Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials

Abstract

Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli. SSVEPs are robust signals measurable in the electroencephalogram (EEG) and are commonly used in brain-computer interfaces (BCIs). However, methods for high-accuracy decoding of SSVEPs usually require hand-crafted approaches that leverage domain-specific knowledge of the stimulus signals, such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement. When this knowledge is unavailable, such as when SSVEP signals are acquired asynchronously, such approaches tend to fail. In this paper, we show how a compact convolutional neural network (Compact-CNN), which only requires raw EEG signals for automatic feature extraction, can be used to decode signals from a 12-class SSVEP dataset without the need for any domain-specific knowledge or calibration data. We report across subject mean accuracy of approximately 80% (chance being 8.3%) and show this is substantially better than current state-of-the-art hand-crafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, we analyze our Compact-CNN to examine the underlying feature representation, discovering that the deep learner extracts additional phase and amplitude related features associated with the structure of the dataset. We discuss how our Compact-CNN shows promise for BCI applications that allow users to freely gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as provides a method for analyzing SSVEP signals in a way that might augment our understanding about the basic processing in the visual cortex.

Accepted for publication at the Journal of Neural Engineering

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• Universal Joint Feature Extraction for P300 EEG Classification using Multi-task Autoencoder
• Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning
• A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
• Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016
• Multi-Scale Neural network for EEG Representation Learning in BCI
• Two-stage frequency recognition method based on correlated component analysis for SSVEP-based BCI