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A large-scale evaluation framework for EEG deep learning architectures
EEG is the most common signal source for noninvasive BCI applications.
Intracranial Error Detection via Deep Learning
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalography (EEG).
The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks
Analysis of brain signals from a human interacting with a robot may help identifying robot errors, but accuracies of such analyses have still substantial space for improvement.
Brain Responses During Robot-Error Observation
Our findings suggest that non-invasive recordings of brain responses elicited when observing robots indeed contain decodable information about the correctness of the robot's action and the type of observed robot.
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