Wijaya, Adi and Adji, Teguh Bharata and Setiawan, Noor Akhmad (2021) Logistic Regression based Feature Selection and Two-Stage Detection for EEG based Motor Imagery Classification. International Journal of Intelligent Engineering and Systems, 14 (1). 134 – 146. ISSN 2185310X
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Electroencephalogram (EEG) based motor imagery (MI) classification requires efficient feature extraction and consistent accuracy for reliable brain-computer interface (BCI) systems. Achieving consistent accuracy in EEGMI classification is still big challenge according to the nature of EEG signal which is subject dependent. To address this problem, we propose a feature selection scheme based on Logistic Regression (LRFS) and two-stage detection (TSD) in channel instantiation approach. In TSD scheme, Linear Discriminant Analysis was utilized in first-stage detection; while Gradient Boosted Tree and k-Nearest Neighbor in second-stage detection. To evaluate the proposed method, two publicly available datasets, BCI competition III-Dataset IVa and BCI competition IV-Dataset 2a, were used. Experimental results show that the proposed method yielded excellent accuracy for both datasets with 95.21 and 94.83, respectively. These results indicated that the proposed method has consistent accuracy and is promising for reliable BCI systems. © 2021. International Journal of Intelligent Engineering and Systems. All Rights Reserved.
Item Type: | Article |
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Additional Information: | Cited by: 5; All Open Access, Bronze Open Access |
Uncontrolled Keywords: | Acetic acid; Batch reactors; Catalysts; Epoxidation; Molar ratio; pH; Vegetable oils; Velocity; Advanced materials; Conversion to oxirane; Iodine value; Molar ratio; Non-edible vegetable oil; Oxiranes; Research and development; Stirring velocity; The molar ratio of acetic acid to unsaturated fatty acid; Tung oil; Unsaturated fatty acids |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electrical and Information Technology Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 05 Oct 2024 12:31 |
Last Modified: | 05 Oct 2024 12:31 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8795 |