Arthur Dehgan

MEEGNet is a Python library for applying convolutional neural networks to MEG data, achieving high accuracy in classification tasks while offering interpretability tools.

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.

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