Yoon Gi Chung

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Slow-adapting type I (SA-I) afferents deliver sensory signals to the somatosensory cortex during low-frequency (or static) mechanical stimulation. It has been reported that the somatosensory projection from SA-I afferents is effective and reliable for object grasping and manipulation. Despite a large number of neuroimaging studies on cortical activation(More)
Epilepsy is a critical neurological disorder resulting from abnormal hyper-excitability of neurons in the brain. Studies have shown that epilepsy can be detected in electroencephalography (EEG) recordings of patients suffering from seizures. The performance of EEG-based epileptic seizure detection relies largely on how well one can extract features from an(More)
Overall accuracy of noninvasive brain-computer interfaces (BCIs) based on motor imagery electroencephalography (EEG) is highly dependent on the extraction of features from the oscillation of sensorimotor rhythms (SMRs) during imagination of movements. In this study, we statistically evaluated whole-brain connectivity using the measurement of linear(More)
In the human mechanosensation system, rapidly adapting afferents project sensory signals of flutter (5-50Hz) to the contralateral primary somatosensory cortex (S1) and bilateral secondary somatosensory cortex (S2) whereas Pacinian afferents project sensory signals of vibration (50-400Hz) to bilateral S2. However, it remains largely unknown how somatosensory(More)
Tactile adaptation is a phenomenon of the sensory system that results in temporal desensitization after an exposure to sustained or repetitive tactile stimuli. Previous studies reported psychophysical and physiological adaptation where perceived intensity and mechanoreceptive afferent signals exponentially decreased during tactile adaptation. Along with(More)
Mechanosensation includes the detection of mechanical stimuli from mechanoreceptors and sensory signal transduction to the somatosensory cortex through neural afferents. Previous studies reported the sensory signals of flutter (5-50 Hz) and vibration (50-400 Hz) traveled through separated neural afferents of rapidly adapting type 1 (RA) and rapidly adapting(More)
As the use of wearable haptic devices with vibrating alert features is commonplace, an understanding of the perceptual categorization of vibrotactile frequencies has become important. This understanding can be substantially enhanced by unveiling how neural activity represents vibrotactile frequency information. Using functional magnetic resonance imaging(More)
Previous neural decoding studies have mainly focused on discrimination of activation patterns evoked by active movements. Nonetheless, comparatively, little attention has been devoted toward understanding how brain signals are observed with passive stimulus. In this study, we examined whether the stimulus locations on between fingers, one of the most(More)