Yannick Roy

Yannick Roy
Université de Montréal | UdeM · School of Optometry

About

7
Publications
3,078
Reads
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617
Citations
Citations since 2016
7 Research Items
616 Citations
2016201720182019202020212022050100150200
2016201720182019202020212022050100150200
2016201720182019202020212022050100150200
2016201720182019202020212022050100150200
Additional affiliations
January 2014 - June 2014
McGill University
Position
  • Master's Student

Publications

Publications (7)
Article
Visual working memory (VWM) allows us to actively store, update, and manipulate visual information surrounding us. While the underlying neural mechanisms of VWM remain unclear, contralateral delay activity (CDA), a sustained negativity over the hemisphere contralateral to the positions of visual items to be remembered, is often used to study VWM. T...
Preprint
Full-text available
Our ability to track multiple objects in a dynamic environment enables us to perform everyday tasks such as driving, playing team sports, and walking in a crowded mall. Despite more than three decades of literature on multiple object tracking (MOT) tasks, the underlying and intertwined neural mechanisms remain poorly understood. Here we looked at t...
Preprint
Full-text available
Visual working memory (VWM) allows us to actively store, update and manipulate visual information surrounding us. While the underlying neural mechanisms of VWM remain unclear, contralateral delay activity (CDA), a sustained negativity over the hemisphere contralateral to the positions of visual items to be remembered, is often used to study VWM. To...
Article
Full-text available
Context: Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representati...
Preprint
Full-text available
Electroencephalography (EEG) is a complex signal and can require several years of training to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditiona...
Chapter
EEGLAB, a widely used toolbox in MATLAB (The Mathworks, Inc.), uses Independent Component Analysis (ICA) to decompose the EEG signal into sub-signals, and localizes brain sources of those sub-signals prior to independent component (IC) clustering for group study. In 2013, the Measure Projection Toolbox (MPT) was introduced as a new data-driven IC c...

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