为探索神经反馈训练在提升射击表现方面的应用效果和训练过程中的无应答者特性,开展一项用于提升射击表现的神经反馈训练(neurofeedback training for sport performance,SP-NFT)实验研究,招募20名受试者,进行2周4次的“巅峰”范式SP-N...为探索神经反馈训练在提升射击表现方面的应用效果和训练过程中的无应答者特性,开展一项用于提升射击表现的神经反馈训练(neurofeedback training for sport performance,SP-NFT)实验研究,招募20名受试者,进行2周4次的“巅峰”范式SP-NFT,采集受试者前、后测隐显目标射击表现和相关脑电(electroencephalograph,EEG)数据,检验SP-NFT对射击表现的提升效果、静息态EEG特征、SP-NFT期间正常组和无应答组EEG特性变化情况。结果表明:受试者后测射击成绩显著高于前测(P<0.01),静息态theta频带功率显著降低(P<0.01);相对正常受试者,无应答者在SP-NFT期间的努力程度更高,theta频段功率和SMR功率的变化程度更低,SP-NFT能够有效提升受试者射击表现,进一步揭示了无应答者的相关生理机制。研究成果为用于提升射击表现的SP-NFT技术进一步发展提供理论支撑和实验证据。展开更多
Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly...Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.展开更多
文摘为探索神经反馈训练在提升射击表现方面的应用效果和训练过程中的无应答者特性,开展一项用于提升射击表现的神经反馈训练(neurofeedback training for sport performance,SP-NFT)实验研究,招募20名受试者,进行2周4次的“巅峰”范式SP-NFT,采集受试者前、后测隐显目标射击表现和相关脑电(electroencephalograph,EEG)数据,检验SP-NFT对射击表现的提升效果、静息态EEG特征、SP-NFT期间正常组和无应答组EEG特性变化情况。结果表明:受试者后测射击成绩显著高于前测(P<0.01),静息态theta频带功率显著降低(P<0.01);相对正常受试者,无应答者在SP-NFT期间的努力程度更高,theta频段功率和SMR功率的变化程度更低,SP-NFT能够有效提升受试者射击表现,进一步揭示了无应答者的相关生理机制。研究成果为用于提升射击表现的SP-NFT技术进一步发展提供理论支撑和实验证据。
基金the Fundamental Research Funds for the Central Universities(GrantNo.IR2021222)received by J.Sthe Future Science and Technology Innovation Team Project of HIT(216506)received by Q.W.
文摘Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.
