Drowsiness Detection using EEG signals

[sreevidya-16]

Is your feature request related to a problem? Please describe.

Drowsiness detection using EEG signals addresses the critical need to identify and mitigate the risks associated with reduced alertness during activities such as driving or operating heavy machinery. EEG, which measures electrical activity in the brain, offers a non-invasive method to monitor changes in cognitive states, including alertness and drowsiness.

@SrijanShovit, Could you please assign me this issue under GSSOC'24

Describe the solution you'd like along with reference dataset.

Objectives:

• Develop a system to detect drowsiness using Electroencephalogram (EEG) signals, which reflect brain activity and can indicate different states of alertness.
• Implement real-time monitoring to alert users or operators when signs of drowsiness are detected.

Implementation Details:

1. Data Collection: Gather EEG data from individuals in controlled settings to capture brainwave patterns associated with alertness and drowsiness.

2. Preprocessing: Clean and preprocess EEG signals to enhance signal quality and remove artifacts for accurate analysis.

3. Feature Extraction: Extract relevant features from EEG signals that correlate with drowsiness, such as alpha and theta wave frequencies.

4. Model Development: Utilize machine learning algorithms, such as Support Vector Machines (SVM) or Deep Neural Networks (DNN), to classify EEG patterns and detect drowsiness states.

5. Real-time Detection: Integrate the trained model into a real-time monitoring system that continuously analyzes incoming EEG signals to detect onset or progression of drowsiness.

DATASET - https://figshare.com/articles/dataset/EEG_driver_drowsiness_dataset/14273687

Code of Conduct

• I agree to follow this project's Code of Conduct

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