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Anxiety, nervousness and stress are daily challenges for mankind. These challenges are very severe specifically for students of the age group between years 14 to 25. Therefore it very important to develop the simplest, low cost, accurate and handy process which will be helpful for the society to gauge the anxiety levels and take necessary corrective actions to avoid health and psychological issues. It is of extreme importance to have regular checks on change in behavior and to ensure correct emotion analysis and take the corrective action. Article elaborates unique feature extraction method called as “linear formulation of differential entropy”. With this method we have significantly reduced number of (Electroencephalography) EEG channels for emotion detection. This work has discovered new approach in neuroscience. It’s proved that, single-channel EEG contains sufficient information for emotion recognition. The performance of the newly proposed technique is based on the “Database for Emotion Analysis using Physiological Signals” (DEAP) benchmark database using single channel FP1, prefrontal channel [FP1, AF3, FP2, AF4], and all 32 channel. Bidirectional long short term memory (BiLSTM) is used as classifier. The performance shows that, accuracy achieved using proposed method of single channel (FP1) is almost equivalent to the accuracy of 32 channels.

Optimal Number of Electrode Selection for EEG Based Emotion Recognition using Linear Formulation of Differential Entropy