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Improving EEG Decoding via Clustering-Based Multitask Feature Learning
- Source :
- IEEE Transactions on Neural Networks and Learning Systems. 33:3587-3597
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- Accurate electroencephalogram (EEG) pattern decoding for specific mental tasks is one of the key steps for the development of brain-computer interface (BCI), which is quite challenging due to the considerably low signal-to-noise ratio of EEG collected at the brain scalp. Machine learning provides a promising technique to optimize EEG patterns toward better decoding accuracy. However, existing algorithms do not effectively explore the underlying data structure capturing the true EEG sample distribution and, hence, can only yield a suboptimal decoding accuracy. To uncover the intrinsic distribution structure of EEG data, we propose a clustering-based multitask feature learning algorithm for improved EEG pattern decoding. Specifically, we perform affinity propagation-based clustering to explore the subclasses (i.e., clusters) in each of the original classes and then assign each subclass a unique label based on a one-versus-all encoding strategy. With the encoded label matrix, we devise a novel multitask learning algorithm by exploiting the subclass relationship to jointly optimize the EEG pattern features from the uncovered subclasses. We then train a linear support vector machine with the optimized features for EEG pattern decoding. Extensive experimental studies are conducted on three EEG data sets to validate the effectiveness of our algorithm in comparison with other state-of-the-art approaches. The improved experimental results demonstrate the outstanding superiority of our algorithm, suggesting its prominent performance for EEG pattern decoding in BCI applications.
- Subjects :
- Computer Networks and Communications
Computer science
Physics::Medical Physics
Multi-task learning
Computer Science::Human-Computer Interaction
02 engineering and technology
Electroencephalography
Machine Learning
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
medicine
Cluster Analysis
Cluster analysis
Quantitative Biology::Neurons and Cognition
medicine.diagnostic_test
business.industry
Pattern recognition
Computer Science Applications
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Brain-Computer Interfaces
Affinity propagation
020201 artificial intelligence & image processing
Neural Networks, Computer
Artificial intelligence
business
Feature learning
Software
Decoding methods
Subjects
Details
- ISSN :
- 21622388 and 2162237X
- Volume :
- 33
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks and Learning Systems
- Accession number :
- edsair.doi.dedup.....890d913e67d85f48cec740d45cf8a94e