Publications by Kyuhwa Lee | EPFL Graph Search

Learning to control a BMI-driven wheelchair for people with severe tetraplegia

José del Rocio Millán Ruiz, Luca Tonin, Serafeim Perdikis, Bastien Orset, Kyuhwa Lee

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the h ...

Cell Press2022

User Adaptation to Closed-Loop Decoding of Motor Imagery Termination

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Bastien Orset, Kyuhwa Lee

One of the most popular methods in non-invasive brain machine interfaces (BMI) relies on the decoding of sensorimotor rhythms associated to sustained motor imagery. Although motor imagery has been intensively studied, its termination is mostly neglected. O ...

2021

Workshops of the seventh international brain-computer interface meeting: not getting lost in translation

Ricardo Andres Chavarriaga Lozano, Anton Nijholt, Kyuhwa Lee

The Seventh International Brain-Computer Interface (BCI) Meeting was held May 21-25th, 2018 at the Asilomar Conference Grounds, Pacific Grove, California, United States. The interactive nature of this conference was embodied by 25 workshops covering topics ...

TAYLOR & FRANCIS LTD2019

EEG-based lower-limb movement onset decoding: Continuous classification and asynchronous detection

Mohamed Bouri, Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Kyuhwa Lee, Fumiaki Iwane, Dong Liu

Brain-machine interfaces (BMIs) have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography (EEG). Active neural participation is crucial to promote brain plasticity thus to enhance the ...

2018

Endogenous Control of Powered Lower-Limb Exoskeleton

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Kyuhwa Lee, Dong Liu

We present an online decoding method for controlling a powered lower-limb exoskeleton using endogenously generated electroencephalogram (EEG) signals of human users. By performing a series of binary classifications, users control the exoskeleton in three d ...

Springer Int Publishing Ag2017

Brain-actuated gait trainer with visual and proprioceptive feedback

Mohamed Bouri, Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Kyuhwa Lee, Dong Liu

Objective. Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes moto ...

Institute of Physics2017

An EEG-based brain-computer interface for gait training

José del Rocio Millán Ruiz, Kyuhwa Lee, Dong Liu

This work presents an electroencephalography (EEG)-based Brain-computer Interface (BCI) that decodes cerebral activities to control a lower-limb gait training exoskeleton. Motor imagery (MI) of flexion and extension of both legs was distinguished from the ...

2017

A Brain-Controlled Exoskeleton with Cascaded Event-Related Desynchronization Classifiers

Ricardo Andres Chavarriaga Lozano, José del Rocio Millán Ruiz, Kyuhwa Lee, Dong Liu, Dong Liu

This paper describes a brain-machine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the user’s sensorimotor cortical areas. We train a binary decoder that can distinguish two d ...

Elsevier2017