STAR: A Spatial-Temporal Autoencoder for EEG Restoration in Emotion Recognition

Research in emotion recognition using electroencephalography (EEG) has advanced rapidly, and affective EEG-based Brain-computer Interface (aBCI) technology is increasingly moving from lab research to real-world application. Nevertheless, EEG signals are inherently delicate and prone to noise and art...

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Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5
Hlavní autoři: Yin, Hao-Long, Zheng, Wei-Long, Lu, Bao-Liang
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 06.04.2025
Témata:
Attention mechanisms
Autoencoders
Brain modeling
Data models
EEG restoration
EEG-based emotion recognition
Electroencephalography
Emotion recognition
Spatiotemporal phenomena
Speech recognition
Stars
ISSN:2379-190X
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Shrnutí:Research in emotion recognition using electroencephalography (EEG) has advanced rapidly, and affective EEG-based Brain-computer Interface (aBCI) technology is increasingly moving from lab research to real-world application. Nevertheless, EEG signals are inherently delicate and prone to noise and artifacts, especially in real-world environments where data quality often lags behind laboratory standards. This disparity poses substantial challenges for models trained on high-quality datasets. Conventional methods, such as data interpolation or exclusion, limit model efficacy. To overcome these challenges, we introduce the Spatial-Temporal Autoencoder for EEG Restoration (STAR). STAR leverages dynamic channel and temporal masking to mimic real-world signal degradation and incorporates a spatial-temporal alternating attention mechanism to encapsulate intricate spatiotemporal dynamics within EEG data. Our evaluations on three premium emotion recognition EEG datasets reveal that STAR effectively restores signals across varying corruption levels, significantly bolstering the performance of emotion recognition models in suboptimal conditions.
ISSN:2379-190X
DOI:10.1109/ICASSP49660.2025.10889545