S
Sungho Jo
Researcher at KAIST
Publications - 116
Citations - 2111
Sungho Jo is an academic researcher from KAIST. The author has contributed to research in topics: Robot & Brain–computer interface. The author has an hindex of 20, co-authored 108 publications receiving 1438 citations. Previous affiliations of Sungho Jo include Seoul National University & Massachusetts Institute of Technology.
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Journal ArticleDOI
Toward Brain-Actuated Humanoid Robots: Asynchronous Direct Control Using an EEG-Based BCI
TL;DR: The experimental results showed that the subjects successfully controlled the humanoid robot in the indoor maze and reached the goal by using the proposed asynchronous EEG-based active BCI system.
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A deep-learned skin sensor decoding the epicentral human motions
TL;DR: A new measuring system, a novel electronic skin integrated with a deep neural network that captures dynamic motions from a distance without creating a sensor network, and is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics.
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A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition.
Bongjae Choi,Sungho Jo +1 more
TL;DR: An approach that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system is described.
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Quadcopter flight control using a low-cost hybrid interface with EEG-based classification and eye tracking
TL;DR: A wearable hybrid interface where eye movements and mental concentration directly influence the control of a quadcopter in three-dimensional space is proposed to support users to complete their complicated tasks in a constrained environment in which only visual feedback is provided.
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Use of Deep Learning for Characterization of Microfluidic Soft Sensors
TL;DR: This research implemented a hierarchical recurrent sensing network, a type of recurrent neural network model, to the calibration of soft sensors for estimating the magnitude and the location of a contact pressure simultaneously.