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脑机接口（BCI）；脑电图（EEG）；中文拼写器；英文拼写器 1

脑电图（EEG）；运动想象；改进的共同空间模式（B-CSP）；特征提取；分类 1

脑电图；情感识别；注意力机制；人格特征 1

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Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent

Bang-hua YANG,Liang-fei HE,Lin LIN,Qian WANG

《信息与电子工程前沿（英文）》 2015年第16卷第6期页码 486-496 doi: 10.1631/FITEE.1400299

摘要： Ocular artifacts cause the main interfering signals within electroencephalogram (EEG) signal measurements. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject. To remove ocular artifacts from EEG in brain-computer interfaces (BCIs), a method named spatial constraint independent component analysis based recursive least squares (SCICA-RLS) is proposed. The method consists of two stages. In the first stage, independent component analysis (ICA) is used to decompose multiple EEG channels into an equal number of independent components (ICs). Ocular ICs are identified by an automatic artifact detection method based on kurtosis. Then empirical mode decomposition (EMD) is employed to remove any cerebral activity from the identified ocular ICs to obtain exact artifact ICs. In the second stage, first, SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal. These extracted ICs are called spatial constraint ICs (SC-ICs). Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference, which avoids the need for parallel EOG recordings. In addition, the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA. Based on the EEG data recorded from seven subjects, the new approach can lead to average classification accuracies of 3.3% and 12.6% higher than those of the standard ICA and raw EEG, respectively. In addition, the proposed method has 83.5% and 83.8% reduction in time-consumption compared with the standard ICA and ICA-RLS, respectively, which demonstrates a better and faster OA reduction.

关键词： Ocular artifacts Electroencephalogram (EEG) Electrooculogram (EOG) Brain-computer interface (BCI) Spatial constraint independent component analysis based recursive least squares (SCICA-RLS)

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EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application

《医学前沿（英文）》 2021年第15卷第5期页码 740-749 doi: 10.1007/s11684-020-0794-5

摘要： Stroke is one of the most serious diseases that threaten human life and health. It is a major cause of death and disability in the clinic. New strategies for motor rehabilitation after stroke are undergoing exploration. We aimed to develop a novel artificial neural rehabilitation system, which integrates brain--computer interface (BCI) and functional electrical stimulation (FES) technologies, for limb motor function recovery after stroke. We conducted clinical trials (including controlled trials) in 32 patients with chronic stroke. Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation (NMES) group. The changes in outcome measures during intervention were compared between groups, and the trends of ERD values based on EEG were analyzed for BCI-FES group. Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity (FMA-UE) and Kendall Manual Muscle Testing (Kendall MMT) scores of the BCI-FES group was significantly higher than that in the sham group, which indicated the practicality and superiority of the BCI-FES system in clinical practice. The change in the laterality coefficient (LC) values based on μ-ERD (ΔLC_m_-ERD) had high significant positive correlation with the change in FMA-UE(r= 0.6093, P=0.012), which provides theoretical basis for exploring novel objective evaluation methods.

关键词： brain–computer interface functional electrical stimulation electroencephalogram laterality coefficient chronic stroke

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一种面向机器情感智能的人格引导型情感脑机接口 Research Article

李少杰,李伟,邢泽健,袁文杰,韦香玉,张晓炜,胡斌

《信息与电子工程前沿（英文）》 2022年第23卷第8期页码 1158-1173 doi: 10.1631/FITEE.2100489

摘要：然而，由于脑电图（electroencephalogram, EEG）信号的复杂性和情绪反应的个体差异性，设计一个可靠和有效的模型仍然是一个巨大挑战。

关键词：脑电图；情感识别；注意力机制；人格特征

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面向脑机接口基于改进的共同空间模式方法的单次运动想象脑电分类 Research Articles

Zhi-chuan TANG, Chao LI, Jian-feng WU, Peng-cheng LIU, Shi-wei CHENG

《信息与电子工程前沿（英文）》 2019年第20卷第8期页码 1087-1098 doi: 10.1631/FITEE.1800083

摘要：单次运动想象脑电分类常用于脑机接口系统控制，是人-机之间的沟通桥梁。然而，脑电信号具有低信噪比和个性化差异，会对分类结果产生不利影响。本文提出一种改进的共同空间模式（B-CSP）方法，提取特征并消除负面影响。首先，针对不同被试，采用巴氏距离并基于事件相关去同步（ERD）和事件相关同步（ERS）模式选择每个电极通道的最优频率段；其次，采用B-CSP方法提取最优频率段脑电信号特征，获得可以最大程度区分两类运动想象的特征。采用所提方法对公共数据集和实验数据集提取特征，并结合反向传播神经网络进行单次运动想象脑电分类。将B-CSP方法与两种传统脑电特征提取方法——原始共同空间模式（CSP）和自回归（AR）——比较。采用B-CSP方法在公共数据集的表现（左手/双脚：91.25%±1.77%；左手/右手：84.50%±5.42%）和实验数据集的表现（左手/双脚：90.43%±4.26%）均优于两种传统方法。实验结果表明，本文所提方法能够有效分类运动想象脑电，并能对脑机接口系统开发提供实践和理论基础。

关键词：脑电图（EEG）；运动想象；改进的共同空间模式（B-CSP）；特征提取；分类

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基于脑电图中文拼音输入系统的脑机接口综述 None

Ming-hui SHI, Chang-le ZHOU, Jun XIE, Shao-zi LI, Qing-yang HONG, Min JIANG, Fei CHAO, Wei-feng REN, Xiang-qian LIU, Da-jun ZHOU, Tian-yu YANG

《信息与电子工程前沿（英文）》 2018年第19卷第3期页码 423-436 doi: 10.1631/FITEE.1601509

摘要：基于脑电图（EEG）的脑机接口能够让用户通过脑电信号与外部环境通信，而不必依赖于肌肉等大脑信号的常规输出路径。基于EEG的拼写器是EEG的重要应用，可将脑电信号转换为拼写特定字符或词的意图，从而帮助重度残疾患者（如肌萎缩侧索硬化症患者，ALS患者）。近年来，基于EEG的英文拼写器（EEGES）已得到较广泛研究，而基于脑电的中文拼写器（EEGCS）则研究甚少。由于英文只包含26个字母，而中文包含11000多个图形字符，因此，EEGCS比EEGES更难开发。本文旨在对EEGCS系统的相关文献进行综述，首先，对当前EEGCS系统进行了系统地分类，为后续讨论奠定基础；然后，提出统一当前EEGCS和EEGES的通用系统架构，其中将基于EEG的选择作为核心部件；最后，对当前各种EEGCS系统进行综述和讨论，指出EEGCS研究的当前进展、存在的问题和未来方向。

关键词：脑机接口（BCI）；脑电图（EEG）；中文拼写器；英文拼写器

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标题作者时间类型操作

Fast removal of ocular artifacts from electroencephalogram signals using spatial constraint independent

Bang-hua YANG,Liang-fei HE,Lin LIN,Qian WANG

期刊论文

EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application

期刊论文

一种面向机器情感智能的人格引导型情感脑机接口

李少杰,李伟,邢泽健,袁文杰,韦香玉,张晓炜,胡斌

期刊论文

面向脑机接口基于改进的共同空间模式方法的单次运动想象脑电分类