脑电图信号分析用于人类活动分类：一种基于机器学习和运动想象的解决方案
Electroencephalography Signal Analysis for Human Activities Classification: A Solution Based on Machine Learning and Motor Imagery
AI总结：
本研究提出基于随机森林的机器学习方法，利用消费级和研究级脑电图系统对真实及想象运动进行分类，有效区分活动类型与身体部位，但受人际变异性影响。
AI中文摘要：
脑电图（EEG）是理解与人类运动活动相关的大脑电活动的基本工具。脑机接口（BCI）利用此类电活动来开发辅助技术，尤其是面向身体残疾人士的技术。然而，提取信号特征和模式仍然复杂，有时需交由机器学习（ML）算法处理。因此，本研究旨在开发一种基于随机森林算法的机器学习方法，以对执行真实和想象运动活动的受试者的脑电图信号进行分类。脑电图信号的解释和正确分类使得开发由认知过程控制的工具成为可能。我们使用消费级和研究级脑电图系统评估了我们的机器学习随机森林算法。随机森林能够有效区分想象活动和真实活动，并确定相关的身体部位，即使使用消费级脑电图也是如此。然而，脑电图信号的人际变异性对分类过程产生了负面影响。
英文摘要：
Electroencephalography (EEG) is a fundamental tool for understanding the brain's electrical activity related to human motor activities. Brain-Computer Interface (BCI) uses such electrical activity to develop assistive technologies, especially those directed at people with physical disabilities. However, extracting signal features and patterns is still complex, sometimes delegated to machine learning (ML) algorithms. Therefore, this work aims to develop a ML based on the Random Forest algorithm to classify EEG signals from subjects performing real and imagery motor activities. The interpretation and correct classification of EEG signals allow the development of tools controlled by cognitive processes. We evaluated our ML Random Forest algorithm using a consumer and a research-grade EEG system. Random Forest efficiently distinguishes imagery and real activities and defines the related body part, even with consumer-grade EEG. However, interpersonal variability of the EEG signals negatively affects the classification process.
