Feature Reconstruction Based Channel Selection for Emotion Recognition Using EEG

James Msonda, Zhimin He, Chuan Lu

Research output: Contribution to Book/ReportChapterpeer-review

Abstract

There has been a surge in the use of consumer grade wearable Electroencephalogram (EEG) devices for emotion discrimination tasks in various research laboratories in recent times. The obvious advantages carried by these compared to medical grade equipment are reduced costs and portability, which enable monitoring for a longer term and in more natural environment. Different manufacturers of consumer grade EEG devices place the electrodes at different locations. In this paper we present a novel method for determining locations of the fewest electrodes with the most emotion valence discriminative power. It starts with feature generation and selection for identifying positional features for the classification task, followed by channel selection that minimizes the feature reconstruction error. To evaluate the proposed methods, benchmarking analysis was done using leave out one subject cross validation with various machine learning models, using three public datasets. Results show with 8 electrodes AUC scores of 0.78, 0.8 and 0.67 are obtained for AMIGOS, DREAMER and DEAP datasets, respectively on emotion valence classification task. It is further observed that out of the best 8 channels selected, frontal (F8), parietal (P7), and temporal (T8 and T7) are common brain areas which are active during emotion processing across all the three datasets.
Original languageEnglish
Title of host publication2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
PublisherIEEE
Number of pages7
ISBN (Electronic)978-1-6654-2897-2
ISBN (Print)978-1-6654-2898-9
DOIs
Publication statusPublished - 18 Jan 2022

Publication series

NameIEEE Signal Processing in Medicine and Biology Symposium (SPMB)
PublisherIEEE
ISSN (Print)2372-7241
ISSN (Electronic)2473-716X

Bibliographical note

We express our gratitude to Super Computing Wales (SCW) for providing computing resources used in this research.

Keywords

  • electrodes
  • measurement uncertainty
  • laboratories
  • signal processing
  • machine learning
  • electroencephalography

Fingerprint

Dive into the research topics of 'Feature Reconstruction Based Channel Selection for Emotion Recognition Using EEG'. Together they form a unique fingerprint.

Cite this