A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification
In the realm of Brain-Computer Interface (BCI) research, the precise decoding of motor imagery electroencephalogram (MI-EEG) signals is pivotal for the realization of systems that can be seamlessly integrated into practical applications, enhancing the autonomy of individuals with mobility impairment...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10802872/ |
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| author | Neha Sharma Manoj Sharma Amit Singhal Nuzhat Fatema Vinay Kumar Jadoun Hasmat Malik Asyraf Afthanorhan |
| author_facet | Neha Sharma Manoj Sharma Amit Singhal Nuzhat Fatema Vinay Kumar Jadoun Hasmat Malik Asyraf Afthanorhan |
| author_sort | Neha Sharma |
| collection | DOAJ |
| description | In the realm of Brain-Computer Interface (BCI) research, the precise decoding of motor imagery electroencephalogram (MI-EEG) signals is pivotal for the realization of systems that can be seamlessly integrated into practical applications, enhancing the autonomy of individuals with mobility impairments. This study presents an enhanced method for the precise recognition of MI tasks using EEG data, to facilitate more intuitive interactions between individuals with mobility challenges and their environment. The core challenge addressed herein is the development of robust algorithms that enable the accurate identification of MI tasks, thereby empowering individuals with mobility impairments to control devices and interfaces through cognitive commands. Although there are many different methods for analyzing MI-EEG signals, research into deep learning and transfer learning approaches for MI-EEG analysis remains scarce. This research leverages the superlet transform (SLT) to transform EEG signals into a two-dimensional (2-D) high-resolution spectral representation. This 2-D representation of segmented MI-EEG signals is then processed through an adapted pretrained residual network, which classifies the MI-EEG signals. The effectiveness of the suggested technique is evident as the achieved classification accuracy is 99.9% for binary tasks and 96.4% for multi-class tasks, representing a significant advancement over existing methods. Through an intensive comparison with present algorithms assessed in variety of performance evaluating metrics the present study emphasize the exceptional ability of proposed approach to accurately classify the different MI categories from the EEG signals and which is a great contribution to the field of BCI research field. |
| format | Article |
| id | doaj-art-f30da86519874801b32390f08715461d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f30da86519874801b32390f08715461d2025-01-07T00:01:31ZengIEEEIEEE Access2169-35362025-01-01132141215110.1109/ACCESS.2024.351763910802872A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery ClassificationNeha Sharma0https://orcid.org/0000-0002-4613-9812Manoj Sharma1https://orcid.org/0000-0001-5592-1649Amit Singhal2https://orcid.org/0000-0002-4010-6614Nuzhat Fatema3Vinay Kumar Jadoun4https://orcid.org/0000-0002-3373-8613Hasmat Malik5https://orcid.org/0000-0002-0085-9734Asyraf Afthanorhan6https://orcid.org/0000-0002-8817-9062Department of Computer Science Engineering, Faculty of Engineering and Technology, SGT University, Gurugram, IndiaSchool of Artificial Intelligence, Bennett University, Greater Noida, IndiaElectronics and Communication Department, Netaji Subhas University of Technology, Delhi, IndiaFaculty of Management, Universiti Teknologi Malaysia (UTM), Johor Bahru, MalaysiaDepartment of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Electrical Power Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Skudai, MalaysiaArtificial Intelligence for Islamic Civilization and Sustainability, Universiti Sultan Zainal Abidin (UniSZA), Kuala Nerus, Kuala Terengganu, Terengganu, MalaysiaIn the realm of Brain-Computer Interface (BCI) research, the precise decoding of motor imagery electroencephalogram (MI-EEG) signals is pivotal for the realization of systems that can be seamlessly integrated into practical applications, enhancing the autonomy of individuals with mobility impairments. This study presents an enhanced method for the precise recognition of MI tasks using EEG data, to facilitate more intuitive interactions between individuals with mobility challenges and their environment. The core challenge addressed herein is the development of robust algorithms that enable the accurate identification of MI tasks, thereby empowering individuals with mobility impairments to control devices and interfaces through cognitive commands. Although there are many different methods for analyzing MI-EEG signals, research into deep learning and transfer learning approaches for MI-EEG analysis remains scarce. This research leverages the superlet transform (SLT) to transform EEG signals into a two-dimensional (2-D) high-resolution spectral representation. This 2-D representation of segmented MI-EEG signals is then processed through an adapted pretrained residual network, which classifies the MI-EEG signals. The effectiveness of the suggested technique is evident as the achieved classification accuracy is 99.9% for binary tasks and 96.4% for multi-class tasks, representing a significant advancement over existing methods. Through an intensive comparison with present algorithms assessed in variety of performance evaluating metrics the present study emphasize the exceptional ability of proposed approach to accurately classify the different MI categories from the EEG signals and which is a great contribution to the field of BCI research field.https://ieeexplore.ieee.org/document/10802872/Motor imagery (MI)deep neural network (DNN)superlet transform (SLT)brain-computer interface (BCI) |
| spellingShingle | Neha Sharma Manoj Sharma Amit Singhal Nuzhat Fatema Vinay Kumar Jadoun Hasmat Malik Asyraf Afthanorhan A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification IEEE Access Motor imagery (MI) deep neural network (DNN) superlet transform (SLT) brain-computer interface (BCI) |
| title | A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification |
| title_full | A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification |
| title_fullStr | A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification |
| title_full_unstemmed | A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification |
| title_short | A Spatiotemporal Feature Extraction Technique Using Superlet-CNN Fusion for Improved Motor Imagery Classification |
| title_sort | spatiotemporal feature extraction technique using superlet cnn fusion for improved motor imagery classification |
| topic | Motor imagery (MI) deep neural network (DNN) superlet transform (SLT) brain-computer interface (BCI) |
| url | https://ieeexplore.ieee.org/document/10802872/ |
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