• Journal of Korea Game Society
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• v.9 no.2
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• pp.23-28
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• 2009

There are a lot of games which have good benefits in the game genre such as serious game. In this paper we implement an serious game for increasing intensive power by calculating the index of the intensive power based on EEG signal. First we explain the definition of the EEG and the classification of the brainwaves and we depict the method for increasing the intensive power. Then we apply the index of the intensive power to the game production to train the intensive power. At last we make an experiment on the effect of an game which increases the intensive power and the analysis shows the increase of the intensive power.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.2
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• pp.305-316
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• 2021

In this study, the effect of wearing functional compression pants is verified using a lower-limb wearable robot through a bio-signal analysis and subjective fit evaluation. First, the compression area to be applied to the functional compression pants is derived using the quad method for nine men in their 20s. Subsequently, functional compression pants are prepared, and changes in Electroencephalogram (EEG) and Electrocardiogram (ECG) signals when wearing the functional compression and normal regular pants inside a wearable robot are measured. The EEG and ECG signals are measured with eyes closed and open. Results indicate that the Relative alpha (RA) and Relative gamma wave (RG) of the EEG signal differ significantly, resulting in increased stability and reduced anxiety and stress when wearing the functional compression pants. Furthermore, the ECG analysis results indicate statistically significant differences in the Low frequency (LF)/High frequency (HF) index, which reflect the overall balance of the autonomic nervous system and can be interpreted as feeling comfortable and balanced when wearing the functional compression pants. Moreover, subjective sense is discovered to be effective in assessing wear fit, ease of movement, skin friction, and wear comfort when wearing the functional compression pants.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.2083-2085
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• 2009

Brain Computer Interface (BCI) is a communication pathway between devices (computers) and human brain. It treats brain signals in real-time basis and discriminates some information of what human brain is doing. In this work, we develop a EEG BCI system using a feature extraction such as common spatial pattern (CSP) and a classifier using Fisher linear discriminant analysis (FLDA). Two-class EEG motor imagery movement datasets with both cued and uncued are tested to verify its feasibility.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.3
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• pp.258-266
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• 2013

This paper presents the correlation between psychological and physiological acoustics for the automotive acceleration sound. The research purpose of this paper is to evaluate the sound quality of acceleration sound of a passenger car based EEG signal. The previous method for the objective evaluation of sound quality is to use sound metrics based on psychological acoustics. This method uses not only psychological acoustics but also physiological acoustics. For this work, the sounds of 7 premium passenger cars are recorded and evaluated subjectively by 33 people. The correlation between the subjective rating and sound metrics is calculated based on physiological acoustics. Finally the correlation between the subjective rating and the EEG signal measured on the brain is also calculated. Throughout these results the new evaluation system for the sound quality on the automotive acceleration sound of a passenger car has been developed based on bio-signal.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.786-791
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• 2011

An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student's-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.

• Journal of Sensor Science and Technology
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• v.24 no.6
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• pp.392-397
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• 2015

Recently computer games have been widely spread. For the purpose of studying brain activities, EEG was measured during the computer game and analyzed in terms of channels and frequency bands. EEG data were obtained during the resting state and game immersion. Then the power spectra of alpha, beta and theta bands were computed. During game immersion, the ratio between theta / alpha could effectively differentiate between rest and game immersion. Changes in brain activity (26~53%) were observed in the parietal and occipital lobes. Interestingly, immersion shows different features compared to concentration. The state of game immersion could be detected. Therefore, it is possible to utilize the state of immersion as one of the game parameters or to generate a control signal that may be used to provide a warning message or abort the game when the situation of the excessive indulgence in the game reaches. EEG can be applied as smart interface for computer game.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.124-132
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• 2023

In this paper, we present a simple classification model based on statistical features and demonstrate the successful implementation of a brain-computer interface (BCI) based light on/off control system. This research shows study and development of light on/off control system based on BCI technology, which allows the users to control switching a lamp using electroencephalogram (EEG) signals. The logistic regression algorithm is used for classification of the EEG signal to convert it into light on, light off control commands. Training data were collected using 14-channel BCI system which records the brain signals of participants watching a screen with flickering lights and saves the data into .csv file for future analysis. After extracting a number of features from the data and performing classification using logistic regression, we created commands to switch on a physical lamp and tested it in a real environment. Logistic regression allowed us to quite accurately classify the EEG signals based on the user's mental state and we were able to classify the EEG signals with 82.5% accuracy, producing reliable commands for turning on and off the light.

• Journal of the Ergonomics Society of Korea
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• v.18 no.1
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• pp.13-24
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• 1999

The objective of the study is to discriminate EEG(electroencephalogram) due to emotional changes. Emotion was evoked by the series of auditory stimuli which were selected from the natural sounds in the sound effect collection of compact disc. Seventeen university students participated and experienced positive or negative emotions by six auditory stimuli with intermission between stimuli. Temporal EEG ($T_3$, $T_4$, $T_5$, and $T_6$) was recorded at the same time and a subjective test was performed on the eleven point scales after the experiment. The maximum and minimum scores of the EEG among six stimuli EEG were analyzed for discrimination of emotion. The EEG signals were transformed into feature objects based on scalar intervention model coefficients. Auditory stimulus was considered as intervention variable. They were classified by Discriminant Analysis for each channel. The features showed results with the best classification accuracy of 91.2 % in $T_4$ for auditory stimuli. This study could be extended to establish an algorithm which quantifies and classifies emotions evoked by auditory stimulus using time-series models.

• 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.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.187-188
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• 2011

Independent component analysis (ICA) is a method separating the mixture of signals into statistically and mutually independent ones. It has been applied to not only the Cocktail-party problem but also EEG analysis using the EEG waveform, digital signal processing, image processing and cognitive technique field actively. This study aims to propose a procedure to estimate the modal responses and mode shapes of a floating structure by using the ICA method from measured responses of the floating structure.