Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach
Brain-computer interface (BCI) technologies significantly facilitate the interaction between physically impaired people and their surroundings. In electroencephalography (EEG) based BCIs, a variety of physiological responses including P300, motor imagery, movement-related potential, steady-state vi...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTHM
2020
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/30690/ http://umpir.ump.edu.my/id/eprint/30690/1/Five-Class%20SSVEP%20Response%20Detection%20using%20Common.pdf |
| _version_ | 1848823576731320320 |
|---|---|
| author | Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Bari, Bifta Sama |
| author_facet | Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Bari, Bifta Sama |
| author_sort | Rashid, Mamunur |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Brain-computer interface (BCI) technologies significantly facilitate the interaction between physically impaired people and their surroundings. In electroencephalography (EEG) based BCIs, a variety of physiological
responses including P300, motor imagery, movement-related potential, steady-state visual evoked potential (SSVEP) and slow cortical potential have been utilized. Because of the superior signal-to-noise ratio (SNR) together with quicker information transfer rate (ITR), the intentness of SSVEP-based BCIs is progressing significantly. This paper represents the feature extraction and classification frameworks to detect five classes EEG-SSVEP responses. The common-spatial pattern (CSP) has been employed to extract the features from SSVEP responses and these features have been classified through the support vector machine (SVM). The proposed architecture has achieved the highest classification accuracy of 88.3%. The experimental result proves that the proposed architecture could be utilized for the detection of SSVEP responses to develop any BCI applications.
Keywords: EEG, BCI, SSVEP, CSP, SVM, Machine Learning |
| first_indexed | 2025-11-15T02:59:20Z |
| format | Article |
| id | ump-30690 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:59:20Z |
| publishDate | 2020 |
| publisher | Penerbit UTHM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-306902021-02-18T08:47:41Z http://umpir.ump.edu.my/id/eprint/30690/ Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Bari, Bifta Sama TK Electrical engineering. Electronics Nuclear engineering Brain-computer interface (BCI) technologies significantly facilitate the interaction between physically impaired people and their surroundings. In electroencephalography (EEG) based BCIs, a variety of physiological responses including P300, motor imagery, movement-related potential, steady-state visual evoked potential (SSVEP) and slow cortical potential have been utilized. Because of the superior signal-to-noise ratio (SNR) together with quicker information transfer rate (ITR), the intentness of SSVEP-based BCIs is progressing significantly. This paper represents the feature extraction and classification frameworks to detect five classes EEG-SSVEP responses. The common-spatial pattern (CSP) has been employed to extract the features from SSVEP responses and these features have been classified through the support vector machine (SVM). The proposed architecture has achieved the highest classification accuracy of 88.3%. The experimental result proves that the proposed architecture could be utilized for the detection of SSVEP responses to develop any BCI applications. Keywords: EEG, BCI, SSVEP, CSP, SVM, Machine Learning Penerbit UTHM 2020-07-30 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/30690/1/Five-Class%20SSVEP%20Response%20Detection%20using%20Common.pdf Rashid, Mamunur and Norizam, Sulaiman and Mahfuzah, Mustafa and Bari, Bifta Sama (2020) Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach. International Journal of Integrated Engineering, 12 (6). pp. 165-173. ISSN 2229-838X (Print); 2600-7916 (Online). (Published) https://doi.org/10.30880/ijie.2020.12.06.019 10.30880/ijie.2020.12.06.019 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Rashid, Mamunur Norizam, Sulaiman Mahfuzah, Mustafa Bari, Bifta Sama Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach |
| title | Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach |
| title_full | Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach |
| title_fullStr | Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach |
| title_full_unstemmed | Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach |
| title_short | Five-Class SSVEP Response Detection using Common Spatial Pattern (CSP)-SVM Approach |
| title_sort | five-class ssvep response detection using common spatial pattern (csp)-svm approach |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/30690/ http://umpir.ump.edu.my/id/eprint/30690/ http://umpir.ump.edu.my/id/eprint/30690/ http://umpir.ump.edu.my/id/eprint/30690/1/Five-Class%20SSVEP%20Response%20Detection%20using%20Common.pdf |