Recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review.
The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and medical fields drew our attention to perform a detailed analysis. However, the problem is ill-posed as these signals...
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2023
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Online Access: | http://eprints.utm.my/104898/1/NehaSharmaManojSharmaAmitSinghal2023_RecentTrendsinEEGBasedMotorImagerySignal.pdf http://eprints.utm.my/104898/ http://dx.doi.org/10.1109/ACCESS.2023.3299497 |
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my.utm.1048982024-03-25T09:27:30Z http://eprints.utm.my/104898/ Recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review. Sharma, Neha Sharma, Manoj Singhal, Amit Vyas, Ritesh Malik, Hasmat Afthanorhan, Asyraf Hossaini, Mohammad Asef TK Electrical engineering. Electronics Nuclear engineering The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and medical fields drew our attention to perform a detailed analysis. However, the problem is ill-posed as these signals are highly nonlinear, unpredictable, and noisy, hence making it exceedingly hard to be analyzed adequately. This paper provides a first-of-its-kind comprehensive review of conventional signal processing and deep learning techniques for BCI MI signal analysis. The review comprises extensive works carried out in the domain in the recent past, highlighting the current challenges of the problem. A new categorization of the existing approaches has been presented for better clarification. An all-inclusive description of the signal processing techniques has been corroborated by relevant works in the area. Moreover, architectures of various standard deep learning algorithms along with their merits and demerits are also explicated to assist the readers. The tabular representations of the numerical results are also readily provided. This work also presents the open research problems and future directions. Institute of Electrical and Electronics Engineers Inc. 2023-07-27 Article PeerReviewed application/pdf en http://eprints.utm.my/104898/1/NehaSharmaManojSharmaAmitSinghal2023_RecentTrendsinEEGBasedMotorImagerySignal.pdf Sharma, Neha and Sharma, Manoj and Singhal, Amit and Vyas, Ritesh and Malik, Hasmat and Afthanorhan, Asyraf and Hossaini, Mohammad Asef (2023) Recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review. IEEE Access, 11 . pp. 80518-80542. ISSN 2169-3536 http://dx.doi.org/10.1109/ACCESS.2023.3299497 DOI: 10.1109/ACCESS.2023.3299497 |
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TK Electrical engineering. Electronics Nuclear engineering Sharma, Neha Sharma, Manoj Singhal, Amit Vyas, Ritesh Malik, Hasmat Afthanorhan, Asyraf Hossaini, Mohammad Asef Recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review. |
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The electroencephalogram (EEG) motor imagery (MI) signals are the widespread paradigms in the brain-computer interface (BCI). Its significant applications in the gaming, robotics, and medical fields drew our attention to perform a detailed analysis. However, the problem is ill-posed as these signals are highly nonlinear, unpredictable, and noisy, hence making it exceedingly hard to be analyzed adequately. This paper provides a first-of-its-kind comprehensive review of conventional signal processing and deep learning techniques for BCI MI signal analysis. The review comprises extensive works carried out in the domain in the recent past, highlighting the current challenges of the problem. A new categorization of the existing approaches has been presented for better clarification. An all-inclusive description of the signal processing techniques has been corroborated by relevant works in the area. Moreover, architectures of various standard deep learning algorithms along with their merits and demerits are also explicated to assist the readers. The tabular representations of the numerical results are also readily provided. This work also presents the open research problems and future directions. |
format |
Article |
author |
Sharma, Neha Sharma, Manoj Singhal, Amit Vyas, Ritesh Malik, Hasmat Afthanorhan, Asyraf Hossaini, Mohammad Asef |
author_facet |
Sharma, Neha Sharma, Manoj Singhal, Amit Vyas, Ritesh Malik, Hasmat Afthanorhan, Asyraf Hossaini, Mohammad Asef |
author_sort |
Sharma, Neha |
title |
Recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review. |
title_short |
Recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review. |
title_full |
Recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review. |
title_fullStr |
Recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review. |
title_full_unstemmed |
Recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review. |
title_sort |
recent trends in eeg-based motor imagery signal analysis and recognition: a comprehensive review. |
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Institute of Electrical and Electronics Engineers Inc. |
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2023 |
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http://eprints.utm.my/104898/1/NehaSharmaManojSharmaAmitSinghal2023_RecentTrendsinEEGBasedMotorImagerySignal.pdf http://eprints.utm.my/104898/ http://dx.doi.org/10.1109/ACCESS.2023.3299497 |
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