# Optimal EEG Electrode Set for Emotion Recognition From Brain Signals: An Empirical Quest

arXiv: https://arxiv.org/abs/2311.17204 · HTML: https://mlsanity.com/paper/2311.17204/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 33 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [Enhanced Pediatric Dental Segmentation Using a Custom SegUNet with VGG19 Backbone on Panoramic Radiographs](https://mlsanity.com/paper/2503.06321/) (2025)
- [Assessing Region-Level EEG Contributions to Cognitive Workload Prediction](https://mlsanity.com/paper/2606.02598/) (2026)
- [Automating Learner Assessment: Benchmarking Machine Learning and Deep Learning Models for EEG-Based Familiarity Prediction](https://mlsanity.com/paper/2608.16541/) (2026)

## Abstract (arXiv)

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.