Alexander Bertrand

Professor

KU Leuven

Belgium

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Professor Alexander Bertrand is a faculty member at KU Leuven in Belgium. His research focuses on advanced methods in signal processing and brain-computer interfaces, with recent publications exploring topics such as minimally informed linear discriminant analysis and scalable EEG sensor networks. He is actively engaged in developing innovative techniques for decoding neural responses and enhancing biomedical signal analysis through distributed architectures.

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Collaborators (4)

Jonathan Dan

EPFL (École Polytechnique Fédérale de Lausanne)

SWITZERLAND

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Tinne Tuytelaars

professor

KU Leuven

BELGIUM

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Benjamin Vandendriessche

Case Western Reserve University

UNITED STATES

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Maarten Schelles

Chief Technology Officer

ReVision Implant

BELGIUM

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Alexander Bertrand

Research Interests

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About

Articles (26)

Minimally informed linear discriminant analysis: Training an LDA model with unlabelled data

Unsupervised Accuracy Estimation for Brain–Computer Interfaces Based on Selective Auditory Attention Decoding

A Wireless, Scalable, and Modular EEG Sensor Network Platform for Unobtrusive Brain Recordings

A Distributed Neural Network Architecture for Dynamic Sensor Selection With Application to Bandwidth-Constrained Body-Sensor Networks

Distributed Adaptive Signal Fusion for Fractional Programs

Distributed Adaptive Spatial Filtering With Inexact Local Solvers

Distributed Blind Source Separation Based on FastICA

EEG-Based Decoding of Selective Visual Attention in Superimposed Videos

Identifying temporal correlations between natural single-shot videos and EEG signals

Avoiding Post-Processing With Event-Based Detection in Biomedical Signals

‘Are you even listening?’ - EEG-based decoding of absolute auditory attention to natural speech

Neuronal tuning and population representations of shape and category in human visual cortex

What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention

A Human-in-the-Loop Method for Annotation of Events in Biomedical Signals

Editorial: Special Issue on the ICASSP 2023 Signal Processing Grand Challenges

Stimulus-Informed Generalized Canonical Correlation Analysis for Group Analysis of Neural Responses to Natural Stimuli

Sensor selection and miniaturization limits for detection of interictal epileptiform discharges with wearable EEG

Bandwidth-Efficient Distributed Neural Network Architectures With Application to Neuro-Sensor Networks

A Unified Algorithmic Framework for Distributed Adaptive Signal and Feature Fusion Problems — Part II: Convergence Properties

A Unified Algorithmic Framework for Distributed Adaptive Signal and Feature Fusion Problems—Part I: Algorithm Derivation

Time-Adaptive Unsupervised Auditory Attention Decoding Using EEG-Based Stimulus Reconstruction

Spatially Variant Ultrasound Attenuation Mapping Using a Regularized Linear Least-Squares Approach

Objective evaluation of stimulation artefact removal techniques in the context of neural spike sorting

Electrocardiogram Quality Assessment Using Unsupervised Deep Learning

MAXVAR-Based Distributed Correlation Estimation in a Wireless Sensor Network

EEG miniaturization limits for stimulus decoding with EEG sensor networks

Collaborators (4)

Jonathan Dan

Tinne Tuytelaars

Benjamin Vandendriessche

Maarten Schelles

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