What does my network learn? Assessing interpretability of deep learning for EEG.

Area of Science:

• Neuroscience
• Machine Learning
• Signal Processing

Background:

• Electrophysiological studies increasingly use multivariate pattern analysis.
• Traditional machine learning methods often assume consistent response latencies, limiting their application.
• Deep learning offers high performance in EEG analysis but faces challenges in feature interpretability.

Purpose of the Study:

• To evaluate how preprocessing, network architecture, and feature extraction/visualization impact deep learning interpretability for electroencephalography (EEG) data.
• To compare the interpretability of EEGNet and ResNet convolutional neural networks (CNNs) for EEG decoding.
• To assess the influence of gradient-based visualization techniques (saliency, GradCam) on feature interpretability.

Main Methods:

• Trained two CNNs, EEGNet and ResNet, to decode single-trial EEG responses to visual and auditory stimuli.
• Extracted and visualized learned features using saliency and Gradient-weighted Activation Maps (GradCam).
• Compared the performance and feature interpretability of the two CNN architectures and visualization techniques.

Main Results:

• EEGNet and ResNet demonstrated comparable decoding performance.
• Different network architectures learned distinct data features; EEGNet features showed higher similarity to raw EEG data.
• GradCam visualization yielded features more aligned with EEG data compared to saliency mapping.
• Visualization techniques influenced the perceived latency and distribution of important electrodes.

Conclusions:

• Network architecture and feature visualization significantly impact the interpretability of deep learning models for EEG.
• EEGNet may offer better interpretability than ResNet due to feature similarity to EEG data.
• GradCam is a more effective visualization technique for EEG interpretability than saliency.
• Further research into architecture and visualization is essential for advancing deep learning in EEG research.