Author

A. Bertrand

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

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Bioengineering· 4

Signature Assays, Organisms & Methods

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Assay:electrophysiology· 4 Approach:statistical model· 3 Approach:experimental assay· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1

Frequent Co-Authors

N. Das· 2 shared T. Francart· 2 shared A. Mundanad Narayanan· 1 shared A. Stebner· 1 shared H. Van hamme· 1 shared J. Zegers· 1 shared P. Patrinos· 1 shared S. Geirnaert· 1 shared

Activity by Year

2018-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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2 shared papersBioengineering

N. Das

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4 papers

Recent·Most cited

Identifying Temporal Correlations Between Natural Single-shot Videos and EEG Signals

Y. Yao, A. Stebner, T. Tuytelaars, S. Geirnaert, A. Bertrand

The paper identifies temporal correlations between natural single-shot videos and EEG signals, showing that object-based features outperform traditional video features in capturing these correlations.

bioRxiv · Sep 19, 2023Bioengineering

Optimal versus approximate channel selection methods for EEG decoding with application to topology-constrained neuro-sensor networks

A. Mundanad Narayanan, P. Patrinos, A. Bertrand

This paper quantifies the optimality gap between approximate and optimal channel selection methods for EEG decoding and demonstrates topology-constrained electrode placement using MIQP.

bioRxiv · Oct 2, 2020Bioengineering

Linear versus deep learning methods for noisy speech separation for EEG-informed attention decoding

N. Das, J. Zegers, H. Van hamme, T. Francart, A. Bertrand

This paper compares linear and deep learning methods for speech separation in EEG-informed attention decoding, finding linear methods competitive and DNNs beneficial when combined with beamformers.

bioRxiv · Jan 22, 2020Bioengineering

EEG-based auditory attention detection: boundary conditions for background noise and speaker positions

N. Das, A. Bertrand, T. Francart

This paper investigates how background noise and speaker positions affect EEG-based auditory attention decoding accuracy in a cocktail party scenario.

Journal of Neural Engineering · Oct 9, 2018Peer-reviewedBioengineering