Machine learning for physical motion identification using EEG signals: a comparative study of classifiers and hyperparameter tuning

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-03-11 DOI:10.1007/s12652-024-04764-4
Poh Foong Lee, Kah Yoon Chong
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Abstract

This study addresses the crucial task of accurately classifying brainwave signals associated with distinct brain states, utilizing five supervised machine learning algorithms: K-Nearest Neighbors, Support Vector Machine, Decision Tree, Linear Discriminant Analysis, and Logistics Regression. The primary objectives encompass developing and optimizing these models, assessing the impact of hyperparameter tuning on performance through metrics like accuracy, consistency, and prediction time, and creating a user-friendly web-based deployment interface. The Decision Tree model emerges with the highest average accuracy score of 90.03%, swift prediction times, and notable consistency. Following hyperparameter tuning, SVM and LR showcase substantial accuracy gains (15.63% and 1.50% respectively), enhancing all models' consistency. KNN and SVM are identified as the top-performing algorithms for accurate brain state classification. This study's findings hold implications for neuroscience research, human–computer interaction, healthcare diagnostics, and assistive technologies, offering insights into both effective algorithm selection and the role of hyperparameter tuning in optimizing model performance.

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利用脑电信号进行物理运动识别的机器学习:分类器和超参数调整的比较研究
本研究利用五种有监督的机器学习算法,完成了对与不同大脑状态相关的脑电波信号进行准确分类的重要任务:K-近邻、支持向量机、决策树、线性判别分析和物流回归。主要目标包括开发和优化这些模型,通过准确性、一致性和预测时间等指标评估超参数调整对性能的影响,以及创建一个用户友好的基于网络的部署界面。决策树模型的平均准确率最高,达到 90.03%,预测时间最短,一致性也很好。经过超参数调整后,SVM 和 LR 的准确率大幅提高(分别为 15.63% 和 1.50%),增强了所有模型的一致性。KNN 和 SVM 被认为是准确进行大脑状态分类的最佳算法。本研究的发现对神经科学研究、人机交互、医疗诊断和辅助技术具有重要意义,为有效的算法选择和超参数调整在优化模型性能方面的作用提供了启示。
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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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