![]() ![]() Results suggest that EEGNet is robust enough to learn a wide variety of Of a trained EEGNet model to enable interpretation of the learned features. We demonstrate three different approaches to visualize the contents We show that EEGNet generalizes across paradigmsīetter than the reference algorithms when only limited training data isĪvailable. Negativity responses (ERN), movement-related cortical potentials (MRCP), and We compare EEGNet to current state-of-the-art approachesĪcross four BCI paradigms: P300 visual-evoked potentials, error-related Introduce the use of depthwise and separable convolutions to construct anĮEG-specific model which encapsulates well-known EEG feature extractionĬoncepts for BCI. Introduce EEGNet, a compact convolutional network for EEG-based BCIs. ![]() Paradigms, while simultaneously being as compact as possible. Single CNN architecture to accurately classify EEG signals from different BCI ![]() Successfully been applied to EEG-based BCIs however, they have mainly beenĪpplied to single BCI paradigms and thus it remains unclear how theseĪrchitectures generalize to other paradigms. Which have been used in computer vision and speech recognition, have Given BCI paradigm, feature extractors and classifiers are tailored to theĭistinct characteristics of its expected EEG control signal, limiting itsĪpplication to that specific signal. This neural signal is generallyĬhosen from a variety of well-studied electroencephalogram (EEG) signals. Using neural activity as the control signal. Lance Download PDF Abstract: Brain computer interfaces (BCI) enable direct communication with a computer, ![]()
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