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

Ramkumar Sivasakthivel, Manikandan Rajagopal, G Anitha, K Loganathan, Mohamed Abbas, Amel Ksibi, Koppula Srinivas Rao

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

Advanced internet of things enhanced activity recognition for disability people using deep learning model with nature-inspired optimization algorithms.Scientific reports · 2025
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7 authors.

Ramkumar SivasakthivelDepartment of Computer Science, School of Sciences, Christ University, Bengaluru, Karnataka, India.

Manikandan RajagopalDepartment of Lean Operations and Systems, School of Business and Management, Christ University, Bengaluru, Karnataka, India.

G AnithaDepartment of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, Tamil Nadu, India.

K LoganathanDepartment of Mathematics and Statistics, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India. loganathan.karuppusamy@jaipur.manipal.edu.

Mohamed AbbasCentral Labs, King Khalid University, P.O. Box 960, AlQura'a, Abha, Saudi Arabia.

Amel KsibiDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Koppula Srinivas RaoDepartment of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AlgorithmsBrain-Computer InterfacesNeurodegenerative DiseasesAdultElectroencephalographyFemaleHumansMaleMiddle AgedNeural Networks, ComputerBrain-Computer interfaceCheetah optimization algorithmCommon Spatial patternLower motor neuronRadial basis functionUpper motor neuronWelch power spectral density

Identifiers

PMID40089573

DOI10.1038/s41598-025-93047-9

PMCPMC11910560

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.