4 papers
EEGDash: An open-source platform for machine learning on public neurophysiological data
Bruno Aristimunha, Aviv Dotan, Pierre Guetschel +7
Public neurophysiological datasets are increasingly accessible but remain hard to reuse: turning one into a trained model still takes thousands of lines of code for download, loadi…
WavJEPA: Semantic learning unlocks robust audio foundation models for raw waveforms
Goksenin Yuksel, Pierre Guetschel, Michael Tangermann +2
Learning audio representations from raw waveforms overcomes key limitations of spectrogram-based audio representation learning, such as the long latency of spectrogram computation…
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel +16
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-ba…
Review of Deep Representation Learning Techniques for Brain-Computer Interfaces and Recommendations
Pierre Guetschel, Sara Ahmadi, Michael Tangermann
In the field of brain-computer interfaces (BCIs), the potential for leveraging deep learning techniques for representing electroencephalogram (EEG) signals has gained substantial i…