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- W2401497722 abstract "Drowsiness is a serious problem for drivers which causes many accidents every day. It is estimated that drowsinessis the cause of four deaths and 100 injuries per day in the United States. In this paper two methods havebeen developed to detect drowsiness based on features of ocular artifacts in EEG signals. The ocular artifactsare derived from the EEG signals by using Canonical Correlation Analysis (BSS-CCA). Wavelet transformsare used to automatically select components containing eye blinks. Sixteen features are then calculated fromthe eye blink and used for drowsiness detection. The first method is based on linear regression, the second onfuzzy detection. For the first method, the drowsiness level is correctly detected in 72% of the epochs. The secondmethod uses fuzzy detection and detects the drowsiness correctly in 65% of the epochs. The best resultsare obtained when using one single eye blink feature." @default.
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- W2401497722 date "2014-01-01" @default.
- W2401497722 modified "2023-09-24" @default.
- W2401497722 title "Advanced EEG Processing for the Detection of Drowsiness in Drivers" @default.
- W2401497722 doi "https://doi.org/10.5220/0004800102050212" @default.
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