Gayoung Choi

Curcumin is a major diarylheptanoid component of Curcuma longa with traditional usage for anxiety and depression. It has been known for the anti-inflammatory, antistress, and neurotropic effects. Here we examined curcumin effect in neural plasticity and cell viability. 60-channel multielectrode array was applied on organotypic hippocampal slice cultures (OHSCs) to monitor the effect of 10 μ M curcumin in long-term depression (LTD) through low-frequency stimulation (LFS) to the Schaffer collatera

BACKGROUND: A steady-state visual-evoked potential (SSVEP) is a brain response to visual stimuli modulated at certain frequencies; it has been widely used in electroencephalography (EEG)-based brain-computer interface research. However, there are few published SSVEP datasets for brain-computer interface. In this study, we obtained a new SSVEP dataset based on measurements from 30 participants, performed on 2 days; our dataset complements existing SSVEP datasets: (i) multi-band SSVEP datasets are

Alzheimer's disease (AD) is a neurodegenerative disorder, characterized by memory loss and cognitive decline. Among the suggested pathogenic mechanisms of AD, the cholinergic hypothesis proposes that AD symptoms are a result of reduced synthesis of acetylcholine (ACh). A non-selective antagonist of the muscarinic ACh receptor, scopolamine (SCOP) induced cognitive impairment in rodents. Umbelliferone (UMB) is a Apiaceae-family-derived 7-hydeoxycoumarin known for its antioxidant, anti-tumor, antic

The seriousness of the diseases caused by aging have recently gained attention. Alzheimer's disease (AD), a chronic neurodegenerative disease, accounts for 60-80% of senile dementia cases. Continuous research is being conducted on the cause of Alzheimer's disease, and it is believed to include complex factors, such as genetic factors, the accumulation of amyloid beta plaques, a tangle of tau protein, oxidative stress, cholinergic dysfunction, neuroinflammation, and cell death. Sinapic acid is a

Traditional electroencephalography (EEG)-based authentication systems generally use external stimuli that require user attention and relatively long time for authentication. The aim of this study is to investigate whether EEGs measured in resting state without using external stimuli can be used to develop a biometric authentication system. Seventeen subjects participated in the experiment in which EEG data were measured while the subjects repetitively closed and opened their eyes. Changes in alp

Alzheimer's disease (AD) is characterized by cognitive impairment, loss of learning and memory, and abnormal behaviors. Scopolamine (SCOP) is a non-selective antagonist of muscarinic acetylcholine receptors that exhibits the behavioral and molecular hallmarks of AD. Vanillic acid (VA), a phenolic compound, is obtained from the roots of a traditional plant called Angelica sinensis, and has several pharmacologic effects, including antimicrobial, anti-inflammatory, anti-angiogenic, anti-metastatic,

The finding of genotypic abnormalities in the tumor-adjacent epithelia supports the concept of field cancerization. Such genotypic parameters may provide a genetic basis for the development of an early recurrence or second primary tumors after therapeutic treatment of head and neck squamous cell carcinomas.

BACKGROUND: To apply transcranial electrical stimulation (tES) to the motor cortex, motor hotspots are generally identified using motor evoked potentials by transcranial magnetic stimulation (TMS). The objective of this study is to validate the feasibility of a novel electroencephalography (EEG)-based motor-hotspot-identification approach using a machine learning technique as a potential alternative to TMS. METHODS: EEG data were measured using 63 channels from thirty subjects as they performed

Electroencephalography (EEG)-based open-access datasets are available for emotion recognition studies, where external auditory/visual stimuli are used to artificially evoke pre-defined emotions. In this study, we provide a novel EEG dataset containing the emotional information induced during a realistic human-computer interaction (HCI) using a voice user interface system that mimics natural human-to-human communication. To validate our dataset via neurophysiological investigation and binary emot

The biometrics based on resting state electroencephalography (EEG) is better than other EEG-based authentication protocols in terms of usability because it does not require any external stimuli and has a relatively short authentication time. Most of previous resting state EEG-based authentication systems have used a relatively long EEG data (e.g., > 1 min) measured once, and they were segmented to create many trials (e.g., > 100). In this case, however, it is difficult to reflect real-authentica

Neurorehabilitation based on transcranial electrical stimulation (tES) has been introduced to improve the motor rehabilitation for patients with neurological disorders. To define an optimal tES site, transcranial magnetic stimulation (TMS) is generally used. However, although TMS is an optimal tool to identify an individual motor hotspot for tES, it requires a cumbersome procedure involving the empirical judgment of an expert. In this study, we proposed a convolutional neural network (CNN)-based