通过优化特征工程，显著提升脑机接口在运动想象识别中的准确率。
Robust Feature Engineering Techniques for Designing Efficient Motor Imagery-Based BCI-Systems
- 从16通道脑电信号中提取时域、频域和小波特征，用MRMR筛选关键特征。
- 采用高斯核SVM分类器，运动想象识别准确率达95.48%，超越此前74.36%的最高纪录。
- 方法简单高效，适合资源有限的神经康复场景使用。
全球有大量人群受运动功能障碍困扰。利用脑机接口（BCI）技术的神经假体有望改善康复效果。然而，脑电图（EEG）数据的复杂性仍是当前BCI系统的主要挑战。尽管近期发布了涵盖上下肢运动及运动想象任务的高质量EEG信号数据集，但基于该数据集训练的机器学习模型表现不佳，评估框架亦显不足。为此，本文引入稳健的特征工程方法：从16通道EEG信号中提取时域、频域与小波特征，并采用最大相关最小冗余（MRMR）方法筛选出四个最具代表性的特征。在此基础上，使用K近邻（KNN）、支持向量机（SVM）、决策树（DT）和朴素贝叶斯（NB）进行分类，以测试精度、精确率、召回率和F1分数为评估指标。结果显示，采用高斯核的SVM模型在运动活动识别上达到92.50%的测试准确率，在运动想象任务上达95.48%，远超前人研究最高74.36%的水平。本研究深入分析了MI Limb EEG数据集，为设计低成本、高效且可靠的神经康复用脑机接口系统提供可行路径。
原文摘要 · Abstract (English)
A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bayes (NB) models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provides an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation.
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