https://github.com/codedby-mozz/song_familiarity_eeg
Independent EEG analysis of neural responses to familiar vs. unfamiliar songs using public data and MNE-Python. A self-driven project focused on EEG preprocessing, ERPs, and auditory familiarity.
https://github.com/codedby-mozz/song_familiarity_eeg
eeg-analysis mne-python neuroscience python
Last synced: about 1 month ago
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Independent EEG analysis of neural responses to familiar vs. unfamiliar songs using public data and MNE-Python. A self-driven project focused on EEG preprocessing, ERPs, and auditory familiarity.
- Host: GitHub
- URL: https://github.com/codedby-mozz/song_familiarity_eeg
- Owner: codedby-mozz
- License: mit
- Created: 2025-05-20T10:56:12.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-07-18T14:55:37.000Z (11 months ago)
- Last Synced: 2025-07-18T19:12:16.538Z (11 months ago)
- Topics: eeg-analysis, mne-python, neuroscience, python
- Language: Jupyter Notebook
- Homepage:
- Size: 10.4 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# EEG Study on Song Familiarity
This repository documents an **independent EEG analysis** project, driven by self-learning, that explores neural responses to familiar vs. unfamiliar melodies.
> 🧠 **Status**: In progress
> 👩🔬 **Purpose**: Personal skill development in EEG analysis and MNE-Python
> 📚 **Data Source**: Jared R. Girard, Aaron M. Bishop, and Cameron D. Hassall (2025). *Song Familiarity*. OpenNeuro.[Doi: 10.18112/openneuro.ds005876.v1.0.1](https://doi.org/10.18112/openneuro.ds005876.v1.0.1)
---
## 🧪 Study Overview
The goal is to investigate potential neural markers of auditory familiarity using EEG event-related potentials (ERPs). Specifically, this analysis is focused on answering these questions:
- What are the EEG correlates of the moment a song becomes familiar?
- How does behavioral recognition (accuracy and response time) relate to ERP components or spectral EEG activity?
- Can we detect neural patterns predictive of correct vs incorrect identification?
⚠️ **Note:** This is not an official research project. It is a personal exploration of EEG methods and tools, based on public data.
---
## 🚧 Current Progress
- [x] Preprocessed EEG data for a single participant
- [x] Applied artifact rejection (AutoReject)
- [x] Epoched data around labeled note events
- [x] Re-referenced and baseline-corrected the data
- [x] Preprocess EEG data for all participants
- [x] Manual inspection of epochs - Preprocessing was largely automated. I manually reviewed each participant's data to identify any remaining bad epochs or problematic channels. If any significant issues are found, I will revisit and adjust the preprocessing steps accordingly.
- [x] Reassess preprocessing steps - Some channels for certain participants were too noisy or completely "dead".
- [x] Finish preprocessing
- [ ] Group-level ERP analysis
- [ ] Statistical comparison of conditions
- [ ] Training an AI on EEG data to classify songs as familiar or unfamiliar
---
## 🧰 Tools & Libraries
- Python
- [MNE](https://mne.tools/stable/index.html)
- [MNE_bids](https://mne.tools/mne-bids/stable/index.html)
- [Matplotlib](https://matplotlib.org)
- [Pandas](https://pandas.pydata.org)
- [Autoreject](https://autoreject.github.io/stable/index.html)
- [Numpy](https://numpy.org)
---
## 📌 Why I'm Doing This
This project supports my self-guided learning in cognitive neuroscience and EEG analysis, aiming to:
- Deepen understanding of EEG preprocessing pipelines
- Gain hands-on experience with the MNE-Python
- Explore neural markers of auditory familiarity
- Improve scientific coding and visualization skills
---
## 📝 Feedback Welcome!
This project is part of my effort to gain hands-on experience with EEG data. While I have an academic background in cognitive neuroscience, most of what I’ve learned about working with EEG has come from self-study, online resources, and community forums—as it wasn’t a major focus in my coursework.
Because of that, some of the approaches I’ve taken might be suboptimal or overlook more established solutions. If you have any feedback, suggestions, or deeper insights into EEG analysis, I’d genuinely love to hear from you! Whether it's a quick comment or a technical deep-dive, your input would be incredibly valuable as I continue to learn and grow in this area.
---
## 🧠 Data Acknowledgment
The EEG data used in this project is sourced from **OpenNeuro**, a platform for open neuroimaging datasets. This analysis is based on one of the publicly available datasets, used here strictly for educational purposes.
Dataset: Song Familiarity
DOI: [10.18112/openneuro.ds005876.v1.0.1](https://doi.org/10.18112/openneuro.ds005876.v1.0.1)
---
### ✨ Author
This repository is maintained by Andy Hendrikx, an MSc graduate in Cognitive & Clinical Neuroscience.
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