This project investigates the neural representation of AI-generated hyper-realistic faces compared to real human faces. Despite behavioral difficulty in distinguishing them, our goal is to use Machine Learning and Deep Learning to classify brain responses (ERP) recorded via EEG.
- P.I.: Prof. Alice Mado Proverbio (Univ. Milano-Bicocca)
- Supervision: Prof. Claudia Casellato (Univ. Pavia)
- Team: Pablo Rimoldi, Tommaso Godino, Andrea De Paola, Giacomo Colombo
- Data Source: 128-channel EEG (10/5% system), 512 Hz sampling rate.
- Stimuli: 440 male/female faces (Real vs. GAN-generated).
- Signal Processing:
- Bandpass filter: 0.01-70 Hz.
- Notch filter: 50 Hz.
- Epochs: -100 ms to 800 ms.
- Reference: Common Average Reference (CAR).
We aim to classify EEG trials into 4 categories:
- Code 50: AI Male
- Code 60: AI Female
- Code 70: Real Male
- Code 80: Real Female
Focus on the 200-600 ms time window and specific electrodes:
O1, O2, PO9, PO10, TP7, TP8, P3, P4, AF3, AF4, AFF1h, AFF2h, AFF3h, AFF4h.
- clone repository
git clone https://github.com/Pablo-Rimoldi/neural-classification-artificial-faces
cd neural-classification-artificial-faces- Install dependencies:
pip install -r requirements.txt- Run cleaning:
python src/data_cleaner.py