Lucas Jeay-Bizot

Section overview

This section provides an overview of the research I have and am currently conducting as part of my PhD.

If you are a prospective undergraduate research assistant look to the left tab for projects labelled as open for prospective RAs.

Feel free to reach out via email if you have any questions about these projects.

An investigation of the impact of baseline correction on response-locked potentials

Authors: Lucas Jeay-Bizot, Uri Maoz and Aaron Schurger

Summary: we investigate the impact of baseline correction, a standard EEG pre-processing step, on response-locked potentials.

Neuroscience tools: EEG

Computational tools: Meta-Analysis, data simulations

Conferences: 25th annual meeting of the Association for the Scientific Study of Consciousness, Amsterdam, Netherlands (talk, 2022)

An investigation of the neural spontaneity of spontaneous decisions

Authors: Lucas Jeay-Bizot, Amy Whitmarsh, Uri Maoz and Aaron Schurger

Summary: we propose to investigate whether spontaneous decisions of what to do or whether to act are also spontaneous in the brain. We apply machine learning to EEG data to uncover the timecourse of classifier performance with respect to the time of the decision (of either what to do or whether to act).

Neuroscience tools: EEG, EMG, EOG

Computational tools: Common Spatial Patterns Linear Discriminant Analysis, Support Vector Machines, AdaBoost

Conferences: The 26th Annual Meeting of the Association for the Scientific Study of Consciousness, New York, USA (poster, 2023)

An investigation of the onset of intentions

Authors: Lucas Jeay-Bizot, Ryan Guglielmo, Tomáš Dominik, Uri Maoz and Aaron Schurger

Summary: Using a novel approach we measure the onset of intentions.

An investigation of the cortical sources of self-initiated actions using brain stimulation

Authors: Lucas Jeay-Bizot , Uri Maoz and Aaron Schurger

Summary: we propose to investigate the role of M1 and pre/SMA in generate self-initiated actions and their corresponding brain activity. Following a promising set of pilot data, we are proposing a registered report study where all methods will be peer-reviewed prior to the final data collection.

Neuroscience tools: EEG, fMRI, MRI, TMS, Theta-burst stimulation

Computational tools: Bayesian statistics

Publication: Stage I registered report in review

Conferences: The 4th Annual Meeting of the Neurophilosophy of Free Will Consortium, Sigtuna, Sweden (talk, 2023)

The time course of neural activity predictive of impending movement

Authors: Lucas Jeay-Bizot , Mehmet Basbug , Uri Maoz , Robert Schapire and Aaron Schurger

Summary: we investigated the timecourse of classifiability of two types of EEG data, EEG data that preceeds a self-initiated movement versus a matched control with no movement. The point of interest being how early can we tell apart such data with respect to the timing of the movement. In theory when the brain data begins to reflect the decision to move, the two classes should become separable and therefore classifiable. We found that upon taking this classificatory approach despite high classification performace at time of movement, early classification performance was poor.

Neuroscience tools: EEG, MEG

Computational tools: AdaBoost with Haar wavelets

Preprint: link

Publication: in review

No evidence for the modulation of the readiness potential by respiratory phase

Authors: Lucas Jeay-Bizot , Raniyah Chishti, Uri Maoz and Aaron Schurger

Summary: we investigated the relationship between self-initiated movements, brain activity and respiration. We reproduced results from a recent study regarding a coupling between behavior and respiration but did not confirm their other result of a coupling between brain activity and respiration. We explained this discrepancy by identifying a confounder.

Neuroscience tools: EEG, respiratory belt

Computational tools: Phase amplitude coupling analysis

Publication: submitted