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Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis

Sensors (Basel, Switzerland), 2022-10, Vol.22 (19), p.7596 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2022 by the authors. 2022 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s22197596 ;PMID: 36236694

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Title:
Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis
Author: Antony, Mary Judith ; Sankaralingam, Baghavathi Priya ; Mahendran, Rakesh Kumar ; Gardezi, Akber Abid ; Shafiq, Muhammad ; Choi, Jin-Ghoo ; Hamam, Habib
Subjects: Accuracy ; adaptive classifier ; Chemical industry ; Classification ; Classifiers ; common spatial pattern ; Convulsions & seizures ; Datasets ; Discriminant analysis ; electroencephalogram ; Electroencephalography ; Feature extraction ; Feature selection ; Herbicides ; Independent component analysis ; Information sources ; Methods ; online recursive independent component analysis ; Pesticides industry ; Spatial filtering ; support vector machine ; Support vector machines ; User training ; Wavelet transforms
Is Part Of: Sensors (Basel, Switzerland), 2022-10, Vol.22 (19), p.7596
Description: An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain—computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes’ motor images, namely Dataset 2a of BCI Competition IV.
Publisher: Basel: MDPI AG
Language: English
Identifier: ISSN: 1424-8220
EISSN: 1424-8220
DOI: 10.3390/s22197596
PMID: 36236694
Source: Geneva Foundation Free Medical Journals at publisher websites
AUTh Library subscriptions: ProQuest Central
PubMed Central
ROAD: Directory of Open Access Scholarly Resources
DOAJ Directory of Open Access Journals

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Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis

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