Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN
This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024032298 |
| _version_ | 1797247805614456832 |
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| author | Srinath Akuthota RajKumar K Janapati Ravichander |
| author_facet | Srinath Akuthota RajKumar K Janapati Ravichander |
| author_sort | Srinath Akuthota |
| collection | DOAJ |
| description | This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks.The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process.The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%. |
| first_indexed | 2024-04-24T20:04:32Z |
| format | Article |
| id | doaj.art-5d463a6f836b4933aa8ca5a7ac3a62b8 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T20:04:32Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-5d463a6f836b4933aa8ca5a7ac3a62b82024-03-24T06:59:29ZengElsevierHeliyon2405-84402024-04-01107e27198Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNNSrinath Akuthota0RajKumar K1Janapati Ravichander2Corresponding author.; Department of Electronics & Communication Engineering, SR University, Warangal-506371, Telangana, IndiaDepartment of Electronics & Communication Engineering, SR University, Warangal-506371, Telangana, IndiaDepartment of Electronics & Communication Engineering, SR University, Warangal-506371, Telangana, IndiaThis paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks.The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process.The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.http://www.sciencedirect.com/science/article/pii/S2405844024032298BCIBrain computer interfaceEEGElectro Enchephalo GramICAIndependent component analysis |
| spellingShingle | Srinath Akuthota RajKumar K Janapati Ravichander Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN Heliyon BCI Brain computer interface EEG Electro Enchephalo Gram ICA Independent component analysis |
| title | Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN |
| title_full | Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN |
| title_fullStr | Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN |
| title_full_unstemmed | Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN |
| title_short | Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN |
| title_sort | artifact removal and motor imagery classification in eeg using advanced algorithms and modified dnn |
| topic | BCI Brain computer interface EEG Electro Enchephalo Gram ICA Independent component analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024032298 |
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