Temporal Pyramid Pooling for Decoding Motor-Imagery EEG Signals
Detecting a user's intentions is critical in human-computer interactions. Recently, brain-computer interfaces (BCIs) have been extensively studied to facilitate more accurate detection and prediction of the user's intentions. Specifically, various deep learning approaches have been applied...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9309212/ |
| _version_ | 1818448799802064896 |
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| author | Kwon-Woo Ha Jin-Woo Jeong |
| author_facet | Kwon-Woo Ha Jin-Woo Jeong |
| author_sort | Kwon-Woo Ha |
| collection | DOAJ |
| description | Detecting a user's intentions is critical in human-computer interactions. Recently, brain-computer interfaces (BCIs) have been extensively studied to facilitate more accurate detection and prediction of the user's intentions. Specifically, various deep learning approaches have been applied to the BCIs for decoding the user's intent from motor-imagery electroencephalography (EEG) signals. However, their ability to capture the important features of an EEG signal remains limited, resulting in the deterioration of performance. In this paper, we propose a multi-layer temporal pyramid pooling approach to improve the performance of motor imagery-based BCIs. The proposed scheme introduces the application of multilayer multiscale pooling and fusion methods to capture various features of an EEG signal, which can be easily integrated into modern convolutional neural networks (CNNs). The experimental results based on the BCI competition IV dataset indicate that the CNN architectures with the proposed multilayer pyramid pooling method enhance classification performance compared to the original networks. |
| first_indexed | 2024-12-14T20:25:16Z |
| format | Article |
| id | doaj.art-aa789e75c7984ef09b477662f919bdf5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T20:25:16Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-aa789e75c7984ef09b477662f919bdf52022-12-21T22:48:39ZengIEEEIEEE Access2169-35362021-01-0193112312510.1109/ACCESS.2020.30476789309212Temporal Pyramid Pooling for Decoding Motor-Imagery EEG SignalsKwon-Woo Ha0https://orcid.org/0000-0001-5287-7902Jin-Woo Jeong1https://orcid.org/0000-0001-9313-6860Department of Computer Engineering, Kumoh National Institute of Technology, Gumi, South KoreaDepartment of Computer Engineering, Kumoh National Institute of Technology, Gumi, South KoreaDetecting a user's intentions is critical in human-computer interactions. Recently, brain-computer interfaces (BCIs) have been extensively studied to facilitate more accurate detection and prediction of the user's intentions. Specifically, various deep learning approaches have been applied to the BCIs for decoding the user's intent from motor-imagery electroencephalography (EEG) signals. However, their ability to capture the important features of an EEG signal remains limited, resulting in the deterioration of performance. In this paper, we propose a multi-layer temporal pyramid pooling approach to improve the performance of motor imagery-based BCIs. The proposed scheme introduces the application of multilayer multiscale pooling and fusion methods to capture various features of an EEG signal, which can be easily integrated into modern convolutional neural networks (CNNs). The experimental results based on the BCI competition IV dataset indicate that the CNN architectures with the proposed multilayer pyramid pooling method enhance classification performance compared to the original networks.https://ieeexplore.ieee.org/document/9309212/Brain–computer interfacedeep learningfeature fusionpyramid pooling |
| spellingShingle | Kwon-Woo Ha Jin-Woo Jeong Temporal Pyramid Pooling for Decoding Motor-Imagery EEG Signals IEEE Access Brain–computer interface deep learning feature fusion pyramid pooling |
| title | Temporal Pyramid Pooling for Decoding Motor-Imagery EEG Signals |
| title_full | Temporal Pyramid Pooling for Decoding Motor-Imagery EEG Signals |
| title_fullStr | Temporal Pyramid Pooling for Decoding Motor-Imagery EEG Signals |
| title_full_unstemmed | Temporal Pyramid Pooling for Decoding Motor-Imagery EEG Signals |
| title_short | Temporal Pyramid Pooling for Decoding Motor-Imagery EEG Signals |
| title_sort | temporal pyramid pooling for decoding motor imagery eeg signals |
| topic | Brain–computer interface deep learning feature fusion pyramid pooling |
| url | https://ieeexplore.ieee.org/document/9309212/ |
| work_keys_str_mv | AT kwonwooha temporalpyramidpoolingfordecodingmotorimageryeegsignals AT jinwoojeong temporalpyramidpoolingfordecodingmotorimageryeegsignals |