Title: 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.