Speech expression is an important high-level cognitive behavior of human beings. The realization of this behavior is closely related to human brain activity. Both true speech expression and speech imagination can activate part of the same brain area. Therefore, speech imagery becomes a new paradigm of brain-computer interaction. Brain-computer interface (BCI) based on speech imagery has the advantages of spontaneous generation, no training, and friendliness to subjects, so it has attracted the attention of many scholars. However, this interactive technology is not mature in the design of experimental paradigms and the choice of imagination materials, and there are many issues that need to be discussed urgently. Therefore, in response to these problems, this article first expounds the neural mechanism of speech imagery. Then, by reviewing the previous BCI research of speech imagery, the mainstream methods and core technologies of experimental paradigm, imagination materials, data processing and so on are systematically analyzed. Finally, the key problems and main challenges that restrict the development of this type of BCI are discussed. And the future development and application perspective of the speech imaginary BCI system are prospected.
Brain ; Computers ; Humans ; Imagery, Psychotherapy ; Speech ; Technology

PURPOSE: The purpose of this study was to identify effects of guided imagery on stress including cognitive, affective, marital and social, and anxiety among women receiving in vitro fertilization (IVF). METHODS: Data were collected between April, 21 and June, 17, 2008. The participants in this study were 57 women (26 for the experimental group, 31 for the control group) receiving IVF for primary or secondary infertility in one of the outpatient infertility centers in Seoul. The guided imagery (Suk, 2001) was provided through audio CD to the experimental group by themselves 8 minutes per day for 2 weeks. Data were analyzed by SPSS 12.0 windows program. RESULTS: After guided imagery, the experimental group showed significantly lower affective stress and total stress scores. Anxiety scores increased significantly in the control group, but not in the experimental group after treatment. CONCLUSION: The findings suggest that guided imagery is an effective nursing intervention for reducing stress especially affective stress and anxiety among infertile women receiving IVF in outpatient infertility center.
Anxiety ; Female ; Fertilization in Vitro ; Humans ; Imagery (Psychotherapy) ; Infertility ; Outpatients

Motor imagery electroencephalogram (EEG) signals are non-stationary time series with a low signal-to-noise ratio. Therefore, the single-channel EEG analysis method is difficult to effectively describe the interaction characteristics between multi-channel signals. This paper proposed a deep learning network model based on the multi-channel attention mechanism. First, we performed time-frequency sparse decomposition on the pre-processed data, which enhanced the difference of time-frequency characteristics of EEG signals. Then we used the attention module to map the data in time and space so that the model could make full use of the data characteristics of different channels of EEG signals. Finally, the improved time-convolution network (TCN) was used for feature fusion and classification. The BCI competition IV-2a data set was used to verify the proposed algorithm. The experimental results showed that the proposed algorithm could effectively improve the classification accuracy of motor imagination EEG signals, which achieved an average accuracy of 83.03% for 9 subjects. Compared with the existing methods, the classification accuracy of EEG signals was improved. With the enhanced difference features between different motor imagery EEG data, the proposed method is important for the study of improving classifier performance.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Humans ; Imagery, Psychotherapy ; Imagination

As the most common active brain-computer interaction paradigm, motor imagery brain-computer interface (MI-BCI) suffers from the bottleneck problems of small instruction set and low accuracy, and its information transmission rate (ITR) and practical application are severely limited. In this study, we designed 6-class imagination actions, collected electroencephalogram (EEG) signals from 19 subjects, and studied the effect of collaborative brain-computer interface (cBCI) collaboration strategy on MI-BCI classification performance, the effects of changes in different group sizes and fusion strategies on group multi-classification performance are compared. The results showed that the most suitable group size was 4 people, and the best fusion strategy was decision fusion. In this condition, the classification accuracy of the group reached 77%, which was higher than that of the feature fusion strategy under the same group size (77.31%
Brain ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination

Motor imagery (MI) is an important paradigm of driving brain computer interface (BCI). However, MI is not easy to control or acquire, and the performance of MI-BCI depends heavily on the performance of the subjects' MI. Therefore, the correct execution of MI mental activities, ability evaluation and improvement methods play important and even critical roles in the improvement and application of MI-BCI system's performance. However, in the research and development of MI-BCI, the existing researches mainly focus on the decoding algorithm of MI, but do not pay enough attention to the above three aspects of MI mental activities. In this paper, these problems of MI-BCI are discussed in detail, and it is pointed out that the subjects tend to use visual motor imagery as kinesthetic motor imagery. In the future, we need to develop some objective, quantitatively visualized MI ability evaluation methods, and develop some effective and less time-consumption training methods to improve MI ability. It is also necessary to solve the differences and commonness of MI problems between and within individuals and MI-BCI illiteracy to a certain extent.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Imagination

Motor imagery (MI), motion intention of the specific body without actual movements, has attracted wide attention in fields as neuroscience. Classification algorithms for motor imagery electroencephalogram (MI-EEG) signals are able to distinguish different MI tasks based on the physiological information contained by the EEG signals, especially the features extracted from them. In recent years, there have been some new advances in classification algorithms for MI-EEG signals in terms of classifiers versus machine learning strategies. In terms of classifiers, traditional machine learning classifiers have been improved by some researchers, deep learning and Riemannian geometry classifiers have been widely applied as well. In terms of machine learning strategies, ensemble learning, adaptive learning, and transfer learning strategies have been utilized to improve classification accuracies or reach other targets. This paper reviewed the progress of classification algorithms for MI-EEG signals, summarized and evaluated the existing classifiers and machine learning strategies, to provide new ideas for developing classification algorithms with higher performance.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography ; Imagery, Psychotherapy ; Imagination ; Machine Learning

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.
Humans ; Time Factors ; Brain ; Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer

The purpose of this study was to identify the effects of the guided imagery program on stress of adolescents. The study design was nonequivalent control group pretest-posttest study. The Data were collected from the 1st to 30th of September in 2000. Two schools were selected as an experimental group and a control group. Each group included two classes. The experimental group was consisted of 40 male students and 42 female students and the control group was consisted of 41 males and 42 females. The guided imagery was provided with audiotapes to the subjects in the classroom for 8 minutes per each therapy, 5 times a week for 4 weeks. The pretest was given before the therapy to measure variables for both groups and the posttests were performed twice after 2 weeks and 4 weeks from the start of intervention. The Instruments used in this study were perception of stress scale developed by Park(1996), Vividness of Imagery Scale; short form of bett's test scale developed by Sheenhan(1967). The data were analyzed by the SAS program using Chi-square test, t-test, repeated measure ANOVA and Bonferroni correction. The results of this study are as follows: "The level of stress of adolescents who received the guided imagery will be significantly lower than that of control group" was supported(F=10.14, p=.00). In conclusion, the guided imagery was suggested as an effective nursing intervention did reduce the stress of adolescents which school nurses could utilize for adolescents at school.
Adolescent* ; Female ; Humans ; Imagery (Psychotherapy)* ; Male ; Nursing ; Tape Recording ; Child Health

PURPOSE: The anxiety and stress of nursing students on performance intramuscular injection diminished nursing skill performance. The purpose of this study was to identify the effects of the guided imagery program on anxiety, stress and nursing skill performance of nursing students. METHOD: The study design was time series with a nonequivalent control group pretest- posttest study. The Data were collected from the 30th of Oct. to the 6th of Nov. 2001. The objects of this study were 36 sophomores of university(18 for the experimental group, 18 for the control group). The Instruments used in this study were State Trait Anxiety Inventory developed by Spielberger (1972), Visual Analogue Scale for Stress and Nursing skill performance developed by the researcher. The guided imagery was provided through audiotapes to the subjects for 8 minutes. The pretest was given before the therapy to measure variables for both groups and the posttests were performed after intervention. The data were analyzed by the SAS program using t-test and paired t-test. RESULT: The results of this study are as follows. The level of anxiety of students who received the guided imagery were significantly lower than that of control group. the level of stress had a deeling tendency and the nursing skill performance level was significantly higher than that of control group. CONCLUSION: The guided imagery suggested as an effective nursing intervention did reduce the anxiety and promoted nursing skill performance of nursing students.
Anxiety ; Humans ; Imagery (Psychotherapy)* ; Injections, Intramuscular* ; Nursing* ; Students, Nursing* ; Tape Recording

Adolescent ; Adult ; Athletic Performance ; psychology ; Back ; physiology ; Electromyography ; Exercise ; physiology ; Humans ; Imagery (Psychotherapy) ; Male ; Young Adult