Optimal EEG Electrode Set for Emotion Recognition From Brain Signals: An Empirical Quest
This work addresses the need for efficient electrode selection in EEG-based emotion recognition, which is incremental as it optimizes existing methods for a specific application.
The study tackled the problem of identifying the most effective brain regions for emotion recognition by empirically analyzing EEG electrode contributions, finding that a reduced set of 12 electrodes achieved 95.81% accuracy compared to 97.34% with 32 electrodes, with the frontal lobe being the most important.
The human brain is a complex organ, still completely undiscovered, that controls almost all the parts of the body. Apart from survival, the human brain stimulates emotions. Recent research indicates that brain signals can be very effective for emotion recognition. However, which parts of the brain exhibit most of the emotions is still under-explored. In this study, we empirically analyze the contribution of each part of the brain in exhibiting emotions. We use the DEAP dataset to find the most optimal electrode set which eventually leads to the effective brain part associated with emotions. We use Fast Fourier Transformation for effective feature extraction and a 1D-CNN with residual connection for classification. Though 32 electrodes from the DEAP dataset got an accuracy of 97.34%, only 12 electrodes (F7, P8, O1, F8, C4, T7, PO3, Fp1, Fp2, O2, P3, and Fz) achieve 95.81% accuracy. This study also shows that adding more than 10 electrodes does not improve performance significantly. Moreover, the frontal lobe is the most important for recognizing emotion.