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SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG
Electroencephalography (EEG) provides access to neuronal dynamics non-invasively with
millisecond resolution, rendering it a viable method in neuroscience and healthcare …
millisecond resolution, rendering it a viable method in neuroscience and healthcare …
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space
Objective. This review paper provides an integrated perspective of Explainable Artificial
Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use …
Intelligence (XAI) techniques applied to Brain-Computer Interfaces (BCIs). BCIs use …
Tensor-cspnet: A novel geometric deep learning framework for motor imagery classification
Deep learning (DL) has been widely investigated in a vast majority of applications in
electroencephalography (EEG)-based brain–computer interfaces (BCIs), especially for …
electroencephalography (EEG)-based brain–computer interfaces (BCIs), especially for …
Feel your reach: An EEG-based framework to continuously detect goal-directed movements and error processing to gate kinesthetic feedback informed artificial arm …
Establishing the basic knowledge, methodology, and technology for a framework for the
continuous decoding of hand/arm movement intention was the aim of the ERC-funded …
continuous decoding of hand/arm movement intention was the aim of the ERC-funded …
SPDIM: Source-Free Unsupervised Conditional and Label Shifts Adaptation in EEG
The non-stationary nature of electroencephalography (EEG) introduces distribution shifts
across domains (eg, days and subjects), posing a significant challenge to EEG-based …
across domains (eg, days and subjects), posing a significant challenge to EEG-based …
Controlling the Fréchet variance improves batch normalization on the symmetric positive definite manifold
Symmetric positive definite (SPD) matrices, and in particular co-variance matrices as data
descriptors find widespread application in various fields but also pure machine learning …
descriptors find widespread application in various fields but also pure machine learning …
Effects of cognitive distraction on upper limb movement decoding from EEG signals
Objective: Hand movement decoding from electroencephalograms (EEG) signals is vital to
the rehabilitation and assistance of upper limb-impaired patients. Few existing studies on …
the rehabilitation and assistance of upper limb-impaired patients. Few existing studies on …
Review of training-free event-related potential classification approaches in the world robot contest 2021
Recently, rapid serial visual presentation (RSVP), as a new event-related potential (ERP)
paradigm, has become one of the most popular forms in electroencephalogram signal …
paradigm, has become one of the most popular forms in electroencephalogram signal …
Rcoco: contrastive collective link prediction across multiplex network in riemannian space
Link prediction typically studies the probability of future interconnection among nodes with
the observation in a single social network. More often than not, real scenario is presented as …
the observation in a single social network. More often than not, real scenario is presented as …
Bayesian scalar-on-network regression with applications to brain functional connectivity
This paper presents a Bayesian regression model relating scalar outcomes to brain
functional connectivity represented as symmetric positive definite (SPD) matrices. Unlike …
functional connectivity represented as symmetric positive definite (SPD) matrices. Unlike …