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Published on: July 7, 2023
Effect of ocular artifact removal in brain computer interface accuracy.
M Thulasidas1, C Guan, S Ranganatha
1NeuroInformatics, Institute for Infocomm Research, Singapore.
Summary
Removing ocular artifacts significantly impacts brain-computer interface accuracy. This study compares artifact removal algorithms for P300-based word processing, moving beyond subjective visual analysis to objective performance metrics.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Human-Computer Interaction
Background:
- Ocular artifacts contaminate electroencephalography (EEG) signals, particularly the P300 component.
- Accurate artifact removal is crucial for reliable brain-computer interface (BCI) performance.
- Current evaluation of artifact removal methods often relies on subjective visual inspection.
Purpose of the Study:
- To investigate the impact of ocular artifact removal on P300-based word processing.
- To provide an objective comparison of different artifact rectification algorithms.
- To enhance the accuracy and reliability of BCI systems.
Main Methods:
- Utilized a P300-based word-processing BCI application.
- Implemented and evaluated various algorithms for removing ocular artifacts from EEG data.
- Compared algorithm performance based on objective metrics related to BCI accuracy.
Main Results:
- Demonstrated a direct correlation between artifact rectification effectiveness and BCI system accuracy.
- Quantified the performance improvements achieved by different artifact removal techniques.
- Established a basis for objective algorithm selection.
Conclusions:
- Objective comparison of artifact removal algorithms is essential for BCI development.
- Effective ocular artifact removal significantly boosts the performance of P300 spellers.
- This work facilitates the selection of optimal algorithms for improved BCI applications.
