A Fusion Classification Prototypical for Eye State Recognition in Stroke Patients Using Electroencephalogram (EEG) Data

Authors

  • R. S. Ernest Ravindran Department of Electronics & Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India
  • Yathish Aradhya B. C. Dept of ISE, Kalpataru Institute of Technology, India
  • A. Senthil Kumar Sri Vishnu Engineering College for Women, Andhra Pradesh India
  • T. R. Vijaya Lakshmi Department of Electronics & Communication Engineering, Mahatma Gandhi Institute Of Technology, India
  • Sugasri Sureshkumar Department of neurology, Faculty of Physiotherapy, Meenakshi Academy of higher Education and Research, Chennai, India
  • Syed Abudaheer Kajamohideen School of Physiotherapy, AIMST University, Malaysia

Keywords:

BCI, EEG, CI, sleep-waking patterns

Abstract

The electroencephalography (EEG) signal is a crucial part of Brain-Computer Interface (BCI) technology. Simply put, the BCI is a non-muscular channel for information transfer between the brain and other devices. The primary goal of BCIs is to restore some level of social interaction for those who are unable to use their mouths or hands because of neurological impairments. Classification of EEG signals is essential for many uses, including imaging of motor imagery, diagnosis of pharmacological effects, identification of emotions, prediction of seizures, detection of eye states, and many others. As a result, the construction of an autonomous solution in the medical arena necessitates a powerful classification model capable of efficiently processing the EEG information. Accurate diagnosis of an eye disease using EEG data is a challenging but essential task in medicine and daily life. The fundamental goal of this study is to develop a hybrid model based on machine learning that improves the accuracy with which the ocular status of stroke patients may be detected from EEG data. It can aid in finding and eliminating anomalies, and it can help in developing the robotic or smart machine-based answer to societal problems. To determine its usefulness and accuracy, this hybrid categorization model was compared to state-of-the-art machine learning methods. The experimental analysis proves that the suggested hybrid classification model outperforms the competition. The suggested hybrid model outperforms the state-of-the-art on every test and validation metric.

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Published

11.07.2023

How to Cite

Ravindran, R. S. E. ., B. C., Y. A. ., Kumar, A. S. ., Lakshmi, T. R. V. ., Sureshkumar, S. ., & Kajamohideen, S. A. . (2023). A Fusion Classification Prototypical for Eye State Recognition in Stroke Patients Using Electroencephalogram (EEG) Data. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 499–507. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3080

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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