Multi-Scale Neural Network for EEG Representation Learning in BCI
| Autor: | Seungwoo Jeong, Wonjun Ko, Heung-Il Suk, Eunjin Jeon |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Computer science Feature extraction Machine Learning (stat.ML) 02 engineering and technology Convolutional neural network Machine Learning (cs.LG) Theoretical Computer Science Statistics - Machine Learning Artificial Intelligence FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Electrical Engineering and Systems Science - Signal Processing Brain–computer interface Network architecture Artificial neural network business.industry Deep learning Pattern recognition 020201 artificial intelligence & image processing Artificial intelligence business Feature learning |
| Zdroj: | IEEE Computational Intelligence Magazine. 16:31-45 |
| ISSN: | 1556-6048 1556-603X |
| Popis: | Recent advances in deep learning have had a methodological and practical impact on brain-computer interface research. Among the various deep network architectures, convolutional neural networks have been well suited for spatio-spectral-temporal electroencephalogram signal representation learning. Most of the existing CNN-based methods described in the literature extract features at a sequential level of abstraction with repetitive nonlinear operations and involve densely connected layers for classification. However, studies in neurophysiology have revealed that EEG signals carry information in different ranges of frequency components. To better reflect these multi-frequency properties in EEGs, we propose a novel deep multi-scale neural network that discovers feature representations in multiple frequency/time ranges and extracts relationships among electrodes, i.e., spatial representations, for subject intention/condition identification. Furthermore, by completely representing EEG signals with spatio-spectral-temporal information, the proposed method can be utilized for diverse paradigms in both active and passive BCIs, contrary to existing methods that are primarily focused on single-paradigm BCIs. To demonstrate the validity of our proposed method, we conducted experiments on various paradigms of active/passive BCI datasets. Our experimental results demonstrated that the proposed method achieved performance improvements when judged against comparable state-of-the-art methods. Additionally, we analyzed the proposed method using different techniques, such as PSD curves and relevance score inspection to validate the multi-scale EEG signal information capturing ability, activation pattern maps for investigating the learned spatial filters, and t-SNE plotting for visualizing represented features. Finally, we also demonstrated our method's application to real-world problems. 11 pages, 7 figures |
| Databáze: | OpenAIRE |
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