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

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

*此作品的通信作者

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

原文English
期刊International Journal of Fuzzy Systems
DOIs
出版狀態Accepted/In press - 2022

指紋

深入研究「Toward CNN-Based Motor-Imagery EEG Classification with Fuzzy Fusion」主題。共同形成了獨特的指紋。

引用此