To investigate the effects of magnetic stimulation at acupoints on brain functional network during mental fatigue, magnetic stimulation was applied to stimulate SHENMEN (HT7), HEGU (LI4) and LAOGONG (PC8) acupoint in ...To investigate the effects of magnetic stimulation at acupoints on brain functional network during mental fatigue, magnetic stimulation was applied to stimulate SHENMEN (HT7), HEGU (LI4) and LAOGONG (PC8) acupoint in this paper. The brain functional networks of normal state, mental fatigue state and stimulated state were constructed and the characteristic parameters were comparatively studied based on the complex network theory. The results showed that the connection of the network was enhanced by stimulating the HT7, LI4 and PC8 acupoint. In conclusion, magnetic stimulation at acupoints can effectively relieve mental fatigue.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
文摘To investigate the effects of magnetic stimulation at acupoints on brain functional network during mental fatigue, magnetic stimulation was applied to stimulate SHENMEN (HT7), HEGU (LI4) and LAOGONG (PC8) acupoint in this paper. The brain functional networks of normal state, mental fatigue state and stimulated state were constructed and the characteristic parameters were comparatively studied based on the complex network theory. The results showed that the connection of the network was enhanced by stimulating the HT7, LI4 and PC8 acupoint. In conclusion, magnetic stimulation at acupoints can effectively relieve mental fatigue.
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。
