Abstract
In this paper, a deep-learning-based driver-drowsiness detection for brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS) is investigated. The passive brain signals from drowsiness were acquired from 13 healthy subjects while driving a car simulator. The brain activities were measured with a continuous-wave fNIRS system, in which the prefrontal and dorsolateral prefrontal cortices were focused. Deep neural networks (DNN) were pursued to classify the drowsy and alert states. For training and testing the models, the convolutional neural networks (CNN) were used on color map images to determine the best suitable channels for brain activity detection in 0~1, 0~3, 0~5, and 0~10 second time windows. The average accuracies (i.e., 82.7, 89.4, 93.7, and 97.2% in the 0~1, 0~3, 0~5, and 0~10 sec time windows, respectively) using DNNs from the right dorsolateral prefrontal cortex were obtained. The CNN architecture resulted in an average accuracy of 99.3%, showing the model to be capable of differentiating the images of drowsy/non-drowsy states. The proposed approach is promising for detecting drowsiness and in accessing the brain location for a passive BCI.
Highlights
Drowsiness has been one of the leading causes of injuries or fatalities in car accidents [1]
The signals were segmented into smaller time windows; for example, a 30 min signal, if segmented into a 0∼1 sec time window, will result in a total of 1,800 values (30 × 60)
There will be 600, 360, and 180 total values for 0∼3 sec, 0∼5 sec, and 0∼10 sec windows. After these segments were acquired, they were passed through the Deep neural networks (DNN) with the model trained to classify between drowsy and non-drowsy states
Summary
Drowsiness has been one of the leading causes of injuries or fatalities in car accidents [1]. Previous research indicates that 10∼30% of car crashes occur owing to driver fatigue or drowsiness [2], [3], which are caused mostly by sleep deprivation [4], intoxication, drug abuse, heat exposure, or/and alcohol [5]. Multiple methods have been devised for the detection of drowsiness. These methods include recording behavior [7], driver physiological signal measurement [8], and vehicle-based performance evaluation [9]. Among these methods, the bio-signal measurement approach showed the
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