Fine-tuned LLM for Electroencephalography(EEG) data classification

This is LLM Fine tuning model that classifies four movements (left arm, right arm, tongue, foot) from EEG.

• LLM performs its own classification operations based on EEG data.
• We trained gpt-4o model utilizing fine-tuning for better performance.

Requirements

Python>=3.8, openai>=1.30.2, mne>=1.6.1
You can install all libraries entering the code:

!pip install -r requirements.txt

Data

Data description : https://www.bbci.de/competition/iii/desc_IIIa.pdf

• cued motor imagery (multi-class) with 4 classes (left hand, right hand, foot, tongue) three subjects (ranging from quite good to fair performance)
• EEG, 60 channels, 60 trials per class
• performance measure: kappa-coefficient

Download : BBCI Competition III (https://www.bbci.de/competition/iii/download/index.html?agree=yes&submit=Submit)

Features

For feature selection and extraction, Fisher Ratio is utilized.

Evaluation

To compare fine-tuned LLM classifier's performance with traditional ML models, we additionally trained SVM, RF and MLP in the same data and same preprocessing method.

Performance metrics:

• Accuracy
• F1 score
• ROC-AUC

(Metrics plot will be here)