Subject Conditioning for Motor Imagery Using Attention Mechanism

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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Bibliographic Details
Main Authors: Adam Gyula Nemes, Gyorgy Eigner
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep learning
electroencephalography
motor imagery
attention
subject conditioning
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.
ISSN:2169-3536

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