Early diagnosis of Alzheimer’s disease is crucial to stall the deterioration of brain function, but conventional diagnostic methods require complicated analytical procedures or inflict acute pain on the patient. Then, label-free Surface-enhanced Raman spectroscopy (SERS) Read more…
For stroke survivors, diminished hand functions limit their ability to perform Activities of Daily Living (ADLs). To help stroke survivors perform rehabilitation exercises, soft robotic gloves have been utilized to assist them in actively practicing Read more…
A universal and rapid bacteria detection method in diverse target media is imperative for food safety analysis and diagnosis of infectious diseases. Surface-enhanced Raman spectroscopy (SERS) shows prospective as a potentially practical method to rapidly Read more…
Learning from human feedback using event related electroencephalography (EEG) signals has attracted extensive attention recently owing to their intuitive communication ability by decoding user intentions. However, this approach requires users to perform specified tasks and Read more…
We develop a wearable ear-EEG acquisition tool and directly compared its performance with a conventional 32-channel scalp-EEG setup in a multi-class speech imagery classification task. Our system uses Riemannian tangent space projection of EEG covariance Read more…
Visual information plays an essential role in enhancing neural activity during mental practices. This study therefore hypothesized that applying immersive virtual reality headsets, with their ability to provide rich immersion and illusion by presenting egocentrically Read more…
Depression is a complicated mental illness that varies in symptoms and needs the cooperation of patients. We propose a novel data-driven attempt to diagnose Major Depression Disorder (MDD) based on clinical questionnaire. It includes deep Read more…
Developing Soft Robotics To Assist Users With Hand-Object Interactions [의도 파악하는 웨어러블 손 로봇 관련 기사들] For hand robots that assist with activities of daily living, this study presents a paradigm for learning-based intention Read more…
We propose a robust physiological model for the recognition of human emotions, which is based on a Convolutional Long Short-Term Memory network and a temporal margin-based loss function. Formulating the emotion recognition problem as Read more…
We develop deep learning algorithms to predict mental depression by observing medical data available. Ongoing. Funded by NRF of Korea.[LINK]