Minimal Electrode EEG for BCI Emotion Detection

Yuxin Li, Hao Fang, Wen Liu*, Chuantong Cheng, Hongda Chen

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Electroencephalography (EEG)-based emotion recognition is a potential research direction in the field of brain-computer interfaces (BCIs). However, its deployment on wearable devices still suffers from the challenges of low accuracy, heavy computation, and complex electrode placement. This study focuses on advancing the efficiency and cost-effectiveness of EEG-based BCIs for emotion recognition. Our approach begins with an investigation of electrode placement in relation to emotion detection, leveraging the SEED dataset to identify an optimal configuration that uses a minimal number of electrodes while maintaining high recognition accuracy. Employing a variety of machine learning and deep learning algorithms, we compare detection accuracy across different electrode combinations. Through these experiments and subsequent analysis, we identify an effective combination of two electrodes, T7 and T8, with the SVM method achieving an impressive 92.8 % accuracy. This finding laid the foundation for the design of our wearable, closed-loop BCI device with EEG-based emotion recognition capability.

Original languageEnglish
Title of host publication2024 4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages379-383
Number of pages5
ISBN (Electronic)9798350394375
DOIs
Publication statusPublished - 2024
Event4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024 - Hybrid, Guangzhou, China
Duration: 19 Jan 202421 Jan 2024

Publication series

Name2024 4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

Conference

Conference4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024
Country/TerritoryChina
CityHybrid, Guangzhou
Period19/01/2421/01/24

Keywords

  • Brain-Computer Interfaces
  • Channel Selection
  • Deep Learning
  • Electroencephalography
  • Emotion Recognition
  • Machine Learning

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