ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury
Brain–computer interfaces (BCIs) facilitate communication between the brain and external devices, providing an alternative solution for individuals with upper limb disabilities. The decoding of brain movement commands in BCIs relies on signal feature extraction and classification. Herein, the BNCI H...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-12-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202300094 |
| _version_ | 1797378728866611200 |
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| author | Sandra Cancino Juan Manuel López Jaime F. Delgado Saa Norelli Schettini |
| author_facet | Sandra Cancino Juan Manuel López Jaime F. Delgado Saa Norelli Schettini |
| author_sort | Sandra Cancino |
| collection | DOAJ |
| description | Brain–computer interfaces (BCIs) facilitate communication between the brain and external devices, providing an alternative solution for individuals with upper limb disabilities. The decoding of brain movement commands in BCIs relies on signal feature extraction and classification. Herein, the BNCI Horizon 2020 dataset is employed, which consists of electroencephalographic signals from ten participants with subacute and chronic cervical spinal cord injuries. These participants perform or attempt five distinct types of arm and hand movements. To extract signal features, a novel technique is introduced that estimates movement‐related cortical potentials and incorporates them into the processing pipeline. Moreover, a time‐frequency domain representation of the dataset is used as input for the classifier. Given the promising outcomes demonstrated by deep learning models in BCI classification, a pretrained ConvNet AlexNet is adopted to decode the motor tasks. The proposed method exhibits a remarkable average accuracy of 76.0% across all five categories, representing a significant advancement over existing state‐of‐the‐art techniques. Additionally, an in‐depth analysis of the convolutional layers in the model is conducted to gain comprehensive insights into the classification process. By examining the ConvNet filters and activations, the method contributes to a deeper understanding of the electrophysiology that underlies attempted movement. |
| first_indexed | 2024-03-08T20:11:53Z |
| format | Article |
| id | doaj.art-319327c7d5bc4f3f96cd6187fe8121f6 |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-03-08T20:11:53Z |
| publishDate | 2023-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-319327c7d5bc4f3f96cd6187fe8121f62023-12-23T04:53:50ZengWileyAdvanced Intelligent Systems2640-45672023-12-01512n/an/a10.1002/aisy.202300094ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord InjurySandra Cancino0Juan Manuel López1Jaime F. Delgado Saa2Norelli Schettini3Department of Electrical and Electronics Engineering Universidad del Norte Km. 5 vía Puerto Colombia, Área Metropolitana de Barranquilla Barranquilla ColombiaCalgary CanadaSciFork SARL Geneva SwitzerlandDepartment of Electrical and Electronics Engineering Universidad del Norte Km. 5 vía Puerto Colombia, Área Metropolitana de Barranquilla Barranquilla ColombiaBrain–computer interfaces (BCIs) facilitate communication between the brain and external devices, providing an alternative solution for individuals with upper limb disabilities. The decoding of brain movement commands in BCIs relies on signal feature extraction and classification. Herein, the BNCI Horizon 2020 dataset is employed, which consists of electroencephalographic signals from ten participants with subacute and chronic cervical spinal cord injuries. These participants perform or attempt five distinct types of arm and hand movements. To extract signal features, a novel technique is introduced that estimates movement‐related cortical potentials and incorporates them into the processing pipeline. Moreover, a time‐frequency domain representation of the dataset is used as input for the classifier. Given the promising outcomes demonstrated by deep learning models in BCI classification, a pretrained ConvNet AlexNet is adopted to decode the motor tasks. The proposed method exhibits a remarkable average accuracy of 76.0% across all five categories, representing a significant advancement over existing state‐of‐the‐art techniques. Additionally, an in‐depth analysis of the convolutional layers in the model is conducted to gain comprehensive insights into the classification process. By examining the ConvNet filters and activations, the method contributes to a deeper understanding of the electrophysiology that underlies attempted movement.https://doi.org/10.1002/aisy.202300094brain–computer interfacesdeep learningelectroencephalography (EEG)transfer learning |
| spellingShingle | Sandra Cancino Juan Manuel López Jaime F. Delgado Saa Norelli Schettini ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury Advanced Intelligent Systems brain–computer interfaces deep learning electroencephalography (EEG) transfer learning |
| title | ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury |
| title_full | ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury |
| title_fullStr | ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury |
| title_full_unstemmed | ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury |
| title_short | ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury |
| title_sort | convnets for electroencephalographic decoding of attempted arm and hand movements of people with spinal cord injury |
| topic | brain–computer interfaces deep learning electroencephalography (EEG) transfer learning |
| url | https://doi.org/10.1002/aisy.202300094 |
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