I am a Postdoctoral Researcher at the Centre for Human Brain Health, University of Birmingham (United Kingdom) working in the field of neurophysiology and congnitive neuroscience. I am interested in cognition (e.g., vision, attention, predictions), open science, programming and data science in general. My work makes uses of advances neurophysiological techniques (EEG, MEG, TMS, eye-tracking) and data analysis methods (time-frequency, connectivity, statistics) to better understand how the human brain and mind work.
- Neuronal effects of predictions based on statistical learning on visual attention (repository)
What we experienced in the past affects how we perceive the external world in the future. For example, an annoying flashing light might be better ignored if we know in advance where it usually appears. This ability of extracting regularities from the environment is called statistical learning. In this project, we explore the neuronal mechanisms allowing the attentional system to overlook items that are unequivocally distracting based on their spatial distribution. By recording brain activity using magnetoencephalography (MEG) while probing neural excitability with a novel technique called rapid invisible frequency tagging (RIFT), we show that the neuronal excitability in early visual cortex is reduced in advance of stimulus presentation for locations where distracting items are more likely to occur.
- COGITATE: an adversarial collaboration on the brain correlates of consciousness ([repository] coming soon)
One of the biggest unresolved questions in neuroscience is the so-called “hard problem” of consciousness: How does a conscious subjective experience like the pleasant feeling of the sun on the skin arise from bioelectric signals processed by billions of neurons in the brain? Many theories have been proposed to unravel this mystery, some collecting robust empirical support and leading to clear and testable predictions. However, the lack of crosstalk between the theoretical frameworks have produced incompatible models and predictions. How can we determine which theory better explain the neuronal substrate of consciousness? The idea behind the Cogitate consortium, funded by the Templeton World Charity Foundation, is to compare two leading theories of consciousness by identifying the most diagnostic points of separation and then design experiments that directly test the contracting predictions. By embracing this adversarial collaborative framework, experiments have been developed with, and endorsed by, the theories’ proponents. Relying on adversarial collaborations, multimodal brain imaging, open science practices as well as international team science, this initiative aims to accelerate research on consciousness whilst promoting good scientific practices.
- FLUX: open-source pipeline for analysing magnetoencephalography (MEG) data (repository)
MEG allows for quantifying modulations of human neuronal activity on a millisecond time scale while also making it possible to estimate the location of the underlying neuronal sources. The technique relies heavily on signal processing and source modelling. To this end, there are several open-source toolboxes developed by the community. While these toolboxes are powerful as they provide a wealth of options for analyses, the many options also pose a challenge for reproducible research as well as for researchers new to the field. The FLUX pipeline aims to make the analyses steps and setting explicit for standard analysis done in cognitive neuroscience. It focuses on quantifying and source localization of oscillatory brain activity, but it can also be used for event-related fields and multivariate pattern analysis. Specifically, the pipeline including documented code is defined for MNE-Python and FieldTrip, and a data set on visuospatial attention is used to illustrate the steps. The scripts are provided as notebooks implemented in Jupyter Notebook and MATLAB Live Editor providing explanations, justifications and graphical outputs for the essential steps. Furthermore, we also provide suggestions for text and parameter settings to be used in registrations and publications to improve replicability and facilitate pre-registrations.