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Found 7 papers, 3 papers with code
Approximate UMAP allows for high-rate online visualization of high-dimensional data streams
In the BCI field, introspection and interpretation of brain signals are desired for providing feedback or to guide rapid paradigm prototyping but are challenging due to the high noise level and dimensionality of the signals.
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
The significance of this study lies in its contribution to establishing a rigorous and transparent benchmark for BCI research, offering insights into optimal methodologies and highlighting the importance of reproducibility in driving advancements within the field.
Synthesizing EEG Signals from Event-Related Potential Paradigms with Conditional Diffusion Models
Data scarcity in the brain-computer interface field can be alleviated through the use of generative models, specifically diffusion models.
S-JEPA: towards seamless cross-dataset transfer through dynamic spatial attention
Motivated by the challenge of seamless cross-dataset transfer in EEG signal processing, this article presents an exploratory study on the use of Joint Embedding Predictive Architectures (JEPAs).
Transfer Learning between Motor Imagery Datasets using Deep Learning -- Validation of Framework and Comparison of Datasets
Performance of this transfer approach is then tested on other trials of the receiver dataset.
An embedding for EEG signals learned using a triplet loss
In offline analyses using EEG data of 14 subjects, we tested the embeddings' feasibility and compared their efficiency with state-of-the-art deep learning models and conventional machine learning pipelines.
Embedding neurophysiological signals
Neurophysiological time-series recordings of brain activity like the electroencephalogram (EEG) or local field potentials can be decoded by machine learning models in order to either control an application, e. g., for communication or rehabilitation after stroke, or to passively monitor the ongoing brain state of the subject, e. g., in a demanding work environment.
