Paper:
Targeting EEG/LFP Synchrony with Neural Nets
We consider the analysis of Electroencephalography (EEG) and Local Field Potential (LFP) datasets, which are "big" in terms of the size of recorded data but rarely have sufficient labels required to train complex models (e.g., conventional deep learning methods). Furthermore, in many scientific applications, the goal is to be able to understand the underlying features related to the classification, which prohibits the blind application of deep networks. This motivates the development of a new model based on parameterized convolutional filters guided by previous neuroscience research; the filters learn relevant frequency bands while targeting synchrony, which are frequency-specific power and phase correlations between electrodes. This results in a highly expressive convolutional neural network with only a few hundred parameters, applicable to smaller datasets. The proposed approach is demonstrated to yield competitive (often state-of-the-art) predictive performance during our empirical tests while yielding interpretable features. Furthermore, a Gaussian process adapter is developed to combine analysis over distinct electrode layouts, allowing the joint processing of multiple datasets to address overfitting and improve generalizability. Finally, it is demonstrated that the proposed framework effectively tracks neural dynamics on children in a clinical trial on Autism Spectrum Disorder.

Review:
The manuscript introduces the novel CNN named SyncNet, which was designed to detect synchrony/spectral coherence in brain electrophysiological signals. SyncNet uses parameterized convolutional filters. To overcome common issues in EEG (varying setup configuration, correlated channels, ...) a Gaussian Process Adaptor is used to transform EEG data into a pseudo input space. The design of SyncNet is chosen to allow interpretation of learned featruers. The method was applied to several publicly available datasets and its performance compared to state of the art algorithms. Results suggest that SyncNet yields competitive results.
The topic of the paper is timely and important and the presented approach very interesting. The paper is well organized and mostly clear, and the reviewer agrees with the authors reasoning. There are, however, some issues that need addressing:
- Are the computed SyncNet results significantly better than the results obtained with state of the art methods?
- One key feature of SynchNet is interpretability of features. Are the results summarized in Fig.4 meaningful?
- LFPs are only mentioned in the title and in lines 319-330. Please provide more details on LFP data and analysis or remove LFPs from the manuscript! Currently, LFP does not contribute to enhancing clarity of the paper.