Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding

• Published in: arXiv.org
• Main Authors: Kay Gregor Hartmann, Robin Tibor Schirrmeister, Ball, Tonio
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 15.12.2017
• Subjects: Amplitudes; Artificial neural networks; Computer Science - Learning; Computer vision; Decoding; Electroencephalography; Frequencies; Human-computer interface; Machine learning; Quantitative Biology - Neurons and Cognition; Representations; Sensitivity; Spectra; Statistics - Machine Learning; Task complexity
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1711.07792

Recently, there is increasing interest and research on the interpretability of machine learning models, for example how they transform and internally represent EEG signals in Brain-Computer Interface (BCI) applications. This can help to understand the limits of the model and how it may be improved, in addition to possibly provide insight about the data itself. Schirrmeister et al. (2017) have recently reported promising results for EEG decoding with deep convolutional neural networks (ConvNets) trained in an end-to-end manner and, with a causal visualization approach, showed that they learn to use spectral amplitude changes in the input. In this study, we investigate how ConvNets represent spectral features through the sequence of intermediate stages of the network. We show higher sensitivity to EEG phase features at earlier stages and higher sensitivity to EEG amplitude features at later stages. Intriguingly, we observed a specialization of individual stages of the network to the classical EEG frequency bands alpha, beta, and high gamma. Furthermore, we find first evidence that particularly in the last convolutional layer, the network learns to detect more complex oscillatory patterns beyond spectral phase and amplitude, reminiscent of the representation of complex visual features in later layers of ConvNets in computer vision tasks. Our findings thus provide insights into how ConvNets hierarchically represent spectral EEG features in their intermediate layers and suggest that ConvNets can exploit and might help to better understand the compositional structure of EEG time series.