Online EEG artifact removal for BCI applications by adaptive spatial filtering
Roberto Guarnieri1, Marco Marino1,2, Federico Barban1,3
1Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium.
Journal of Neural Engineering
|June 29, 2018
Summary
This study introduces a new online method for removing artifacts from electroencephalography (EEG) data to improve brain-computer interface (BCI) performance. The BSS-REG technique effectively filters noise while preserving neural signals, enhancing BCI applications.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Signal Processing
Background:
- Brain-computer interface (BCI) performance is significantly impacted by artifacts in electroencephalography (EEG) recordings.
- Effective artifact removal is crucial for reliable BCI operation.
Purpose of the Study:
- To develop and evaluate a novel online EEG artifact removal method for BCI applications.
- To combine blind source separation (BSS) and regression (REG) analysis for enhanced artifact attenuation.
Main Methods:
- The developed BSS-REG method utilizes a calibration dataset for initial spatial filter setup via BSS.
- Online artifact removal is achieved through dynamic adjustment of the spatial filter using linear regression during experiments.
- The method is designed for low computational requirements, suitable for both low-density and high-density EEG data.
Main Results:
- The BSS-REG method successfully attenuates various artifacts, including ocular and muscular.
- True neural activity is preserved during artifact removal.
- The method demonstrates applicability across different EEG densities due to its computational efficiency.
Conclusions:
- The BSS-REG method offers an effective solution for online EEG artifact removal in BCI.
- This technique can facilitate the development of advanced BCI applications, such as high-density source-based neurofeedback and closed-loop neuromodulation.
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