文章通过丽莎·萨默对一名抑郁症和强迫症患者的个体音乐心理治疗案例的治疗过程,诠释了她在使用邦尼音乐引导想象的个体音乐治疗实践中发现其局限性,从而尝试调整治疗技术,最终发展成为由两种方式:音乐想象(Music&Imagery,缩写...文章通过丽莎·萨默对一名抑郁症和强迫症患者的个体音乐心理治疗案例的治疗过程,诠释了她在使用邦尼音乐引导想象的个体音乐治疗实践中发现其局限性,从而尝试调整治疗技术,最终发展成为由两种方式:音乐想象(Music&Imagery,缩写为MI)和音乐引导想象(Guided Imagery and Music,缩写为GIM),分别在三个层次(支持性、再教育性和重构性)进行实践的六种方法所组成的音乐引导想象连续模式的治疗技术。展开更多
If there is no imagination, there is no music appreciation. It should create imaginary world for students in the music classroom teaching practice, and it should foster the students＇ musical imagination. Thus, collea...If there is no imagination, there is no music appreciation. It should create imaginary world for students in the music classroom teaching practice, and it should foster the students＇ musical imagination. Thus, colleagues can make discussion about strategies proposed including pilot background, context led, screen hygiene conditions and others. We use Cognitive Linguistic Theories to introduce idealized cognitive model and its theoretical basis and the intensified impact on student musical imagination.展开更多
Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,th...Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively.In this paper,we propose an improved common spatial pattern(B-CSP)method to extract features for alleviating these adverse effects.First,for different subjects,the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization(ERD)and event-related synchronization(ERS)patterns;then the signals of the optimal frequency band are decomposed into spatial patterns,and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data.The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network(BPNN)classifier to classify single-trial MI EEG.Another two conventional feature extraction methods,original common spatial pattern(CSP)and autoregressive(AR),are used for comparison.An improved classification performance for both data sets(public data set:91.25%±1.77%for left hand vs.foot and84.50%±5.42%for left hand vs.right hand;experimental data set:90.43%±4.26%for left hand vs.foot)verifies the advantages of the B-CSP method over conventional methods.The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively,and this study provides practical and theoretical approaches to BCI applications.展开更多
文摘文章通过丽莎·萨默对一名抑郁症和强迫症患者的个体音乐心理治疗案例的治疗过程,诠释了她在使用邦尼音乐引导想象的个体音乐治疗实践中发现其局限性,从而尝试调整治疗技术,最终发展成为由两种方式:音乐想象(Music&Imagery,缩写为MI)和音乐引导想象(Guided Imagery and Music,缩写为GIM),分别在三个层次(支持性、再教育性和重构性)进行实践的六种方法所组成的音乐引导想象连续模式的治疗技术。
文摘If there is no imagination, there is no music appreciation. It should create imaginary world for students in the music classroom teaching practice, and it should foster the students＇ musical imagination. Thus, colleagues can make discussion about strategies proposed including pilot background, context led, screen hygiene conditions and others. We use Cognitive Linguistic Theories to introduce idealized cognitive model and its theoretical basis and the intensified impact on student musical imagination.
基金Project supported by the National Natural Science Foundation of China(Nos.61702454 and 61772468)the MOE Project of Humanities and Social Sciences,China(No.17YJC870018)+1 种基金the Fundamental Research Funds for the Provincial Universities of Zhejiang Province,China(No.GB201901006)the Philosophy and Social Science Planning Fund Project of Zhejiang Province,China(No.20NDQN260YB)
文摘Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively.In this paper,we propose an improved common spatial pattern(B-CSP)method to extract features for alleviating these adverse effects.First,for different subjects,the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization(ERD)and event-related synchronization(ERS)patterns;then the signals of the optimal frequency band are decomposed into spatial patterns,and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data.The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network(BPNN)classifier to classify single-trial MI EEG.Another two conventional feature extraction methods,original common spatial pattern(CSP)and autoregressive(AR),are used for comparison.An improved classification performance for both data sets(public data set:91.25%±1.77%for left hand vs.foot and84.50%±5.42%for left hand vs.right hand;experimental data set:90.43%±4.26%for left hand vs.foot)verifies the advantages of the B-CSP method over conventional methods.The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively,and this study provides practical and theoretical approaches to BCI applications.
