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Review of Deep Representation Learning Techniques for Brain-Computer Interfaces
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Exploring new territory: Calibration-free decoding for c-VEP BCI
This study explores two zero-training methods aimed at enhancing the usability of brain-computer interfaces (BCIs) by eliminating the …
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Fast and robust objective EEG audiometry
The current gold standard of audiometry relies on subjective behavioral responses, which is impractical and unreliable for certain …
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Towards auditory attention decoding with noise-tagging: A pilot study
Auditory attention decoding (AAD) aims to extract from brain activity the attended speaker amidst candidate speakers, offering …
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Towards gaze-independent c-VEP BCI: A pilot study
A limitation of brain-computer interface (BCI) spellers is that they require the user to be able to move the eyes to fixate on targets. …
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EuroDBS. Perspectives of Implementation of Closed-Loop Deep Brain Stimulation: From Neurological to Psychiatric Disorders
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Robustness of Physiological Synchrony in Wearable Electrodermal Activity and Heart Rate as a Measure of Attentional Engagement to Movie Clips
Individuals that pay attention to narrative stimuli show synchronized heart rate (HR) and electrodermal activity (EDA) responses. The …
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Estimating person-specific neural correlates of mental rotation: A machine learning approach
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UMM: Unsupervised Mean-difference Maximization
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Introducing block-Toeplitz covariance matrices to remaster linear discriminant analysis for event-related potential brain–computer interfaces
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