• The Journal of the Korea Contents Association
• /
• v.22 no.10
• /
• pp.861-869
• /
• 2022

In patients with Tourette's syndrome, a tic occurs when Mu wave synchronization is broken. Conversely, when Mu wave synchronization is achieved, a tick does not occur. When the tic is suppressed, the cognitive control response process is changed, and if the neurofeedback training that adjusts the EEG frequency power is performed with the changed, the patient will be treated autonomously without artificially suppressing the tic. The results of the research test suggest that if the tic patient does not artificially synchronize mu waves in the premotor cortex (Frontal Cortical 3 site), and if EEG control is performed autonomously like neurofeedback training, as a result, tics do not occur. Cognitive control response processes are altered when a subject is inhibited from tics. By training the altered cognitive control with neurofeedback that modulates EEG frequency power, the patient can be treated autonomously without artificially suppressing the tic.Mu-wave synchronizationcan now be added to existing neurofeedback treatment protocols such as SMR reinforcement, theta-beta-wave imbalance correction, and alpha-wave reinforcement. This study will be used in follow-up studies and clinical trials to more scientifically verify the neurofeedback treatment protocol, a treatment for patients with Tourette's syndrome.

• Science of Emotion and Sensibility
• /
• v.11 no.3
• /
• pp.357-364
• /
• 2008

This study is to find training effect on EEG(Electroencephalography) and EMG(electromyogram) evoked by repetitive movement of a hand. Five university students participated in this study and were asked to perform repetitive movement of right hand for 5 seconds with rest for 10 seconds. They repeated the movement for 48 minutes and for 5 days. EEG and EMG were measured according to every movement. Coherence between EEG and EMG and power spectrum of EEG were analyzed and were tried to observe their changes within a day and between days of the repetitive movement. Training effect according the time of the movement was significantly found in mu and beta frequencies in EEG. However, training effect was not significant between the days of the movement and also, not in coherence between EEG and EMG.

• Journal of KIISE
• /
• v.41 no.12
• /
• pp.1050-1057
• /
• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.