Jin Woo Choi

Visual information plays an essential role in enhancing neural activity during mental practices. Previous research has shown that using different visual scenarios during mental practices that involve imagining the movement of a specific body part may result in differences in performance. Many of these scenarios utilize the concept of embodiment, or one's observation of another entity to be a part of oneself, to improve practice quality of the imagined body movement. We therefore hypothesized tha

Recent advancements in immersive virtual reality head-mounted displays allowed users to better engage with simulated graphical environments. Having the screen egocentrically stabilized in a way such that the users may freely rotate their heads to observe virtual surroundings, head-mounted displays present virtual scenarios with rich immersion. With such an enhanced degree of freedom, immersive virtual reality displays have also been integrated with electroencephalograms, which make it possible t

Mapping drivers’ thoughts directly to mobility system control would make driving more intuitive as if the mobility system is an extension of their own body. Such a system would allow patients with motor disabilities to drive, as it would not require any physical movement. In this article, we therefore propose a brain-controlled mobility system that analyzes real-time neural signals elicited from motor imagery, an imagination of different body movements. As such asynchronous brain–computer interf

This paper describes a real-time human action recognition system that tracks multiple persons and recognizes each human action through image sequences acquired from single surveillance camera. In particular, given an image, blobs are segmented by the Mixture of Gaussians with hierarchical data structure. And we track people by estimating the state to which each blob belongs and assigning people according to its state. Then we make Motion History Images for tracked people and recognize actions us

Brain-computer interfaces allow direct control over devices without any physical action by the user. Motor imagery-based brain-computer interfaces analyze spatial patterns from brain signals elicited when the user imagines execution of a specific behavior. One of the ways to obtain such brain signals is with electroencephalography, which measures signals over the scalp. In this paper, we analyzed the brain patterns from when the users performed different motor imagery tasks and applied them to n

Most user interfaces require motion; the motor-impaired therefore do not have many options to choose from. Electroencephalography (EEG) and the analysis of eye movement are two of the commonly proposed methods for enhancing the user experiences of the motor-impaired. In this paper, we propose an electrooculography (EOG)/EEG-based hybrid BCI that combines the strengths of EOG and EEG by using them simultaneously. We have shown the effectiveness of EOG/EEG-based hybrid BCIs by implementing the pro

Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performa

전신마비 환자, 루게릭병 환자 등 신체를 자유자재로 움직이지 못하는 사람에게 눈 동작은 자율적으로 사용할 수 있는 몇 되지 않는 신체의 일부이다. 이러한 이유로, 사람의 눈 동작 분석에 사용되는 전기안구법(EOG, Electrooculogram)신호는 신체가 불편한 사람들의 각종 기기제어를 돕기 위해 다방면으로 활용된다. 본 논문에서는 망막의 전위를 측정하는 전기안구법 신호를 활용한 눈 깜빡임 검출 알고리즘을 제안한다. 본 연구는 또한 이 논문에서 서술한 눈 깜빡임 검출방법을 활용하여 좌측 눈 깜빡임과 우측 눈 깜빡임, 양측 눈 깜빡임과 눈 뜬 상태를 분류하고, 이를 체스게임 제어에 도입하여 알고리즘의 정확도를 측정한다. 본 논문에서 제시한 눈 깜빡임 검출 방법을 기반으로 환자들이 게임 콘텐츠를 적극적으로 활용할 수 있는 계기가 만들어질 것으로 기대된다.

<p>Decoding motor imagery, an imagination of body movement, is an essential component for electroencephalogram (EEG) based brain-computer interface (BCI) applications. Recent studies have shown that acquiring brain signals within immersive VR environments can enhance motor imagery performance with more discriminant neural patterns. However, applying such signals directly to train classification models for real-life BCI systems may have limitations, as exposure to different environments may

Discriminating concentration of a user is one of the few tasks that non-invasive BCIs can be applied in real-life situations. To have EEG-based BCIs more accessible to users, attempts have been made in terms of both hardware, where EEG acquisition devices have been redesigned to be more affordable and comfortable to wear, and software, where better algorithms have been introduced to improve the interface’s performance. For concentration discrimination, a task highly relevant to EEG signals from

Brain-computer interfaces (BCIs) rely on accurate classification of a user's intent in order to perform the correct actions. However, when used in reality, devices controlled by BCIs may often react differently from what the user intended due to noise and other factors resulting in misclassification. In such cases, error-related potentials (ErrPs) may be evoked and can be captured from the user's neural signals. Detection of these ErrPs can then be used to recognize and correct erroneous respons