Cross-Subject Zero Calibration Driver's Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 29; pp. 905 - 915
• Main Authors: Paulo, Joao Ruivo, Pires, Gabriel, Nunes, Urbano J.
• Format: Journal Article
• Language: English
• Published: United States IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Advanced driver assistance systems; Artificial neural networks; Attention task; Brain injury; Calibration; Classification; convolutional neural network; Driver’s drowsiness detection; Driving ability; Drowsiness; EEG; Electrodes; Electroencephalography; Entropy; Fatigue; Gramian angular fields; Human-computer interface; Image classification; Interfaces; Neural networks; recurrence plot; Signal to noise ratio; Sleepiness; Task analysis; Windows (intervals)

This paper explores two methodologies for drowsiness detection using EEG signals in a sustained-attention driving task considering pre-event time windows, and focusing on cross-subject zero calibration. Driving accidents are a major cause of injuries and deaths on the road. A considerable portion of...