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Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases

Publication Type : Journal Article

Publisher : Springer Science and Business Media LLC

Source : Scientific Reports

Url : https://doi.org/10.1038/s41598-025-93047-9

Campus : Chennai

School : School of Computing

Department : Computer Science and Engineering

Year : 2025

Abstract : Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD). The features extracted from the signals were trained with a hybrid Feed Forward Neural Network Cheetah Optimization Algorithm (FFNNCOA) in both offline and online modes. Totally, eighteen subjects were involved in this study. The study proved that the offline analysis phase outperformed than the online phase in the real-time. The experiment was achieved the accuracies of 95.56% and 93.88% for men and female respectively. Furthermore, the study confirms that the subject’s performance in the offline can manage the task more easily than in online mode.

Cite this Research Publication : Ramkumar Sivasakthivel, Manikandan Rajagopal, G. Anitha, K. Loganathan, Mohamed Abbas, Amel Ksibi, Koppula Srinivas Rao, Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases, Scientific Reports, Springer Science and Business Media LLC, 2025, https://doi.org/10.1038/s41598-025-93047-9

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