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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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10716385/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |