1.Robotics and Surgery.
Journal of the Korean Medical Association 2006;49(5):430-438
Nowadays robotics are used in all surgical fields. By increasing the intra-abdominal articulation while operating through a small incision, robotics are increasingly used for a large number of visceral and solid organ operations, including surgery on the gallbladder, esophagus, stomach, intestines, colon, and rectum, as well as for the endocrine organs. As a specialty, robotics should continue to grow. The robotic era enables general surgeons perform more and more complex procedures through small incisions. As technology catches up with our imagination, robotic instruments and 3-D monitoring will become a routine practice and continue to improve the patient care by providing surgeons with most precise, least traumatic ways of treating surgical diseases.
Colon
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Esophagus
;
Gallbladder
;
Imagination
;
Intestines
;
Patient Care
;
Rectum
;
Robotics*
;
Stomach
2.The Prospect of a New Smart Healthcare System: A Wearable Device-Based Complex Structure of Position Detecting and Location Recognition System
Kyung Jin CHUNG ; Jayoung KIM ; Taeg Keun WHANGBO ; Khae Hawn KIM
International Neurourology Journal 2019;23(3):180-184
In upcoming fourth industrial revolution era, it is inevitable to address smart healthcare as not only scientist but also clinician. We have the task to plan and realize this through human imagination, creativity, and applicability for the clarification of the direction of the development and utilization of this technology. One thing that is clear is that it is important to understand what information is needed, how to interpret it, what will be the outcomes, and how to respond in artificial intelligence and Internet of Things era. Therefore, we would like to briefly discuss the characteristics of smart healthcare, and, suggest one approach that is easily applicable in the current situation.
Artificial Intelligence
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Creativity
;
Delivery of Health Care
;
Humans
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Imagination
;
Internet
;
Urination
3.Research progress and prospect of collaborative brain-computer interface for group brain collaboration.
Lixin ZHANG ; Xiaocui CHEN ; Long CHEN ; Bin GU ; Zhongpeng WANG ; Dong MING
Journal of Biomedical Engineering 2021;38(3):409-416
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
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Brain-Computer Interfaces
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Electroencephalography
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Humans
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Imagery, Psychotherapy
;
Imagination
4.Execution, assessment and improvement methods of motor imagery for brain-computer interface.
Guixin TIAN ; Junjie CHEN ; Peng DING ; Anmin GONG ; Fan WANG ; Jiangong LUO ; Yiyang DONG ; Lei ZHAO ; Caiping DANG ; Yunfa FU
Journal of Biomedical Engineering 2021;38(3):434-446
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
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Brain-Computer Interfaces
;
Electroencephalography
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Humans
;
Imagery, Psychotherapy
;
Imagination
5.Progress of classification algorithms for motor imagery electroencephalogram signals.
Tuo LIU ; Yangyang YE ; Kun WANG ; Lichao XU ; Weibo YI ; Minpeng XU ; Dong MING
Journal of Biomedical Engineering 2021;38(5):995-1002
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
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Brain-Computer Interfaces
;
Electroencephalography
;
Imagery, Psychotherapy
;
Imagination
;
Machine Learning
6.Motor imagery electroencephalogram classification based on sparse spatiotemporal decomposition and channel attention.
Hongli LI ; Feichao YIN ; Ronghua ZHANG ; Xin MA ; Hongyu CHEN
Journal of Biomedical Engineering 2022;39(3):488-497
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
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Brain-Computer Interfaces
;
Electroencephalography/methods*
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Humans
;
Imagery, Psychotherapy
;
Imagination
7.Parameter transfer learning based on shallow visual geometry group network and its application in motor imagery classification.
Journal of Biomedical Engineering 2022;39(1):28-38
Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.
Algorithms
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Brain-Computer Interfaces
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Electroencephalography/methods*
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Humans
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Imagination
;
Machine Learning
8.An experimental study on the evaluation of significance of the dreams.
Journal of Korean Neuropsychiatric Association 1992;31(5):1001-1008
No abstract available.
Dreams*
9.Which Is Crucial, Strengthen the Foundation or Building the Dream House?.
Hiroyuki ISAYAMA ; Yousuke NAKAI ; Toshio FUJISAWA
Gut and Liver 2017;11(4):453-454
No abstract available.
Dreams*
10.Comparison of Dream Themes, Emotions and Sleep Parameters between Nightmares and Bad Dreams in Nightmare Sufferers.
Journal of Sleep Medicine 2016;13(2):53-59
OBJECTIVES: The current study aimed to explore the difference of dream themes, emotional intensity, and sleep parameters between nightmares and bad dreams in nightmare sufferers. METHODS: Twenty-four nightmare sufferers who endorsed clinical levels of nightmares (Disturbing Dream and Nightmare Severity Index Scores ≥10) recorded daily information about their dream themes using a modified version of the Typical Dreams Questionnaire, emotional intensity about their nightmares and bad dreams, and sleep for two weeks on a mobile device. RESULTS: Evil presence (35%) was reported with higher frequency in nightmares, whereas interpersonal conflicts (31%) were predominantly reported in bad dreams. Nightmares were rated substantially more emotionally intense than bad dreams. Especially, fear (Z=-2.118, p=0.034) was rated as being significantly higher in nightmares than bad dreams. There were differences on time in bed, wake after sleep onset, sleep efficiency on the days with nightmares or bad dreams compared to other days; however, there were no differences in sleep parameters between nightmares and bad dreams. CONCLUSIONS: The results suggest that nightmares may be qualitatively and quantitatively different from bad dreams in nightmare sufferers.
Dreams*