EEG-based drowsiness estimation for safety driving using independent component analysis
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 chal...
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| Vydané v: | IEEE transactions on circuits and systems. I, Regular papers Ročník 52; číslo 12; s. 2726 - 2738 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.12.2005
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1549-8328, 1558-0806 |
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| Abstract | 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. |
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| AbstractList | 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. [...] we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based. |
| Author | Sheng-Fu Liang Ruei-Cheng Wu Yu-Jie Chen Wen-Hung Chao Chin-Teng Lin Tzyy-Ping Jung |
| Author_xml | – sequence: 1 givenname: Chin-Teng surname: Lin fullname: Lin, Chin-Teng – sequence: 2 givenname: Ruei-Cheng surname: Wu fullname: Wu, Ruei-Cheng – sequence: 3 givenname: Sheng-Fu surname: Liang fullname: Liang, Sheng-Fu – sequence: 4 givenname: Wen-Hung surname: Chao fullname: Chao, Wen-Hung – sequence: 5 givenname: Yu-Jie surname: Chen fullname: Chen, Yu-Jie – sequence: 6 givenname: Tzyy-Ping surname: Jung fullname: Jung, Tzyy-Ping |
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| Snippet | Preventing accidents caused by drowsiness has become a major focus of active safety driving in recent years. It requires an optimal technique to continuously... [...] we present a benchmark study in which the accuracy of ICA-component-based alertness estimates compares favorably to scalp-EEG based. |
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| SubjectTerms | Accidents Brain modeling Correlation coefficient drowsiness electroencephalogram Electroencephalography Independent component analysis independent component analysis (ICA) linear regression model Optimal control power spectrum Safety Spectrum analysis Studies Traffic accidents & safety Vehicle detection Vehicle driving Vehicle dynamics virtual reality (VR) Working environment noise |
| Title | EEG-based drowsiness estimation for safety driving using independent component analysis |
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