Emotion classification using single-channel scalp-EEG recording.

Several studies have found evidence for corticolimbic Theta electroencephalographic (EEG) oscillation in the neural processing of visual stimuli perceived as fear or threatening scene. Recent studies showed that neural oscillations' patterns in Theta, Alpha, Beta and Gamma sub-bands play a main role in brain's emotional processing. The main goal of this study is to classify two different emotional states by means of EEG data recorded through a single-electrode EEG headset.

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.