performance of φ = 0.51 ± 0.07 (mean ± SE), AUC− ROC = 0.88 ± 0.03, and AUC− PR = 0.44 ± 0.09. Significance. Although performance of EEG-based microsleep detection systems is still considered modest, this refined method achieved a new benchmark in microsleep detection.