This repository is part of the coursework for the Advanced Cognitive Neuroscience course at Aarhus University. It centers around a sophisticated analysis of MEG and fMRI brain scans, applying thought-reading experiments data obtained in collaboration with Aarhus Skejby Hospital, Denmark. The analysis employs cutting-edge techniques to decipher brain activity patterns related to cognitive tasks.
The analysis is based on anonymized MEG and fMRI brain scans. Due to ethical and privacy concerns, the dataset is anonymized and not publicly available.
rafs_17_Nilearn_faceWord_classification_searchlight_group.ipynb: This Jupyter Notebook delves into the classification of face vs. word recognition tasks using Nilearn, focusing on a group-level searchlight analysis. The notebook illustrates the methodological steps and insights drawn from the brain scans.
To explore this analysis:
- Clone the repository.
- Ensure your environment has the necessary Python packages installed (
nilearn,numpy,scipy,matplotlib). - Open
rafs_17_Nilearn_faceWord_classification_searchlight_group.ipynbin JupyterLab or Jupyter Notebook to view and run the analysis.
This project is a part of the AU university course assignment. Contributions are welcome from course participants and collaborating researchers.
The content is provided for the educational and research purposes. Ethical and privacy considerations must be respected, especially regarding data access and use.
I thank Aarhus Skejby Hospital for the data and acknowledge the support from instructors and peers in the Advanced Cognitive Neuroscience course at Aarhus University.