• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.139-147
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• 2015

Steady state visual evoked potential (SSVEP) has been actively studied because of its short training time, relatively higher signal-to-noise ratio, and higher information transfer rate. There are two popular analysis methods for SSVEP signals: power spectral density analysis (PSDA) and canonical correlation analysis (CCA). However, the PSDA is known to be vulnerable to noise due to the use of a single channel. Although conventional CCA is more accurate than PSDA, it may not be appropriate for the real-time SSVEP-based BCI system when it has short time window length because it uses sinusoidal signals as references. Therefore, the two methods are not efficient for the real-time BCI system that requires a short TW and a high recognition accuracy. To overcome this limitation of the conventional methods, this paper proposes a frequency recognition method with a combination of CCA and PSDA using the difference between powers of canonical variables obtained from the results of CCA. Experimental results show that the performance of the combination of CCA and PSDA is better than that of CCA for the case of a short TW.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.356-362
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• 2009

In modern society, most people of the world are densely populated in cities so that the traffic sound has a very significant meaning. people tend to classify traffic sound as a noise pollution while they are likely to categorize most nature sound as positive. In this paper, we applied various forms of FFT filters into white noise belonged in nature sound to find frequency characteristics of white noise which preferred by people and confirm its correlation with nature sound. In addition, we conducted an analysis through the comparison of various traffic and nature sound waveforms and spectra. As a result of analysis, the traffic sound have characteristics which sound energy had concentrated on specific frequency bandwidth and point of time compared to nature sound. And we confirmed the fact that these characteristics had negative elements to which could affect to people. Lastly, by letting the subjects listen directly to both traffic and nature sound through brainwave experiment using electrode, the study measured the energy distribution of alpha waves and beta waves. As a result of experiments, it has been noted that urban sound created a noticeably larger amount of beta waves than nature sound; on the contrary, nature sound generated positive alpha waves. These results could directly confirm the negative effects of traffic sound and the positive effects of nature sound.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.11-19
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• 2004

Stage 1 sleep provides important information regarding interpretation of nocturnal polysomnography, particularly sleep onset. It is a short transition period from wakeful consciousness to sleep. Lack of prominent sleep events characterizing stage 1 sleep is a major obstacle in automatic sleep stage scoring. In this study, we attempted to utilize simultaneous EEC and EOG processing and analyses to detect stage 1 sleep automatically. Relative powers of the alpha waves and the theta waves were calculated from spectral estimation. Either the relative power of alpha waves less than 50％ or the relative power of theta waves more than 23％ was regarded as stage 1 sleep. SEM (slow eye movement) was defined as the duration of both eye movement ranging from 1.5 to 4 seconds and regarded also as stage 1 sleep. If one of these three criteria was met, the epoch was regarded as stage 1 sleep. Results f ere compared to the manual rating results done by two polysomnography experts. Total of 169 epochs was analyzed. Agreement rate for stage 1 sleep between automatic detection and manual scoring was 79.3％ and Cohen's Kappa was 0.586 (p＜0.01). A significant portion (32％) of automatically detected stage 1 sleep included SEM. Generally, digitally-scored sleep s1aging shows the accuracy up to 70％. Considering potential difficulties in stage 1 sleep scoring, the accuracy of 79.3％ in this study seems to be robust enough. Simultaneous analysis of EOG provides differential value to the present study from previous oneswhich mainly depended on EEG analysis. The issue of close relationship between SEM and stage 1 sleep raised by Kinnariet at. remains to be a valid one in this study.

• Journal of IKEEE
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• v.23 no.4
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• pp.1273-1279
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• 2019

Though sleep polysomnography (PSG) is considered as a golden rule for medical diagnosis of sleep disorder, it is essential to find alternative diagnosis methods due to its cost and time constraints. Recently, as the popularity of wearable health devices, there are many research trials to replace conventional actigraphy to consumer grade devices. However, these devices are very limited in their use due to the accessibility of the data and algorithms. In this paper, we showed the predictive model for sleep stages classified by American Academy of Sleep Medicine (AASM) standard and we proposed the estimation of sleep cycle by comparing sensor data and power spectrums of δ wave and θ wave. The sleep stage prediction for 31 subjects showed an accuracy of 85.26%. Also, we showed the possibility that proposed algorithm can find the sleep cycle of REM sleep and NREM sleep.

• Annals of Clinical Neurophysiology
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• v.3 no.1
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• pp.31-36
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• 2001

Background & Objectives : Fractal Dimension(FD) could be an index of correlation between variable parameters in non-linear chaotic signals. We tried to demonstrate that EEG wave is compatible with chaotic waves by measuring the Lyapunov exponent index and compared the difference of FD between variable age groups(teens, 30's, 50's) Methods : We estimated the Lyapunov exponent index and the FD from digital EEG data among five persons in each normal age groups by using the software which is programmed in our laboratory. Statistical analysis was done with SPSS win 8.0. The statistical differences of Lyapunov exponent index and FD between each electrodes and each age groups were done with ANOVA and paired sample t-test. Result : The Lyapunov exponent indexes were larger than 1 in each electrode and age group. There is no statistical difference in FD between each electrodes and each age groups. Except in 30th age group. In this group the FD of right hemisphere is larger than that of left hemisphere. Conclusion : The result of Lyapunov exponent index means EEG wave is a non-linear chaotic signal. And the results of FD suggest that chaotic parameters of right hemisphere is larger than those of left hemisphere at rest at least in younger people. We think that chaotic parameters can be a useful tool in investigating the variable diseases or brain states.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.447-452
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• 2020

The purpose of this research was to confirm that prefrontal neurofeedback training has an impact on adolescents. The objective of this study was to prove its scientific effect through experimentation. The effect of the training was measured by the difference in neuro?frequencies before and after the training. For this research, an experimental group and a control group, each with 22 students in J High School located in the city of S participated in this study. From May to July 2019, the training was conducted three times a week and for 30 minutes per session. The neuro?frequency data collected were analyzed through the methods of F.F.T. The resulting changes from the neurofeedback training for each group were analyzed by T-Tests. The result of the study is as follows; Neurofeedback training has had a positive effect on adolescent attention and sleep. In conclusion, the environmental and educational factors also play an important role. As the interaction of the latter two factors yield an individual's unique brain structure and functionality, the impact of the neurofeedback training is important on adolescents. The derivation of the above results by utilizing scientific and objective methods reemphasizes the importance of this study.