Optimizing the Channel Selection and Classification Accuracy in EEG-Based BCI

By Mahnaz Arvaneh, Cuntai Guan, Kai Keng Ang, Hiok Chai Quek. IEEE Transactions on Biomedical Engineering, 2011

435 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d038a4fd5c7c8a2763041a0e9392fa31dd0e454b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. Shuicheng Yan
2. Cuntai Guan
3. Fathi E. Abd El-Samie
4. Saleh A. Alshebeili
5. Ishtiaq Ahmad
6. Siavash Sakhavi
7. Turky Nayef Alotaiby

In Cognitive Neuroscience

1. Cuntai Guan
2. Shuicheng Yan
3. Siavash Sakhavi
4. Fathi E. Abd El-Samie
5. Saleh A. Alshebeili
6. Ishtiaq Ahmad
7. Turky Nayef Alotaiby
8. Fabien Lotte

In Signal Processing

1. Cuntai Guan
2. Shuicheng Yan
3. Siavash Sakhavi
4. Fabien Lotte
5. Dongrui Wu

Citations in the corpus

• Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks (2018, background): “There have been many other successful attempts to improve the CSP algorithm: Sparse CSP [17] adds a regularization factor on the spatial filter estimation, imposing sparsity on the weights.”
• A review of channel selection algorithms for EEG signal processing (2015, methodology): “Arvaneh et al. [48] proposed an SCSP algorithm for subject-dependent channel selection in BCI systems adopting a filtering approach with a pre-specified subset channel selection scheme.”
• Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review (2021, methodology): “Filter bank CSP (FBCSP) [23] is another extended version of the CSP approach that uses the spatial information in EEG channels, as well as the frequency data in MI-EEG signals.”