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Transfer Learning in Brain-Computer Interfaces
TLDR
A framework for transfer learning in the context of BCIs that can be applied to any arbitrary feature space, as well as a novel regression estimation method that is specifically designed for the structure of a system based on the electroencephalogram (EEG). Expand
MOABB: Trustworthy algorithm benchmarking for BCIs
TLDR
A software suite that streamlines both the issues of finding and preprocessing data in a reliable manner, as well as that of using a consistent interface for machine learning methods, is offered. Expand
Speech-specific tuning of neurons in human superior temporal gyrus.
TLDR
Neurons in human superior temporal gyrus use sparse spatially organized population encoding of complex acoustic-phonetic features to help recognize auditory and visual words. Expand
Case series: Slowing alpha rhythm in late-stage ALS patients
TLDR
The alpha peak frequency of the human electroencephalogram (EEG) is a reliable neurophysiological marker for cognitive abilities and a shift towards the lower end of the EEG spectrum in two completely locked-in ALS patients is documented. Expand
Tangent space spatial filters for interpretable and efficient Riemannian classification
TLDR
This work rigorously proved the exact equivalence between any linear function on the tangent space and corresponding derived spatial filters and proposed spatial filtering methods allow for more efficient classification as well as the removal of artifact sources from classifiers built on Riemannian methods. Expand
Brain-computer interfacing in amyotrophic lateral sclerosis: Implications of a resting-state EEG analysis
TLDR
An ALS-specific global increase in gamma power (30-90 Hz) that is not specific to the motor cortex is found, suggesting that the mechanism behind ALS affects non-motor cortical regions even in the absence of comorbid cognitive deficits. Expand
A Cognitive Brain-Computer Interface for Patients with Amyotrophic Lateral Sclerosis
TLDR
A new paradigm that targets higher level cognitive processes to transmit information from the user to the BCI is devised that could serve as a basis for a novel tool which allows for simple, reliable communication with patients in late stages of ALS. Expand
Task-induced frequency modulation features for brain-computer interfacing.
TLDR
This work compares cross-validated classification accuracies using the amplitude and frequency modulated features, as well as a joint feature space, across subjects in various paradigms and pre-processing conditions, and finds that task-induced frequency modulation is robust and straight forward to compute, and increases performance when added to standard amplitude modulation features across paradigm. Expand
A cognitive brain-computer interface for patients with amyotrophic lateral sclerosis.
TLDR
A new paradigm that targets higher-level cognitive processes to transmit information from the user to the BCI is devised and the spatial weights of the decoding algorithm show a preference for the parietal area, consistent with modulation of neural activity in primary nodes of the DMN. Expand
Multi-task logistic regression in brain-computer interfaces
TLDR
The ability of the model to identify relevant topographies along with signal band-power features that agree with neurophysiological properties of a common sensorimotor rhythm paradigm is demonstrated, making it further applicable for identification, analysis and evaluation of paradigm characteristics without relying on expert knowledge. Expand
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