Drowsiness detection using EEG signals using DL

[KamakshiOjha]

Pull Request for DL-Simplified 💡

Issue Title : Drowsiness detection using EEG signals using DL

• Info about the related issue (Aim of the project) : This project aims to implement a drowsiness detection system using deep learning techniques, comparing the performance of a convolutional neural network (CNN) with a Random Forest classifier for binary classification tasks.
  ✅ To be Mentioned while taking the issue :
• Full name : Kamakshi Ojha
• GitHub Profile Link : https://github.com/KamakshiOjha
• Email ID : [email protected]
• What is your participant role? (Mention the Open Source program) GSSOC

Closes: #784

Describe the add-ons or changes you've made 📃

1. New Model Implementation: I have implemented a CNN architecture specifically tailored for processing EEG signals. This model consists of convolutional layers followed by max-pooling layers to extract relevant features from the EEG data.
2. Training and Evaluation: I have trained the new model on a dataset of EEG signals labeled with drowsiness states. After training, I evaluated the model's performance using appropriate metrics such as accuracy, precision, recall, and F1-score.
3. Documentation Updates: I have updated the project documentation to include details about the new model, its architecture, and usage instructions. This ensures that other developers can easily understand and utilize the new addition to the project.

Type of change ☑️

What sort of change have you made:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested? ⚙️

Describe how it has been tested - use of 3 different type of models to test which one has the best accuracy.

Checklist: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

[github-actions]

Our team will soon review your PR. Thanks @KamakshiOjha :)

[KamakshiOjha]

I have added the files, can you please review the work?

[KamakshiOjha]

I have added the files, can you please review the work?

[abhisheks008]

Looks good to me.

Approved @KamakshiOjha