Subject Conditioning for Motor Imagery Using Attention Mechanism
This paper presents an advanced approach for enhancing electroencephalography (EEG) classification accuracy in motor tasks through the integration of subject-specific features. Recognizing the significant challenge posed by inter-subject variability in EEG signal processing, our method focuses on ad...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10716385/ |
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| _version_ | 1846102108805267456 |
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| author | Adam Gyula Nemes Gyorgy Eigner |
| author_facet | Adam Gyula Nemes Gyorgy Eigner |
| author_sort | Adam Gyula Nemes |
| collection | DOAJ |
| description | This paper presents an advanced approach for enhancing electroencephalography (EEG) classification accuracy in motor tasks through the integration of subject-specific features. Recognizing the significant challenge posed by inter-subject variability in EEG signal processing, our method focuses on addressing individual differences in EEG data. The proposed ‘Attentive Subject Fusion’ method leverages power spectral density characteristics to encode subject-specific information using a single-layer perceptron. Subsequently, an attention mechanism integrates these features with the actual EEG signal processed by an M-ShallowConvNet.Empirical evaluations demonstrate that incorporating subject-specific features markedly improves the performance of deep learning models in EEG motor task classification. |
| format | Article |
| id | doaj-art-0e5f6feddffa46b2b90bf81d48f7b0d2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0e5f6feddffa46b2b90bf81d48f7b0d22024-12-28T00:00:35ZengIEEEIEEE Access2169-35362024-01-011217024317024910.1109/ACCESS.2024.347930810716385Subject Conditioning for Motor Imagery Using Attention MechanismAdam Gyula Nemes0https://orcid.org/0009-0004-5855-3174Gyorgy Eigner1https://orcid.org/0000-0001-8038-2210Applied Informatics and Applied Mathematics Doctoral School, Óbuda University, Budapest, HungaryPhysiological Controls Research Center, Óbuda University, Budapest, HungaryThis paper presents an advanced approach for enhancing electroencephalography (EEG) classification accuracy in motor tasks through the integration of subject-specific features. Recognizing the significant challenge posed by inter-subject variability in EEG signal processing, our method focuses on addressing individual differences in EEG data. The proposed ‘Attentive Subject Fusion’ method leverages power spectral density characteristics to encode subject-specific information using a single-layer perceptron. Subsequently, an attention mechanism integrates these features with the actual EEG signal processed by an M-ShallowConvNet.Empirical evaluations demonstrate that incorporating subject-specific features markedly improves the performance of deep learning models in EEG motor task classification.https://ieeexplore.ieee.org/document/10716385/Deep learningelectroencephalographymotor imageryattentionsubject conditioning |
| spellingShingle | Adam Gyula Nemes Gyorgy Eigner Subject Conditioning for Motor Imagery Using Attention Mechanism IEEE Access Deep learning electroencephalography motor imagery attention subject conditioning |
| title | Subject Conditioning for Motor Imagery Using Attention Mechanism |
| title_full | Subject Conditioning for Motor Imagery Using Attention Mechanism |
| title_fullStr | Subject Conditioning for Motor Imagery Using Attention Mechanism |
| title_full_unstemmed | Subject Conditioning for Motor Imagery Using Attention Mechanism |
| title_short | Subject Conditioning for Motor Imagery Using Attention Mechanism |
| title_sort | subject conditioning for motor imagery using attention mechanism |
| topic | Deep learning electroencephalography motor imagery attention subject conditioning |
| url | https://ieeexplore.ieee.org/document/10716385/ |
| work_keys_str_mv | AT adamgyulanemes subjectconditioningformotorimageryusingattentionmechanism AT gyorgyeigner subjectconditioningformotorimageryusingattentionmechanism |