Detecting Frontal EEG Activities with Forehead Electrodes

Authors:
Jeng-Ren Duann;Po-Chuan Chen;Li-Wei Ko;Ruey-Song Huang;Tzyy-Ping Jung;Chin-Teng Lin
Affiliations:
Institute for Neural Computation, University of California San Diego, La Jolla, USA and Brain Reseach Center, National Chiao Tung University, Hsinchu, Taiwan;Brain Reseach Center, National Chiao Tung University, Hsinchu, Taiwan;Brain Reseach Center, National Chiao Tung University, Hsinchu, Taiwan;Institute for Neural Computation, University of California San Diego, La Jolla, USA;Institute for Neural Computation, University of California San Diego, La Jolla, USA and Brain Reseach Center, National Chiao Tung University, Hsinchu, Taiwan;Brain Reseach Center, National Chiao Tung University, Hsinchu, Taiwan
Venue:
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Year:
2009

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Abstract

This study demonstrates the acquisitions of EEG signals from non-hairy forehead sites and tested the feasibility of using the forehead EEG in detecting drowsiness-related brain activities. A custom-made 15-channel forehead EEG-electrode patch and 28 scalp electrodes placed according to the International 10-20 system were used to simultaneously record EEG signals from the forehead and whole-head regions, respectively. A total of five subjects were instructed to perform a night-time long-haul driving task for an hour in a virtual-reality based driving simulator comprising a real car mounted on a 6 degree-of-freedom Steward motion platform and a immersive VR environment with 360 degree projection scenes. Separate independent component analyses were applied to the forehead and whole-head EEG data for each individual subject. For the whole-head independent component (IC) set, the frontal central midline (FCM) IC with an equivalent dipole source located in the anterior cingulate cortex was selected for further analysis. For the forehead IC set, the IC with its theta power changes highly correlated with subject's driving performance was selected. The EEG power changes of the selected forehead ICs were then used to predict driving performance based on a linear regression model. The results of this study showed that it is feasible to accurately estimate quantitatively the changing level of driving performance using the EEG features obtained from the forehead non-hairy channels, and the estimation accuracy was comparable to that using the EEG features of the whole-head recordings.