AuditoryEEG

Source code for Speech Understanding course minor project based on Auditory EEG match mismatch prediction problem.

All 64 channels of EEG after ICA

Modules

1. dataset.py

The dataset.py module is a part of the project's data processing pipeline. It provides classes and functions for handling datasets containing EEG recordings along with corresponding event information and audio stimuli.

• Data Loading: The Dataset class in this module facilitates the loading of EEG recordings and associated event information from specified directories within the dataset.

• Data Processing: It offers methods for preprocessing EEG data, such as extracting features using techniques like wavelet transforms, and extracting features from audio signals using techniques like MFCC (Mel-Frequency Cepstral Coefficients).

• Dataset Preparation: The prepare_dataset method prepares a dataset by loading a specified number of samples, extracting features, and organizing them into arrays suitable for machine learning tasks.

To use dataset.py, you can import it into your project and create an instance of the Dataset class. You can then utilize its methods for loading, preprocessing, preparing, and splitting datasets for EEG-based machine learning tasks.

2. evaluator.py

The evaluator.py module is a project evaluation pipeline component. It provides classes and functions for evaluating machine learning models on datasets processed by the dataset.py module.

• Model Evaluation: The Evaluator class in this module is designed to evaluate the performance of various machine learning models on datasets prepared using the Dataset class from the dataset.py module.

• Model Selection: It allows for evaluating models such as Random Forest Classifier (rfc) and Logistic Regression (lr) on the dataset. Additional models can be easily integrated into the evaluation process.

To utilize evaluator.py, you need to have a prepared dataset instance from the Dataset class. After importing the Evaluator class into your project, you can create an instance of it, passing the dataset as a parameter. You can then call the get_evaluation method, optionally specifying a list of models to evaluate. This method returns a dictionary containing the evaluation results for each model.

from .dataset import Dataset
from .evaluator import Evaluator

# Create dataset instance
dataset = Dataset()

# Create evaluator instance
evaluator = Evaluator(dataset)

# Get evaluation results
evaluation_results = evaluator.get_evaluation(model_list=['rfc', 'lr'])

3. feature_extractor.py

The feature_extractor.py module is a component of the project's data processing pipeline, specifically focusing on feature extraction from EEG recordings and audio signals.

EEG Feature Extraction:

• Short-Time Fourier Transform (STFT) Features:
  • The stft_features function computes STFT features from EEG data after applying Independent Component Analysis (ICA) for dimensionality reduction.
• Wavelet Features:
  • The wavelet_features function computes wavelet-based features from EEG data, including energy, entropy, mean, and standard deviation at different levels.

Audio Feature Extraction:

• Mel-Frequency Cepstral Coefficients (MFCC) Features:
  • The mfcc_features function computes MFCC features from audio signals.

To utilize feature_extractor.py, you can import it into your project. Depending on your data, you can choose the appropriate function for feature extraction.

from .feature_extractor import EEG_Features, Audio_Features

# Example usage for EEG feature extraction
eeg_data = ...  # EEG data as numpy array
stft_feats = EEG_Features.stft_features(eeg_data)
wavelet_feats = EEG_Features.wavelet_features(eeg_data)

# Example usage for audio feature extraction
audio_signal = ...  # Audio signal as numpy array
mfcc_feats = Audio_Features.mfcc_features(audio_signal)

4. test.py

The test.py script is designed to run evaluations on machine learning models using the Evaluator class from evaluator.py and datasets from the Dataset class in dataset.py.

To use test.py, you can simply run the script. It will print out the evaluation results in a pandas DataFrame format, specifically evaluating the Random Forest Classifier (rfc) model by default.

python test.py