• ETRI Journal
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• v.44 no.4
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• pp.672-685
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• 2022

This article proposes a subject-independent application of brain-computer interfacing (BCI). A 32-channel Electroencephalography (EEG) device is used to measure imagined speech (SI) of four words (sos, stop, medicine, washroom) and one phrase (come-here) across 13 subjects. A deep long short-term memory (LSTM) network has been adopted to recognize the above signals in seven EEG frequency bands individually in nine major regions of the brain. The results show a maximum accuracy of 73.56% and a network prediction time (NPT) of 0.14 s which are superior to other state-of-the-art techniques in the literature. Our analysis reveals that the alpha band can recognize SI better than other EEG frequencies. To reinforce our findings, the above work has been compared by models based on the gated recurrent unit (GRU), convolutional neural network (CNN), and six conventional classifiers. The results show that the LSTM model has 46.86% more average accuracy in the alpha band and 74.54% less average NPT than CNN. The maximum accuracy of GRU was 8.34% less than the LSTM network. Deep networks performed better than traditional classifiers.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.25-34
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• 2022

Brain computed tomography (CT) is useful for brain lesion diagnosis, such as brain hemorrhage, due to non-invasive methodology, 3-dimensional image provision, low radiation dose. However, there has been numerous misdiagnosis owing to a lack of radiologist and heavy workload. Recently, object detection technologies based on artificial intelligence have been developed in order to overcome the limitations of traditional diagnosis. In this study, the applicability of a deep learning-based YOLOv5s model was evaluated for brain hemorrhage detection using brain CT images. Also, the effect of hyperparameters in the trained YOLOv5s model was analyzed. The YOLOv5s model consisted of backbone, neck and output modules. The trained model was able to detect a region of brain hemorrhage and provide the information of the region. The YOLOv5s model was trained with various activation functions, optimizer functions, loss functions and epochs, and the performance of the trained model was evaluated in terms of brain hemorrhage detection accuracy and training time. The results showed that the trained YOLOv5s model is able to provide a bounding box for a region of brain hemorrhage and the accuracy of the corresponding box. The performance of the YOLOv5s model was improved by using the mish activation function, the stochastic gradient descent (SGD) optimizer function and the completed intersection over union (CIoU) loss function. Also, the accuracy and training time of the YOLOv5s model increased with the number of epochs. Therefore, the YOLOv5s model is suitable for brain hemorrhage detection using brain CT images, and the performance of the model can be maximized by using appropriate hyperparameters.

• Korean Journal of Cognitive Science
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• v.21 no.2
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• pp.309-338
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• 2010

In this paper, we studied the brain-computer interface (BCI). BCIs help severely disabled people to control external devices by analyzing their brain signals evoked from motor imageries. The findings in the field of neurophysiology revealed that the power of $\beta$(14-26 Hz) and $\mu$(8-12 Hz) rhythms decreases or increases in synchrony of the underlying neuronal populations in the sensorymotor cortex when people imagine the movement of their body parts. These are called Event-Related Desynchronization / Synchronization (ERD/ERS), respectively. We implemented a BCI-based mouse interface system which enabled subjects to control a computer mouse cursor into four different directions (e.g., up, down, left, and right) by analyzing brain signal patterns online. Tongue, foot, left-hand, and right-hand motor imageries were utilized to stimulate a human brain. We used a non-invasive EEG which records brain's spontaneous electrical activity over a short period of time by placing electrodes on the scalp. Because of the nature of the EEG signals, i.e., low amplitude and vulnerability to artifacts and noise, it is hard to analyze and classify brain signals measured by EEG directly. In order to overcome these obstacles, we applied statistical machine-learning techniques. We could achieve high performance in the classification of four motor imageries by employing Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) which transformed input EEG signals into a new coordinate system making the variances among different motor imagery signals maximized for easy classification. From the inspection of the topographies of the results, we could also confirm ERD/ERS appeared at different brain areas for different motor imageries showing the correspondence with the anatomical and neurophysiological knowledge.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.4
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• pp.256-261
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology developed, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on the reinforcement learning.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.

• Biomolecules & Therapeutics
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• v.21 no.6
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• pp.462-469
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• 2013

Eucommia ulmoides Oliv. Bark (EUE) is commonly used for the treatment of hypertension, rheumatoid arthritis, lumbago, and ischialgia as well as to promote longevity. In this study, we tested the effects of EUE aqueous extract in graded doses to protect and enhance cognition in scopolamine-induced learning and memory impairments in mice. EUE significantly improved the impairment of short-term or working memory induced by scopolamine in the Y-maze and significantly reversed learning and memory deficits in mice as measured by the passive avoidance and Morris water maze tests. One day after the last trial session of the Morris water maze test (probe trial session), EUE dramatically increased the latency time in the target quadrant in a dose-dependent manner. Furthermore, EUE significantly inhibited acetylcholinesterase (AChE) and thiobarbituric acid reactive substance (TBARS) activities in the hippocampus and frontal cortex in a dose-dependent manner. EUE also markedly increased brain-derived neurotrophic factor (BDNF) and phosphorylation of cAMP element binding protein (CREB) in the hippocampus of scopolamine-induced mice. Based on these findings, we suggest that EUE may be useful for the treatment of cognitive deficits, and that the beneficial effects of EUE are mediated, in part, by cholinergic signaling enhancement and/or protection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1729-1738
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• 2021

Machine learning has a close relationship with cognitive psychology and brain science and is developing together. This paper analyzes the OASIS-3 dataset using machine learning techniques and proposes a model for predicting dementia. Dimensional reduction through PCA (Principal Component Analysis) is performed on the data quantifying the volume of each area among OASIS-3 data, and only important elements (features) are extracted and then various machine learning including gradient boosting and stacking Apply the models and compare the performance of each. Unlike previous studies, the proposed technique has a great differentiation because it uses not only the brain biometric data, but also basic information data such as the participant's gender and medical information data of the participant. In addition, it was shown that the proposed technique through various performance evaluations is a model that can better predict dementia by finding features that are more related to dementia among various numerical data.

• Journal of Korean Elementary Science Education
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• v.32 no.4
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• pp.567-580
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• 2013

This study is to activate gifted students' brains for creativity ability and also an integrated science and art teaching program. The learning programs integrating science and art, which have 30 periods and 10 topics on art and the knowledge of science, were developed dependant on five steps - observing, having interests and curiosity, experimental designing and performing, internalizing, and expressing in an arts-based manner. This programs were applied to 20 senior gifted students in Y Elementary School in Gyeonggi province, by one group pretest-posttest design. The results from these integrated programs of science and art are as follows: First, in the performance of science tasks, prefrontal lobe(F7, FT7) of left brain increase the relative power of theta wave, whereas in the performance of drawing tasks increase the relative power of beta wave in prefrontal lobe(FP1) of left brain, bilateral frontal(F7, F3, Fz, F4, F8, FT7, FC3, FCz), bilateral temporal(T7, TP7, TP8, P7), parietal lobe of left brain(CP3, CPz, P3, Pz), bilateral occipital(O1, Oz, O2). Second, in the performance of science tasks, the relative power of beta wave activity in the left temporal lobe(T7) of the brains of talented students in science significantly decreased whereas it was greatly activated in another part, the left frontal lobe(F3) of the brain (p<.05). Third, in the performance of drawing tasks, the relative power of theta wave activity in five areas of the brain, namely the left temporal lobe(T7), the left frontal lobe(F3), the right frontal lobe(F4), and the left and right parietal lobes of gifted students in science who took the course of the integrated programs, was considerably increased statistically(p<.05). On top of that, these programs were especially effective in balancing the symmetrical development of both cerebral hemispheres by multiplying theta wave activity in the frontal lobes(F3, F4) and the parietal lobes(CP3, P3, P4), which are particularly related to creative thinking. According to the results of this study of brain-based teaching strategies combining science and art, it is an effective program to develop overall activate gifted students' brains for creativity ability. This is expected to be utilized to activate the brain areas for creativity of gifted students in science.

• Journal of the Korean earth science society
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• v.31 no.2
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• pp.172-184
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• 2010

This study is to investigate the problem solving styles according to the left /right brain preference among earth science gifted students. We took the R/LCT and the test of BPI to investigate the brain preference of earth science gifted students (N=16), and took S-CPST to investigate the problem solving styles on them. In the R/LCT, the earth science gifted students were classified into 3 groups (8 left-brain preference students, 7 right-brain preference students, 1 middle-brain preference student). In the BPI, 8 students had the appearance of left-brain preference, whereas 8 students had the appearance of right-brain preference. According to the result of S-CPST, first the left brain preference students tended to resolve a problem into simple components, then they put together each simple component. They prefer to solve a problem using numbers and mathematical signs logically, but they were afraid of giving trouble to describe own idea with pictures. Whereas the right brain preference students solved a problem with 3 steps. First, they saw an overall form of problem. Second, they tried to analyze each simple component of it, and then, made up all in one. Also, the right brain preference students observed the intuitive pattern of problem first, and then suggested the various problem solving methods later, and they took a solving plan using a picture in detail. In sum, earth science gifted students are unequal in problem solving styles according to the left/right brain preference. Thus, a teaching-learning method needs to be developed based on left/right brain preference for more effective gifted education.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.676-687
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• 2020

Purpose To analyze the altered brain regions and intrinsic brain activity patterns in trauma-exposed firefighters without posttraumatic stress disorder (PTSD). Materials and Methods Resting-state functional MRI (rsfMRI) was performed for all subjects. Thirty-one firefighters over 40 years of age without PTSD (31 men; mean age, 49.8 ± 4.7 years) were included. Twenty-six non-traumatized healthy controls (HCs) (26 men; mean age, 65.3 ± 7.84 years) were also included. Voxel-based morphometry was performed to investigate focal differences in the brain anatomy. Seed-based functional connectivity analysis was performed to investigate differences in spontaneous brain characteristics. Results The mean z-scores of the Seoul Verbal Learning Test for immediate and delayed recall, Controlled Oral Word Association Test (COWAT) score for animals, and COWAT phonemic fluency were significantly lower in the firefighter group than in the HCs, indicating decreased neurocognitive function. Compared to HCs, firefighters showed reduced gray matter volume in the left superior parietal gyrus and left inferior temporal gyrus. Further, in contrast to HCs, firefighters showed alterations in rsfMRI values in multiple regions, including the fusiform gyrus and cerebellum. Conclusion Structural and resting-state functional abnormalities in the brain may be useful imaging biomarkers for identifying alterations in trauma-exposed firefighters without PTSD.