The EEG offers an accessible window into the mind by providing synaptic activity that is moderate to strongly link with the brain state. These electrical activities consist of various frequencies: delta, theta, alpha, beta, and gamma rhythms. The brain responds in different frequencies for different brain activities. Studying EEG in their different frequency ranges separately is crucial to precisely understanding different brain states. In the present work, we have studied the significance of (i) Tsallis entropy, (ii) different EEG rhythms, and (iii) different brain regions for human emotion study. SEED dataset has been used, which consists of the positive, neutral, and negative emotions. Channels F2, C5, T7, FT7, and FC6 of the anterior part of the brain are considered for our study. Different EEG rhythms have been extracted using an FIR filter. The mean and variance of Tsallis entropy and the Probability Distribution Function of the data are then computed and analyzed. With our analysis, it is found that the Tsallis entropy is effective enough to be employed as a feature in a framework for recognizing human emotions. Findings from a rhythmic standpoint indicate that beta and gamma rhythms are best for studying emotions. Also, all the selected channels from the anterior part of the brain show significant differences in emotions in beta and gamma bands. This suggests that when the right frequency is considered, the brain's anterior part is pivotal in emotion study. Hence, a classifier can be built using Tsallis entropy as a feature considering right frequency bands and the anterior region of the brain.

Biomedical Signal Processing, EEG data analysis, EEG Rhythms, Emotion Recognition, Tsallis Entropy, Tsallis Statistics.