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bioRxiv · 10.1101/2023.05.29.542778

OICNet: A Neural Network for Online EEG Source Separation using Independent Component Analysis

Yeh, P.-T.·Tsai, A. C.·Hsieh, C.-Y.·Yang, C.-C.·Wei, C.-S.
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Abstract

Online source separation of EEG signals plays a crucial role in understanding and interpreting brain dynamics in real-time applications such as brain-computer interfaces (BCIs). In this paper, we propose OICNet, a novel neural network designed specifically for online EEG source separation using independent component analysis, aiming to address the challenges of real-time computational efficiency and reliable extraction of independent components from EEG data streams. The OICNet is trained on a loss function that integrates non-Gaussianity measurement and an orthogonality constraint to achieve effective decomposition of multi-channel EEG signals. We conducted comprehensive evaluation of OICNet on both task-related and task-free EEG datasets with comparison against conventional and network-based ICA counterparts. The results demonstrate that OICNet outperforms existing methods in terms of accuracy and computational efficiency. Overall, OICNet provides high-efficiency real-time EEG source separation capabilities and paves the way for advancements in deep-learning EEG processing in real-world BCI applications.

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Yeh, P.-T., Tsai, A. C., Hsieh, C.-Y., Yang, C.-C., Wei, C.-S.. 2023-06-01. OICNet: A Neural Network for Online EEG Source Separation using Independent Component Analysis. https://doi.org/10.1101/2023.05.29.542778

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