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Found 6 papers, 4 papers with code
Brain decoding: toward real-time reconstruction of visual perception
In the past five years, the use of generative and foundational AI systems has greatly improved the decoding of brain activity.
Aligning brain functions boosts the decoding of visual semantics in novel subjects
Deep learning is leading to major advances in the realm of brain decoding from functional Magnetic Resonance Imaging (fMRI).
Self-supervised representation learning from electroencephalography signals
The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains.
Robust learning from corrupted EEG with dynamic spatial filtering
Building machine learning models using EEG recorded outside of the laboratory setting requires methods robust to noisy data and randomly missing channels.
Uncovering the structure of clinical EEG signals with self-supervised learning
Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.
Deep learning-based electroencephalography analysis: a systematic review
To help the field progress, we provide a list of recommendations for future studies and we make our summary table of DL and EEG papers available and invite the community to contribute.
