Search

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.

In this paper we describe a prototype wearable interface that shares a user's first person view and their current emotional state with a remote user in order to create a shared emotional experience. A user evaluation was conducted to explore which interface cues best helped a remote user understand what the local user was feeling. The results showed simple visual cues provided a significantly enhanced experience over no cues at all, or a more detailed data representation. Refereed/Peer-reviewed