A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhanc...A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhance the effectiveness of the Infomax ICA decomposition. After the mixed signal was decomposed by Infomax ICA, the independent component(IC) used in auditory P300 reconstruction was automatically chosen by using the standard deviation of the fixed temporal pattern. And the result of auditory P300 was reconstructed using the selected ICs. The experimental results show that the auditory P300 can be detected automatically within five trials. The Pearson correlation coefficient between the standard signal and the signal detected using the proposed method is significantly greater than that between the standard signal and the signal detected using the average method within five trials. The wave pattern result obtained using the proposed algorithm is better and more similar to the standard signal than that obtained by the average method for the same number of trials. Therefore, the proposed method can automatically detect the effective auditory P300 within several trials.展开更多
针对卷积神经网络(CNN)在感受野有限、缺乏对全局信息的有效感知,以及在处理短时稳态运动视觉诱发电位(SSMVEP)信号时分类效果欠佳的问题,提出了一种紧凑EEGNet-Transformer(即EEGNetformer)网络。EEGNetformer网络融合了为脑电(EEG)信...针对卷积神经网络(CNN)在感受野有限、缺乏对全局信息的有效感知,以及在处理短时稳态运动视觉诱发电位(SSMVEP)信号时分类效果欠佳的问题,提出了一种紧凑EEGNet-Transformer(即EEGNetformer)网络。EEGNetformer网络融合了为脑电(EEG)信号识别任务而设计的通用的卷积神经网络EEGNet网络和Transformer网络的优势,有效地捕捉与处理脑电信号中的局部和全局信息,增强网络对SSMVEP特征的学习,进而实现良好的解码性能。EEGNet网络用于提取SSMVEP的局部时间和空间特征,而Transformer网络用于捕捉脑电时间序列的全局信息。在基于SSMVEP-BCI范式采集的数据基础上,开展了实验以评估EEGNetformer网络的性能。实验结果显示,当在2 s SSMVEP数据条件下,EEGNetformer网络在基于被试者内情况的平均准确率为88.9%±6.6%,在基于跨被试者情况的平均准确率为69.1%±4.3%。与传统的CNN算法相比,EEGNetformer网络的分类性能提升了4.2%~17.4%。研究内容说明,EEGNetformer网络在有效提高SSMVEP-BCI识别准确率方面具有显著优势,为进一步提升SSMVEP-BCI解码性能提供了新的研究思路。展开更多
稳态视觉诱发电位(Steady State Visual Evoked Potential,SSVEP)凭借其信噪比高、信息传输率高等优点成为脑控技术主流范式之一。对SSVEP信号的特征识别和特征提取算法是SSVEP系统研究的关键问题,但目前研究中适用于SSVEP算法的综述较...稳态视觉诱发电位(Steady State Visual Evoked Potential,SSVEP)凭借其信噪比高、信息传输率高等优点成为脑控技术主流范式之一。对SSVEP信号的特征识别和特征提取算法是SSVEP系统研究的关键问题,但目前研究中适用于SSVEP算法的综述较少。针对此问题,总结近年来适用于SSVEP机器学习算法,从机器学习的角度将算法分为无监督学习和有监督学习,介绍典型相关分析、卷积神经网络等算法的原理和适用范围。总结当前SSVEP算法在实际应用中的不足之处,并讨论SSVEP所面临的机遇与挑战。展开更多
特定型语言障碍(specific language impairment,SLI)患者智力正常,却在语言理解和表达方面存在问题,目前其病因尚无定论,仅几个潜在病因正被探讨,其中该群体听觉信息处理能力与其语言问题的关系备受关注。越来越多的研究利用电生理检测...特定型语言障碍(specific language impairment,SLI)患者智力正常,却在语言理解和表达方面存在问题,目前其病因尚无定论,仅几个潜在病因正被探讨,其中该群体听觉信息处理能力与其语言问题的关系备受关注。越来越多的研究利用电生理检测技术探讨SLI患者的听觉信息处理能力,并积累了一定的证据。本研究从听性脑干反应、中潜伏期反应和长潜伏期反应几个方面对SLI患者听觉信息处理能力的实证研究进行综述,尝试厘清SLI患者在听觉信息处理方面的特征,并分析其与语言问题的关系,以期为SLI患者的诊治提供参考。展开更多
基金Projects(81460273,61265006)supported by the National Natural Science Foundation of ChinaProject(2013GXNSFAA019325)supported by Guangxi Natural Science Foundation,ChinaProject(1348020-10)supported by Guangxi Science and Technology Program,China
文摘A method was demonstrated based on Infomax independent component analysis(Infomax ICA) for automatically extracting auditory P300 signals within several trials. A signaling equilibrium algorithm was proposed to enhance the effectiveness of the Infomax ICA decomposition. After the mixed signal was decomposed by Infomax ICA, the independent component(IC) used in auditory P300 reconstruction was automatically chosen by using the standard deviation of the fixed temporal pattern. And the result of auditory P300 was reconstructed using the selected ICs. The experimental results show that the auditory P300 can be detected automatically within five trials. The Pearson correlation coefficient between the standard signal and the signal detected using the proposed method is significantly greater than that between the standard signal and the signal detected using the average method within five trials. The wave pattern result obtained using the proposed algorithm is better and more similar to the standard signal than that obtained by the average method for the same number of trials. Therefore, the proposed method can automatically detect the effective auditory P300 within several trials.
文摘针对卷积神经网络(CNN)在感受野有限、缺乏对全局信息的有效感知,以及在处理短时稳态运动视觉诱发电位(SSMVEP)信号时分类效果欠佳的问题,提出了一种紧凑EEGNet-Transformer(即EEGNetformer)网络。EEGNetformer网络融合了为脑电(EEG)信号识别任务而设计的通用的卷积神经网络EEGNet网络和Transformer网络的优势,有效地捕捉与处理脑电信号中的局部和全局信息,增强网络对SSMVEP特征的学习,进而实现良好的解码性能。EEGNet网络用于提取SSMVEP的局部时间和空间特征,而Transformer网络用于捕捉脑电时间序列的全局信息。在基于SSMVEP-BCI范式采集的数据基础上,开展了实验以评估EEGNetformer网络的性能。实验结果显示,当在2 s SSMVEP数据条件下,EEGNetformer网络在基于被试者内情况的平均准确率为88.9%±6.6%,在基于跨被试者情况的平均准确率为69.1%±4.3%。与传统的CNN算法相比,EEGNetformer网络的分类性能提升了4.2%~17.4%。研究内容说明,EEGNetformer网络在有效提高SSMVEP-BCI识别准确率方面具有显著优势,为进一步提升SSMVEP-BCI解码性能提供了新的研究思路。
文摘稳态视觉诱发电位(Steady State Visual Evoked Potential,SSVEP)凭借其信噪比高、信息传输率高等优点成为脑控技术主流范式之一。对SSVEP信号的特征识别和特征提取算法是SSVEP系统研究的关键问题,但目前研究中适用于SSVEP算法的综述较少。针对此问题,总结近年来适用于SSVEP机器学习算法,从机器学习的角度将算法分为无监督学习和有监督学习,介绍典型相关分析、卷积神经网络等算法的原理和适用范围。总结当前SSVEP算法在实际应用中的不足之处,并讨论SSVEP所面临的机遇与挑战。
文摘特定型语言障碍(specific language impairment,SLI)患者智力正常,却在语言理解和表达方面存在问题,目前其病因尚无定论,仅几个潜在病因正被探讨,其中该群体听觉信息处理能力与其语言问题的关系备受关注。越来越多的研究利用电生理检测技术探讨SLI患者的听觉信息处理能力,并积累了一定的证据。本研究从听性脑干反应、中潜伏期反应和长潜伏期反应几个方面对SLI患者听觉信息处理能力的实证研究进行综述,尝试厘清SLI患者在听觉信息处理方面的特征,并分析其与语言问题的关系,以期为SLI患者的诊治提供参考。
