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A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network

Rui Li^1,2, Di Liu¹, Zhijun Li¹

¹School of Mechanical and Instrumental Engineering, Xi'an University of Technology, Xi'an, China.

Frontiers in Neuroscience

|September 30, 2022

Summary

This study introduces an improved facial-expression-based brain-computer interface (FE-BCI) system using a CNN-GA model. The novel approach enhances classification accuracy for rehabilitation applications.

Keywords:

EEG brain computer interface convolutional neural network (CNN)facial expression genetic algorithm

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Area of Science:

Neuroscience
Rehabilitation Engineering
Artificial Intelligence

Background:

Brain-computer interfaces (BCI) are used in rehabilitation.
Facial-expression-based BCI (FE-BCI) offers an alternative to reduce user fatigue and improve accuracy.
Current machine learning algorithms struggle to identify relevant electroencephalogram (EEG) features for FE-BCI.

Purpose of the Study:

To propose an improved classification method for FE-BCI systems.
To enhance the identification of relevant EEG features for better classification accuracy.
To develop a robust FE-BCI system for real-time applications.

Main Methods:

Utilized a convolutional neural network (CNN) for feature extraction and classification.
Employed a genetic algorithm (GA) for hyperparameter optimization to select the most relevant classification parameters.
Integrated CNN and GA into a hybrid CNN-GA model for FE-BCI.

Main Results:

The proposed CNN-GA model achieved an average accuracy of 89.21 ± 3.79% across subjects.
The highest recorded accuracy reached 97.71 ± 2.07%.
Offline and online experiments demonstrated the superior performance of the improved FE-BCI system over traditional methods.

Conclusions:

The developed CNN-GA model significantly improves classification accuracy in FE-BCI systems.
The enhanced FE-BCI system demonstrates superior performance compared to existing approaches.
This method offers a promising advancement for BCI applications in rehabilitation and beyond.