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Eye State Classification with EEG using Machine Learning

Author(s) Alavala Vinay, Allacheruvu Srikanth, Abburi Venkata Rao, Ms. G. Priyanka, Dr. R. Shoba Rani, Dr. M. Manikandan
Country India
Abstract Through machine learning to monitor eye statuses and set off an alarm as appropriate, this project focuses on creating a real-time classification system for eye states. The main goal is to create a device able to distinguish between open and closed eyes on the fly using image data, track the length of time the eyes remain open, and warn should the eyes be constantly open for more than one minute. For accurate classification of eye states, the system uses OpenCV for real-time video input and eye detection in addition to a trained Convolutional Neural Network(CNN). While the model examines and predicts the state of the
eyes, haar cascades are used for localizing the eye region. The project also combines an alarm system utilising Python libraries to sound an audible alert once particular conditions are met. This solution is meant to be pertinent for situations when long periods of eye openness could indicate weariness or reduced attention, such as driver drowsiness detection, user attention monitoring, and so on. Developed in Python, the system uses machine learning frameworks including TensorFlow/Keras and OpenCV for efficient real-time processing.
Keywords EEG, Machine Learning, Eye State Detection, Convolutional Neural Networks, OpenCV, Keras, HaarCaascade, TensorFlow, long short-term memory, Support Vector Machines, Linear Discriminant Analysis , k-Nearest Neighbors
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-31
Cite This Eye State Classification with EEG using Machine Learning - Alavala Vinay, Allacheruvu Srikanth, Abburi Venkata Rao, Ms. G. Priyanka, Dr. R. Shoba Rani, Dr. M. Manikandan - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.2555
DOI https://doi.org/10.71097/IJSAT.v16.i1.2555
Short DOI https://doi.org/g9dgp8
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