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使用混合猎豹优化算法为神经退行性疾病患者模拟在线和离线任务。

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

作者信息

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

机构信息

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

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

出版信息

Sci Rep. 2025 Mar 15;15(1):8951. doi: 10.1038/s41598-025-93047-9.

DOI:10.1038/s41598-025-93047-9
PMID:40089573
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11910560/
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.

摘要

脑机接口（BCI）是一种为闭锁综合征（LIS）患者提供更好通信系统的通用技术。在当前十年中，对神经退行性疾病患者改善护理和服务的需求不断增长。为克服这一障碍，当前的工作设计了针对瘫痪患者的四种脑机接口状态，使用韦尔奇功率谱密度（W-PSD）。从信号中提取的特征在离线和在线模式下都采用混合前馈神经网络猎豹优化算法（FFNNCOA）进行训练。本研究共涉及18名受试者。研究证明，离线分析阶段在实时性方面优于在线阶段。该实验男性和女性的准确率分别达到了95.56%和93.88%。此外，研究证实，受试者在离线状态下比在线模式下更能轻松地完成任务。

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