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Post-hoc Labeling of Arbitrary M/EEG Recordings for Data-Efficient Evaluation of Neural Decoding Methods
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Workshops of the seventh international brain-computer interface meeting: not getting lost in translation
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Eyes-Closed Increases the Usability of Brain-Computer Interfaces Based on Auditory Event-Related Potentials
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Characterizing Regularization Techniques for Spatial Filter Optimization in Oscillatory EEG Regression Problems
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Unsupervised Learning for Brain-Computer Interfaces Based on Event-Related Potentials: Review and Online Comparison [Research Frontier]
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No evidence for confounding orientation-dependent fixational eye movements under baseline conditions
Decoding has become a standard analysis technique for contemporary cognitive neuroscience. Already more than a decade ago, it was shown …
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Post-hoc labeling of arbitrary EEG recordings for data-efficient evaluation of neural decoding methods
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Deep Learning with Convolutional Neural Networks for EEG Decoding and Visualization
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Improving zero-training brain-computer interfaces by mixing model estimators
Abstract Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded …
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Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG
Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end-to-end learning, i.e. …
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