研究目的
Developing a fully automated and online artifact removal method for EEG to improve BCI performance by removing artifacts without the need for additional signals.
研究成果
The FORCe method effectively removes a wide range of artifacts from EEG signals during both offline and online BCI operation, improving signal quality and BCI performance without the need for additional reference signals. It outperforms state-of-the-art methods LAMIC and FASTER in artifact removal efficacy.
研究不足
The method is currently tested on datasets with 16 EEG channels and may scale linearly with more channels, potentially limiting online operation with higher channel counts. It also requires further validation during other types of BCI operation, such as event-related potential (ERP) based BCI control.
1：Experimental Design and Method Selection:
The FORCe method combines wavelet decomposition, independent component analysis (ICA), and thresholding to remove artifacts from EEG signals.
2：Sample Selection and Data Sources:
EEG recordings from 13 BCI participants with cerebral palsy and three healthy participants.
3：List of Experimental Equipment and Materials:
GAMMAsys active electrode system (g.tec, Austria) for EEG recording.
4：Experimental Procedures and Operational Workflow:
EEG signals are processed in 1 s windows, decomposed using wavelets, ICA is applied to approximation coefficients, artifact-contaminated ICs are identified and removed, and the cleaned EEG is reconstructed.
5：Data Analysis Methods:
Signal quality index (SQI), power spectral densities, event-related de/synchronization (ERD/S) spectra, and classification accuracies are used to evaluate the method's efficacy.
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