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Eyeblink recognition improves fatigue prediction from single-channel forehead EEG in a realistic sustained attention
Li-Wei Ko1,2,3,4, Oleksii Komarov5,2, Wei-Kai Lai2
1Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Hsinchu, Taiwan, Republic of China.
Objective:
A passive brain-computer interface recognizes its operator's cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded signals become greatly contaminated with eyeblink and movement artifacts, which makes it a challenge to acquire the data of suitable for analysis quality, particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms. In this article, we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis, it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor.
Approach:
Fifteen healthy 22-28 years old participants took part in the experiment that implemented a realistic sustained attention task of nighttime highway driving in a virtual environment. The EEG data were collected using a portable wireless Mindo-4 device, which constitutes an adjustable elastic strip with foam-based sensors, a data-acquisition module, an amplification and digitizing unit, and a Bluetooth[Formula: see text] module.
Main Results:
The spectral analysis of the EEG samples that immediately preceded the lane departure events revealed alterations in the tonic power spectral density, which accompanied elongations in the drivers' reaction times. The RMSE of the predicted reaction times, which are based on a combination of the brain-related and eyeblink features, is 0.034 ± 0.019 s, and the r2 is 0.885 ± 0.057 according to a within-session leave-one-trial-out cross-validation.
Significance:
The drowsiness prediction from a frontal single-channel setup can achieve a comparable performance with using an array of occipital EEG sensors. As a direct result of utilizing a dry sensor placed in the non-covered with hair head area, the proposed approach in this study is low-cost and user-friendly.
