Geometric subspace methods and time-delay embedding for EEG artifact removal and classification
Charles W Anderson1, James N Knight, Tim O'Connor
1Department of Computer Science, Colorado State University, Fort Collins, CO 80523, USA. anderson@cs.colostate.edu
Summary
Generalized singular-value decomposition effectively separates electroencephalogram (EEG) signals, removing artifacts and classifying mental tasks. This method enhances EEG analysis for improved brain-computer interface applications.
Area of Science:
- Neuroscience
- Signal Processing
- Machine Learning
Background:
- Multichannel electroencephalogram (EEG) data often contains artifacts that obscure neural activity.
- Accurate artifact removal and task classification are crucial for brain-computer interfaces (BCIs).
Purpose of the Study:
- To develop and validate a novel method for artifact removal in EEG signals.
- To classify distinct mental tasks using processed EEG data.
Main Methods:
- Generalized singular-value decomposition (GSVD) was employed to decompose EEG signals into components optimizing a signal-to-noise ratio, facilitating artifact filtering.
- Short-time principal components analysis (PCA) on time-delay embedded EEG data was used for feature extraction.
- Committees of decision trees were utilized for classifying five distinct mental tasks.
Main Results:
- Demonstrated effective filtering of various common EEG artifacts using the GSVD-based approach.
- Achieved successful classification of five different mental tasks based on the processed EEG data.
Conclusions:
- The proposed GSVD and PCA-based method provides an effective strategy for EEG artifact removal and mental task classification.
- This approach holds promise for advancing the performance and reliability of BCIs.
