An Efficient K-NN Approach for Automatic Drowsiness Detection Using Single-Channel EEG Recording

Drowsy driving is a major cause of many traffic accidents. The aim of this work is to develop an automatic drowsiness detection system using an efficient k-nearest neighbors (K-NN) algorithm. First, the distribution of power in time-frequency space was obtained using short-time Fourier transform (STFT) and then, the mean value of power during time-segments of 0.5 second was calculated for each EEG subband. In addition, standard deviation (SD) and Shanon entropy related to each time-segment were computed from time-domain. Finally, 52 features were extracted. Random forest algorithm was applied over the extracted data, aiming to choose the most informative subset of features. A total of 11 features were selected in order to classify drowsiness and alertness. Kd-trees was used as the nearest neighbors search algorithm so as to have a fast classifier. Our experimental results show that drowsiness can be classified efficiently with 91% accuracy using the methods and materials proposed in this paper.