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Feature optimization based on improved novel global harmony search algorithm for motor imagery electroencephalogram

Bin Shi¹, Xiaokai Chen², Zan Yue^3,4

¹Xi'an Research Institute of High-Technology, Xi'an, Shaanxi, China.

Frontiers in Computational Neuroscience

|January 2, 2023

Summary

This summary is machine-generated.

Optimizing frequency band and time interval for Common Spatial Pattern (CSP) feature extraction using an improved novel global harmony search (INGHS) algorithm enhances electroencephalogram (EEG) decoding accuracy for brain-computer interfaces (BCI). This method shows improved performance and running time compared to other optimization algorithms.

Keywords:

brain-computer interface (BCI)common spatial pattern (CSP)electroencephalogram (EEG)frequency band improved novel global harmony search (INGHS)time interval

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Area of Science:

Neuroscience
Biomedical Engineering
Computer Science

Background:

Decoding electroencephalogram (EEG) patterns for mental tasks is vital for brain-computer interface (BCI) development.
Common Spatial Pattern (CSP) feature extraction from motor imagery EEG is sensitive to frequency band and time interval selection.
Optimizing these parameters is key for effective feature extraction and accurate EEG decoding.

Purpose of the Study:

To propose an improved novel global harmony search (INGHS) algorithm for optimizing frequency-time parameters in CSP feature extraction.
To enhance the accuracy of EEG decoding for motor imagery-based BCI systems.

Main Methods:

Applied the INGHS algorithm to identify optimal frequency bands and temporal intervals for CSP.
Utilized linear discriminant analysis and support vector machine for EEG pattern decoding.
Conducted extensive experiments on three EEG datasets to validate the proposed method.

Main Results:

The INGHS method achieved slightly better average test accuracy than Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms.
The INGHS algorithm demonstrated superior running time compared to PSO and ABC.
Optimal frequency band and time interval selection significantly improved decoding accuracy over traditional CSP methods.

Conclusions:

The proposed INGHS algorithm effectively optimizes frequency-time parameters for CSP feature extraction.
This approach significantly enhances EEG decoding accuracy in motor imagery-based BCI.
The method holds potential for improving the overall performance of BCI systems.