Compact convolutional neural networks for classification of asynchronous steady-state visual evoked potentials. (31st October 2018)

• Record Type: Journal Article
• Authors: Waytowich, Nicholas; Lawhern, Vernon J; Garcia, Javier O; Cummings, Jennifer; Faller, Josef; Sajda, Paul; Vettel, Jean M
• Is Part Of: Journal of neural engineering. Volume 15:Number 6(2018:Dec.)
• Publication Date: 2018-10-31
• Subjects: brain–computer interface -- convolutional neural networks -- steady-state visual evoked potentials -- deep learning; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/aae5d8
• Languages: English
• ISSNs: 1741-2560

Abstract

Abstract: Objective . 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. Approach . 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 user-specific calibration. Main results . The Compact-CNN demonstrates across subject mean accuracy of approximately 80%, out-performing current state-of-the-art, hand-crafted approaches using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, the Compact-CNN approach can reveal the underlying feature representation, revealing that the deep learner extracts additional phase- and amplitude-related features associated with the structure of the dataset. Significance . 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.