Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification
BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a subject with no available training statistics. Dee...
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MDPI AG
2023-02-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/4/640 |
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| author | Farheen Siddiqui Awwab Mohammad M. Afshar Alam Sameena Naaz Parul Agarwal Shahab Saquib Sohail Dag Øivind Madsen |
| author_facet | Farheen Siddiqui Awwab Mohammad M. Afshar Alam Sameena Naaz Parul Agarwal Shahab Saquib Sohail Dag Øivind Madsen |
| author_sort | Farheen Siddiqui |
| collection | DOAJ |
| description | BACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a subject with no available training statistics. Deep learning frameworks are popular among researchers for analyzing both spatial and time series data, making them well-suited for classifying EEG signals. METHOD. In this paper, a deep neural network model is proposed for mental task classification for an imagined task from EEG signal data. Pre-computed features of EEG signals were obtained after raw EEG signals acquired from the subjects were spatially filtered by applying the Laplacian surface. To handle high-dimensional data, principal component analysis (PCA) was performed which helps in the extraction of most discriminating features from input vectors. RESULT. The proposed model is non-invasive and aims to extract mental task-specific features from EEG data acquired from a particular subject. The training was performed on the average combined Power Spectrum Density (PSD) values of all but one subject. The performance of the proposed model based on a deep neural network (DNN) was evaluated using a benchmark dataset. We achieved 77.62% accuracy. CONCLUSION. The performance and comparison analysis with the related existing works validated that the proposed cross-subject classification framework outperforms the state-of-the-art algorithm in terms of performing an accurate mental task from EEG signals. |
| first_indexed | 2024-03-11T08:56:40Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T08:56:40Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-2ec25f5640db4894ae1fc1f4aa0c982f2023-11-16T20:00:50ZengMDPI AGDiagnostics2075-44182023-02-0113464010.3390/diagnostics13040640Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task ClassificationFarheen Siddiqui0Awwab Mohammad1M. Afshar Alam2Sameena Naaz3Parul Agarwal4Shahab Saquib Sohail5Dag Øivind Madsen6Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, IndiaDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, IndiaDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, IndiaDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, IndiaDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, IndiaDepartment of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi 110062, IndiaDepartment of Business, Marketing and Law, USN School of Business, University of South-Eastern Norway, 3511 Hønefoss, NorwayBACKGROUND. Mental task identification using electroencephalography (EEG) signals is required for patients with limited or no motor movements. A subject-independent mental task classification framework can be applied to identify the mental task of a subject with no available training statistics. Deep learning frameworks are popular among researchers for analyzing both spatial and time series data, making them well-suited for classifying EEG signals. METHOD. In this paper, a deep neural network model is proposed for mental task classification for an imagined task from EEG signal data. Pre-computed features of EEG signals were obtained after raw EEG signals acquired from the subjects were spatially filtered by applying the Laplacian surface. To handle high-dimensional data, principal component analysis (PCA) was performed which helps in the extraction of most discriminating features from input vectors. RESULT. The proposed model is non-invasive and aims to extract mental task-specific features from EEG data acquired from a particular subject. The training was performed on the average combined Power Spectrum Density (PSD) values of all but one subject. The performance of the proposed model based on a deep neural network (DNN) was evaluated using a benchmark dataset. We achieved 77.62% accuracy. CONCLUSION. The performance and comparison analysis with the related existing works validated that the proposed cross-subject classification framework outperforms the state-of-the-art algorithm in terms of performing an accurate mental task from EEG signals.https://www.mdpi.com/2075-4418/13/4/640electroencephalographydeep neural networkprincipal component analysismental taskfeature extraction |
| spellingShingle | Farheen Siddiqui Awwab Mohammad M. Afshar Alam Sameena Naaz Parul Agarwal Shahab Saquib Sohail Dag Øivind Madsen Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification Diagnostics electroencephalography deep neural network principal component analysis mental task feature extraction |
| title | Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification |
| title_full | Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification |
| title_fullStr | Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification |
| title_full_unstemmed | Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification |
| title_short | Deep Neural Network for EEG Signal-Based Subject-Independent Imaginary Mental Task Classification |
| title_sort | deep neural network for eeg signal based subject independent imaginary mental task classification |
| topic | electroencephalography deep neural network principal component analysis mental task feature extraction |
| url | https://www.mdpi.com/2075-4418/13/4/640 |
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