Toward the enhancement of affective brain-computer interfaces using dependence within EEG series

Yu Pei, Shaokai Zhao, Liang Xie, Bowen Ji, Zhiguo Luo, Chuang Ma, Kun Gao, Xiaomin Wang, Tingyu Sheng, Ye Yan, Erwei Yin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In recent years, electroencephalogram (EEG)-based affective brain-computer interfaces (aBCI) has made remarkable advances. Objective. However, a subtle but crucial problem caused by the sliding window method has long been overlooked, which is the serious quantity mismatch between stimuli and short-term EEG frames. This may be an important factor limiting the performance of aBCIs. Approach. We refer to this mismatch as the quantity-independence imbalance (Q/I imbalance) and we propose the weak independence hypothesis to explain the mismatch. To validate this hypothesis and explore the effects of the Q/I imbalance on short-term EEG frames, we design four experiments from four perspectives, which are visualization, cross-validation, randomness test, and redundancy test. Main results. Inspired by validation experiments, we propose an inference correction (IC) method to enhancing the emotional predictions by leveraging the majority of the classifier’s outputs. The proposed IC method is evaluated on two datasets involving 60 subjects using both intra-subject and inter-subject validation protocols. Our IC achieves a significant improvement of 14.97% in classification accuracy. Significance. This study promotes the understanding of the time-dependent nature of EEG signals in aBCI.

Original languageEnglish
Article number026038
JournalJournal of Neural Engineering
Volume22
Issue number2
DOIs
StatePublished - 1 Apr 2025

Keywords

  • EEG
  • affective brain-computer interfaces
  • emotion recognition
  • randomness test
  • sliding window method

Fingerprint

Dive into the research topics of 'Toward the enhancement of affective brain-computer interfaces using dependence within EEG series'. Together they form a unique fingerprint.

Cite this