Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.
运动想象指想象特定动作但实际上并不执行该动作的行为，已经在神经科学等领域得到广泛关注。运动想象脑电信号分类算法主要根据脑电信号所包含的生理信息，尤其是从生理信息中提取出的特征，对各类运动想象任务进行区分。近年来，运动想象脑电信号分类算法在分类器与机器学习策略两方面出现了一些新的研究进展。分类器方面，一些研究对传统机器学习分类器进行了改进，深度学习与黎曼几何分类器也已在该领域得到广泛应用。机器学习策略方面，出于提高分类准确率等目的，集成学习、自适应学习与迁移学习等机器学习策略被引入到运动想象脑电信号的分类中。本文综述讨论了运动想象脑电信号分类算法的研究进展，希望能够对各分类器与机器学习策略进行总结评价，为开发更高性能的分类算法提供思路。.
Keywords: classifiers; machine learning strategies; motor imagery electroencephalogram.