BACKGROUND Mild cognitive impairment(MCI)is a high-risk precursor to Alzheimer’s disease characterized by declining memory or other progressive cognitive functions without compromising daily living abilities.AIM To i...BACKGROUND Mild cognitive impairment(MCI)is a high-risk precursor to Alzheimer’s disease characterized by declining memory or other progressive cognitive functions without compromising daily living abilities.AIM To investigate the efficacy of repetitive transcranial magnetic stimulation(rTMS)in patients with MCI.METHODS This retrospective analysis involved 180 patients with MCI who were admitted to The First Hospital of Shanxi Medical University from January 2021 to June 2023.Participants were allocated into the research(n=98,receiving rTMS)and control groups(n=82,receiving sham stimulation).Memory tests,cognitive function assessments,event-related potential–P300 tests,and electroencephalogram(EEG)examinations were conducted pre-treatment and post-treatment.Further,memory quotient(MQ),cognitive function scores,and EEG grading results were compared,along with adverse reaction incidences.RESULTS Pre-treatment MQ scores,long-term and short-term memory,as well as immediate memory scores,demonstrated no notable differences between the groups.Post-treatment,the research group exhibited significant increases in MQ scores,long-term memory,and short-term memory compared to baseline(P<0.05),with these improvements being statistically superior to those in the control group.However,immediate memory scores exhibited no significant change(P>0.05).Further,the research group demonstrated statistically better post-treatment scores on the Revised Wechsler Memory Scale than the control group.Furthermore,post-treatment P300 latency and amplitude improved significantly in the research group,surpassing the control group.EEG grading in the research group improved,and the incidence of adverse reactions was significantly lower than in the control group.CONCLUSION Patients with MCI receiving rTMS therapy demonstrated improved memory and cognitive functions and EEG grading and exhibited high safety with fewer adverse reactions.展开更多
Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that...Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.展开更多
Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fund...Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.展开更多
情绪识别是人机交互(HCI)与情感智能领域的重要前沿课题之一。然而,目前基于脑电(EGG)信号的情绪识别方法主要提取静态特征,无法挖掘情绪的动态变化特性,难以提升情绪识别能力。在基于EGG构建动态脑功能网络的研究中,常采用滑动窗口方法...情绪识别是人机交互(HCI)与情感智能领域的重要前沿课题之一。然而,目前基于脑电(EGG)信号的情绪识别方法主要提取静态特征,无法挖掘情绪的动态变化特性,难以提升情绪识别能力。在基于EGG构建动态脑功能网络的研究中,常采用滑动窗口方法,通过依次构建不同窗口内的功能连接网络以形成动态网络。但该方法存在主观设定窗长的问题,无法提取每个时间点情绪状态的连接模式,导致时间信息丢失和脑连接信息不完整。针对上述问题,提出动态线性相位测量(dyPLM)方法,该方法无需使用滑窗,即可自适应地在每个时间点构建情绪相关脑网络,更精准地刻画情绪的动态变化特性。此外,还提出一种卷积门控神经网络(CNGRU)情绪识别模型,该模型可进一步提取动态脑网络深层次特征,有效提高情绪识别准确性。在公开情绪识别脑电数据集DEAP(Database for Emotion Analysis using Physiological signals)上进行验证,所提方法四分类准确率高达99.71%,较MFBPST-3D-DRLF提高3.51百分点。在SEED(SJTU Emotion EEG Dataset)数据集上进行验证,所提方法三分类准确率达到99.99%,较MFBPST-3D-DRLF提高3.32百分点。实验结果证明了所提出的动态脑网络构建方法dyPLM和情绪识别模型CNGRU的有效性和实用性。展开更多
背景:近年来随着脑机接口技术的发展,它在脑卒中康复过程中的疗效已得到证实,并取得了丰富成果,亟需进行可视化分析以了解研究前沿与热点。目的:应用文献计量学可视化软件分析脑机接口在脑卒中康复领域应用的前沿热点及研究趋势。方法:...背景:近年来随着脑机接口技术的发展,它在脑卒中康复过程中的疗效已得到证实,并取得了丰富成果,亟需进行可视化分析以了解研究前沿与热点。目的:应用文献计量学可视化软件分析脑机接口在脑卒中康复领域应用的前沿热点及研究趋势。方法:以Web of Science核心合集与中国知网数据库作为研究基础,利用Citespace 6.4.1、VOSviewer 1.6.20和Excel 2021工具对检索所得的与脑机接口技术在脑卒中功能恢复中应用相关的中英文相关文献进行可视化数据分析,通过科学计量手段深入剖析脑机接口技术在脑卒中康复领域的研究现状、热点议题及未来趋势。结果与结论:①共纳入2003-2025年中英文文献985篇(英文879篇,中文106篇),该领域国内外年发文量均持续增长;②中国、美国与德国是该领域年发文量最多的国家;该领域最具影响力的机构为德国图宾根大学,中文发文量最高的机构为复旦大学附属华山医院;瑞士的《FRONTIERS IN NEUROSCIENCE》是英文发文量最高的期刊,《中国康复医学杂志》为中文发文量最高的期刊;英文发文量最高的作者为德国的Birbaumer Niels,中文发文量最高的作者为贾杰;③文献分析可见,国际研究侧重理论与临床效果的验证,且关注上肢功能与神经的恢复;国内研究更关注技术与系统的优化与开发,侧重康复领域应用的广泛探索;④运动想象为中英文文献共同的高频关键词,研究热点聚焦在基于脑电图、运动想象的脑机接口系统开发;⑤多模态结合、人工智能融合、康复手段拓展及国际合作深化可能是该领域未来发展的主要趋势。展开更多
文摘BACKGROUND Mild cognitive impairment(MCI)is a high-risk precursor to Alzheimer’s disease characterized by declining memory or other progressive cognitive functions without compromising daily living abilities.AIM To investigate the efficacy of repetitive transcranial magnetic stimulation(rTMS)in patients with MCI.METHODS This retrospective analysis involved 180 patients with MCI who were admitted to The First Hospital of Shanxi Medical University from January 2021 to June 2023.Participants were allocated into the research(n=98,receiving rTMS)and control groups(n=82,receiving sham stimulation).Memory tests,cognitive function assessments,event-related potential–P300 tests,and electroencephalogram(EEG)examinations were conducted pre-treatment and post-treatment.Further,memory quotient(MQ),cognitive function scores,and EEG grading results were compared,along with adverse reaction incidences.RESULTS Pre-treatment MQ scores,long-term and short-term memory,as well as immediate memory scores,demonstrated no notable differences between the groups.Post-treatment,the research group exhibited significant increases in MQ scores,long-term memory,and short-term memory compared to baseline(P<0.05),with these improvements being statistically superior to those in the control group.However,immediate memory scores exhibited no significant change(P>0.05).Further,the research group demonstrated statistically better post-treatment scores on the Revised Wechsler Memory Scale than the control group.Furthermore,post-treatment P300 latency and amplitude improved significantly in the research group,surpassing the control group.EEG grading in the research group improved,and the incidence of adverse reactions was significantly lower than in the control group.CONCLUSION Patients with MCI receiving rTMS therapy demonstrated improved memory and cognitive functions and EEG grading and exhibited high safety with fewer adverse reactions.
文摘Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.
文摘Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.
文摘情绪识别是人机交互(HCI)与情感智能领域的重要前沿课题之一。然而,目前基于脑电(EGG)信号的情绪识别方法主要提取静态特征,无法挖掘情绪的动态变化特性,难以提升情绪识别能力。在基于EGG构建动态脑功能网络的研究中,常采用滑动窗口方法,通过依次构建不同窗口内的功能连接网络以形成动态网络。但该方法存在主观设定窗长的问题,无法提取每个时间点情绪状态的连接模式,导致时间信息丢失和脑连接信息不完整。针对上述问题,提出动态线性相位测量(dyPLM)方法,该方法无需使用滑窗,即可自适应地在每个时间点构建情绪相关脑网络,更精准地刻画情绪的动态变化特性。此外,还提出一种卷积门控神经网络(CNGRU)情绪识别模型,该模型可进一步提取动态脑网络深层次特征,有效提高情绪识别准确性。在公开情绪识别脑电数据集DEAP(Database for Emotion Analysis using Physiological signals)上进行验证,所提方法四分类准确率高达99.71%,较MFBPST-3D-DRLF提高3.51百分点。在SEED(SJTU Emotion EEG Dataset)数据集上进行验证,所提方法三分类准确率达到99.99%,较MFBPST-3D-DRLF提高3.32百分点。实验结果证明了所提出的动态脑网络构建方法dyPLM和情绪识别模型CNGRU的有效性和实用性。
文摘背景:近年来随着脑机接口技术的发展,它在脑卒中康复过程中的疗效已得到证实,并取得了丰富成果,亟需进行可视化分析以了解研究前沿与热点。目的:应用文献计量学可视化软件分析脑机接口在脑卒中康复领域应用的前沿热点及研究趋势。方法:以Web of Science核心合集与中国知网数据库作为研究基础,利用Citespace 6.4.1、VOSviewer 1.6.20和Excel 2021工具对检索所得的与脑机接口技术在脑卒中功能恢复中应用相关的中英文相关文献进行可视化数据分析,通过科学计量手段深入剖析脑机接口技术在脑卒中康复领域的研究现状、热点议题及未来趋势。结果与结论:①共纳入2003-2025年中英文文献985篇(英文879篇,中文106篇),该领域国内外年发文量均持续增长;②中国、美国与德国是该领域年发文量最多的国家;该领域最具影响力的机构为德国图宾根大学,中文发文量最高的机构为复旦大学附属华山医院;瑞士的《FRONTIERS IN NEUROSCIENCE》是英文发文量最高的期刊,《中国康复医学杂志》为中文发文量最高的期刊;英文发文量最高的作者为德国的Birbaumer Niels,中文发文量最高的作者为贾杰;③文献分析可见,国际研究侧重理论与临床效果的验证,且关注上肢功能与神经的恢复;国内研究更关注技术与系统的优化与开发,侧重康复领域应用的广泛探索;④运动想象为中英文文献共同的高频关键词,研究热点聚焦在基于脑电图、运动想象的脑机接口系统开发;⑤多模态结合、人工智能融合、康复手段拓展及国际合作深化可能是该领域未来发展的主要趋势。
