Etienne Combrisson

Sensorimotor encoding of epistemic value during goal-directed causal learning

The paper investigates how the brain encodes epistemic value during goal-directed causal learning, showing that expected information gain is represented in sensorimotor regions and challenges traditional views of reward-based valuation.

Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning

This study shows that information gain in goal-directed learning is encoded through synergistic and higher-order functional interactions across cortical circuits.

Neural interactions in the human frontal cortex dissociate reward and punishment learning

The paper identifies distinct neural interaction subsystems in the human frontal cortex for reward and punishment learning using intracranial recordings.

Neural shape mediation analysis

This study introduces a model to analyze neural signal shapes and identifies brain regions mediating behavioural processes, finding that depletion periods, not amplitude, correlate with behavioural speed.

Local field potentials in human motor and non-motor brain areas encode the direction of upcoming movements: An intracerebral EEG classification study

This study uses intracerebral EEG to show that motor cortex activity encodes hand movement direction during both planning and execution, with different frequency features involved in each phase.

Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data

This paper evaluates the impact of class imbalance on ML performance in neuroscience, showing that Accuracy is misleading and recommending Balanced Accuracy and Random Forest with stratified cross-validation.

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

The paper introduces a non-parametric permutation-based framework for group-level analysis of cognitive brain networks using neurophysiological data and presents an open-source Python toolbox called Frites.

Tensorpac : an open-source Python toolbox for tensor-based Phase-Amplitude Couplingmeasurement in electrophysiological brain signals

Tensorpac provides an open-source Python toolbox for efficient and comprehensive phase-amplitude coupling analysis in electrophysiological brain signals.

Decoding the neural dynamics of oculomotor decision making in humans

This study uses intracerebral EEG recordings in humans to show that sustained high gamma activity in fronto-parietal cortex distinguishes free choice from instructed saccades during a delayed oculomotor decision task.

NeuroPycon: An open-source Python toolbox for fast multi-modal and reproducible brain connectivity pipelines

NeuroPycon provides a Python toolkit for reproducible brain connectivity analysis by integrating neuroimaging tools into a unified framework with parameter files for replication.

Estimation of narrowband amplitude and phase from electrophysiology signals for phase-amplitude coupling studies: a comparison of methods

This study compares Hilbert and wavelet methods for estimating phase-amplitude coupling in electrophysiological signals using simulated and experimental MEG data, finding Hilbert performs better in certain contexts.