Toward CNN-Based Motor-Imagery EEG Classification with Fuzzy Fusion

Jian Xue Huang*, Chia Ying Hsieh, Ya Lin Huang, Chun Shu Wei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Recently, decoding human electroencephalographic (EEG) data using convolutional neural network (CNN) has driven the state-of-the-art recognition of motor-imagery EEG patterns for brain–computer interfacing (BCI). While a variety of CNN models have been used to classify motor-imagery EEG data, it is unclear if aggregating an ensemble of heterogeneous CNN models could further enhance the classification performance. To integrate the outputs of ensemble classifiers, this work utilizes fuzzy integral with particle swarm optimization (PSO) to estimate optimal confidence levels assigned to classifiers. The proposed framework aggregates CNN classifiers and fuzzy integral with PSO, achieving robust performance in single-trial classification of motor-imagery EEG data across various CNN model training schemes depending on the scenarios of BCI usage. This proof-of-concept study demonstrates the feasibility of applying fuzzy fusion techniques to enhance CNN-based EEG decoding and benefit practical applications of BCI.

Original languageEnglish
JournalInternational Journal of Fuzzy Systems
DOIs
StateAccepted/In press - 2022

Keywords

  • Brain–computer interface (BCI)
  • Convolutional neural network (CNN)
  • Electroencephalography (EEG)
  • Fuzzy integral
  • Motor imagery (MI)
  • Particle swarm optimization (PSO)

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