Improving Multi-Class Motor Imagery EEG Classification Using Overlapping Sliding Window and Deep Learning Model
Abstract
:1. Introduction
2. Related Work
2.1. Feature Extraction and Classification Techniques
2.2. Channel Selection Approach
3. Improving Multi-Class MI Classification
3.1. Prepocessing
3.2. LSTM-Based FBCSP with Overlapped Band
3.3. LSTM Based FBCSP with Overlapped Band Applying Channel Selection
4. Experimental Results
4.1. Dataset and Experimental Environment
4.2. Experimental Evaluation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Hwang, J.; Park, S.; Chi, J. Improving Multi-Class Motor Imagery EEG Classification Using Overlapping Sliding Window and Deep Learning Model. Electronics 2023, 12, 1186. https://doi.org/10.3390/electronics12051186
Hwang J, Park S, Chi J. Improving Multi-Class Motor Imagery EEG Classification Using Overlapping Sliding Window and Deep Learning Model. Electronics. 2023; 12(5):1186. https://doi.org/10.3390/electronics12051186
Chicago/Turabian StyleHwang, Jeonghee, Soyoung Park, and Jeonghee Chi. 2023. "Improving Multi-Class Motor Imagery EEG Classification Using Overlapping Sliding Window and Deep Learning Model" Electronics 12, no. 5: 1186. https://doi.org/10.3390/electronics12051186
APA StyleHwang, J., Park, S., & Chi, J. (2023). Improving Multi-Class Motor Imagery EEG Classification Using Overlapping Sliding Window and Deep Learning Model. Electronics, 12(5), 1186. https://doi.org/10.3390/electronics12051186