BEEE Lab at KAIST

Toward invisible brain-machine interfaces using integrated circuit technologies!

The Biomedical Energy-Efficient Electronics Lab in Bio and Brain Engineering at KAIST aims at implementing and advancing invisible brain-machine interfaces and artificial retinae for diagnostic and therapeutic medical instrumentation using integrated circuit technologies. Our key research interests toward innovative bio-convergent technologies include miniaturized wireless neural interfacing microsystems, and unobtrusive energy-efficient brain and body area sensor networks.

Openings

We are actively looking for self-motivated BEEEers in the following positions:

Graduate students: M.S. and Ph.D. courses, and M.S./Ph.D. integrated. We are planning to hire more students this year!

Post-doctoral fellows: we now have an opening for post-doctoral fellow. Please see these documents. If you have any questions for this openings, please send an email to Prof. Kim. Application period: 2021.12.24-2022.01.17.

If interested in applying, please contact Prof. Kim. Due to the sheer number of applicants, we are unable to respond to all requests. We have many research projects for implementing miniaturized implants, artifical retinae, closed-loop stimulation, wireless power transmission, AI-hardware, and wearable devices for biopontial recording and processing.

News

[2022.09] Premravee Teeravichayangoon has joined BEEE lab!

[2022.08] A new paper, "Zero-Weight aware LSTM Architecture for Edge-Level EEG Classification " will be published in BioCAS

[2022.08] Charnmin has joined BEEE lab!

[2022.08] A new paper, "Seamless Capacitive Body Channel Wireless Power Transmission Toward Freely Moving Multiple Animals in an Animal Cage" will be published in TBioCAS

[2022.03] Eojin, Jaeseong has joined BEEE lab!

Publications

Seamless capacitive body channel wireless power transmission toward freely moving multiple animals in an animal cage




Energy-Efficient Integrated Circuit Solutions Toward Miniaturized Closed-Loop Neural Interface System

Motion Artifact Removal Techniques for Wearable EEG and PPG Sensor Systems