Heung‐Il Suk
고려대학교 의과대학 · 신경과학
Heung-Il Suk 교수의 연구실은 뇌-컴퓨터 인터페이스(BCI)와 신경영상 분석을 중심으로, 전기ence팔라그램(EEG) 및 fMRI를 활용한 뇌 기반 진단 기술을 개발하고 있습니다. 특히, 다중 피처 통합, 비모수적 확률 모델링, 다중 작업 학습 기반의 특징 추출 기법을 통해 뇌 전기 활동의 개인별 이질성과 복잡한 분포 특성을 정밀하게 분석합니다. 연구는 주로 뇌 질환(우울증, 알츠하이머병 등)의 객관적 진단 및 뇌 기능 연결성 분석에 초점을 맞추고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In
Abstract EEG‐based discrimination among motor imagery states has been widely studied for brain‐computer interfaces (BCIs) due to the great potential for real‐life applications. However, in terms of designing a motor imagery‐based BCI system, a lot of research in the literature either uses a frequency band of interest selected manually based on the visual analysis of EEG data or is set to a general broad band, causing performance degradation in classification. In this article, we propose a novel
Recently, spatio-temporal filtering to enhance decoding for Brain-Computer-Interfacing (BCI) has become increasingly popular. In this work, we discuss a novel, fully Bayesian-and thereby probabilistic-framework, called Bayesian Spatio-Spectral Filter Optimization (BSSFO) and apply it to a large data set of 80 non-invasive EEG-based BCI experiments. Across the full frequency range, the BSSFO framework allows to analyze which spatio-spectral parameters are common and which ones differ across the s
Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting-state functional magnetic resonance imaging (rs-fMRI) and estimate functional connectivity for brain-disease identification. Recently, attempts have been made to investigate effective connect
In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak distribution of classes. The rationale for our approach is that it is highly likely for neuroimaging data to have multiple peaks or modes in distribution, e.g., mixture of Gaussians, due to t
