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Neuro Load : Mental Workload Assessment using GSR

Project Duration: May 2023 - June 2023

Neuro Load is a research project that introduces a novel approach to categorizing mental workload using Galvanic Skin Response (GSR). The project focuses on leveraging advanced signal processing techniques and machine learning to assess mental workload effectively.

Project Overview

Mental workload is a critical factor in understanding cognitive processes, particularly in environments where optimal performance is essential. This project aimed to provide a new method for categorizing mental workload through the analysis of GSR signals.

Technologies and Techniques Explored

  • Python: The project was implemented in Python, taking advantage of its robust libraries for signal processing and machine learning.
  • Chirp Z Transform (CZT): Used for frequency analysis of the GSR signals, providing high-resolution spectral analysis.
  • Continuous Wavelet Transform (CWT): Applied for time-frequency analysis, enabling the identification of transient features in the GSR signals.
  • Transfer Learning: Employed to enhance the model's ability to generalize across different datasets and improve the accuracy of mental workload classification.

Key Features

1. Signal Processing

  • Feature Engineering: Extracted meaningful features from the GSR signals using CZT and CWT to capture both frequency and time-domain characteristics.
  • Data Preprocessing: Applied various signal processing techniques to clean and normalize the GSR data before analysis.

2. Machine Learning

  • Model Development: Developed models using transfer learning techniques to classify mental workload into different categories.
  • Performance Evaluation: Assessed the models using metrics such as accuracy, precision, and recall to ensure robust performance.

3. Research Contribution

  • Novel Approach: Introduced a new method for mental workload assessment that combines advanced signal processing with machine learning.
  • Potential Applications: The findings from this research can be applied in fields like cognitive neuroscience, human-computer interaction, and occupational health.

Installation

To set up the project on your local machine, follow these steps:

  1. Clone the repository:

    git clone https://github.com/krishnaura45/NeuroLoad.git
cd NeuroLoad

  2. Install required dependencies:

    pip install -r requirements.txt

  3. Run the Jupyter Notebooks:

    • Start by exploring the feature engineering notebook (nl_part1_feature_engg.ipynb).
    • Continue with the transfer learning notebook (nlpart2_transfer_learning.ipynb).

Usage

  1. Feature Engineering:

    • Load the GSR dataset.
    • Apply CWT to extract features.
    • Visualize the signal transformations.

  2. Transfer Learning:

    • Train the model using preprocessed features.
    • Fine-tune the model using transfer learning.
    • Evaluate the model's performance on test data.

Example

Here's a snippet to illustrate how to apply CZT on GSR data:

import numpy as np
from scipy.signal import chirp, find_peaks, peak_widths

# Example GSR data
gsr_signal = np.array([your_signal_data_here])

# Apply Chirp Z Transform
czt_signal = chirp(gsr_signal, f0=0.1, f1=1.0, t1=10, method='linear')

Contributing

We welcome contributions to enhance Neuro Load. Please follow these steps to contribute:

  1. Fork the repository.
  2. Create a new branch: git checkout -b feature/your-feature-name.
  3. Make your changes and commit them: git commit -m 'Add some feature'.
  4. Push to the branch: git push origin feature/your-feature-name.
  5. Open a pull request.

REFERENCES

  1. https://pubmed.ncbi.nlm.nih.gov/22778631/
  2. https://ieeexplore.ieee.org/document/5663259
  3. https://www.researchgate.net/publication/221532890
  4. https://ieee-dataport.org/open-access/maus
  5. https://pub.towardsai.net/feature-scaling
  6. https://www.kaggle.com/code/samsonlo/resnet-50
  7. https://arxiv.org/pdf/1611.06455.pdf 
  8. https://github.com/nachi-hebbar/TL-ResNet
  9. https://github.com/codebasics/deep-learning-keras-tf-tutorial