Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.
Visuo-perceptual processing in autism is characterized by intact or enhanced performance on static spatial tasks and inferior performance on dynamic tasks, suggesting a deficit of dorsal visual stream processing in autism. However, previous findings by Bertone et al. indicate that neuro-integrative mechanisms used to detect complex motion, rather than motion perception per se, may be impaired in autism. We present here the first demonstration of concurrent enhanced and decreased performance in autism on the same visuo-spatial static task, wherein the only factor dichotomizing performance was the neural complexity required to discriminate grating orientation. The ability of persons with autism was found to be superior for identifying the orientation of simple, luminance-defined (or first-order) gratings but inferior for complex, texture-defined (or second-order) gratings. Using a flicker contrast sensitivity task, we demonstrated that this finding is probably not due to abnormal information processing at a sub-cortical level (magnocellular and parvocellular functioning). Together, these findings are interpreted as a clear indication of altered low-level perceptual information processing in autism, and confirm that the deficits and assets observed in autistic visual perception are contingent on the complexity of the neural network required to process a given type of visual stimulus. We suggest that atypical neural connectivity, resulting in enhanced lateral inhibition, may account for both enhanced and decreased low-level information processing in autism.
Evidence suggests that an athlete's sports-related perceptual-cognitive expertise is a crucial element of top-level competitive sports1. When directly assessing whether such experience-related abilities correspond to fundamental and non-specific cognitive laboratory measures such as processing speed and attention, studies have shown moderate effects leading to the conclusion that their special abilities are context-specific2. We trained 308 observers on a complex dynamic visual scene task void of context and motor control requirements3 and demonstrate that professionals as a group dramatically differ from high-level amateur athletes, who dramatically differ from non-athlete university students in their capacity to learn such stimuli. This demonstrates that a distinguishing factor explaining the capacities of professional athletes is their ability to learn how to process complex dynamic visual scenes. This gives us an insight as to what is so special about the elite athletes' mental abilities, which allows them to express great prowess in action.
We present the first assessment of motion sensitivity for persons with autism and normal intelligence using motion patterns that require neural processing mechanisms of varying complexity. Compared to matched controls, our results demonstrate that the motion sensitivity of observers with autism is similar to that of nonautistic observers for different types of first-order (luminance-defined) motion stimuli, but significantly decreased for the same types of second-order (texture-defined) stimuli. The latter class of motion stimuli has been demonstrated to require additional neural computation to be processed adequately. This finding may reflect less efficient integrative functioning of the neural mechanisms that mediate visuoperceptual processing in autism. The contribution of this finding with regards to abnormal perceptual integration in autism, its effect on cognitive operations, and possible behavioral implications are discussed.
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