EEG-based drowsiness estimation for safety driving using independent component analysis

Chin Teng Lin, Ruei Cheng Wu, Sheng Fu Liang, Wen Hung Chao, Yu Jie Chen, Tzyy Ping Jung

Research output: Contribution to journalArticlepeer-review

315 Citations (Scopus)


Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control in (near-) real-time. The major challenges in developing such a system include: 1) the lack of significant index for detecting drowsiness and 2) complicated and pervasive noise interferences in a realistic and dynamic driving environment. In this paper, we develop a drowsiness-estimation system based on electroencephalogram (EEG) by combining independent component analysis (ICA), power-spectrum analysis, correlation evaluations, and linear regression model to estimate a driver's cognitive state when he/ she drives a car in a virtual reality (VR)-based dynamic simulator. The driving error is defined as deviations between the center of the vehicle and the center of the cruising lane in the lane-keeping driving task. Experimental results demonstrate the feasibility of quantitatively estimating drowsiness level using ICA-based multistream EEG spectra. The proposed ICA-based method applied to power spectrum of ICA components can successfully (1) remove most of EEG artifacts, (2) suggest an optimal montage to place EEG electrodes, and estimate the driver's drowsiness fluctuation indexed by the driving performance measure. Finally, we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based.

Original languageEnglish
Pages (from-to)2726-2738
Number of pages13
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Issue number12
Publication statusPublished - 2005 Dec

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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