Low-Noise EEG Sensor and Neural Stimulator : For Motion and Stimulation Artifact Removal

  • Geunchang Seong Korea Advanced Institute of Science and Technology (KAIST)
  • Dongyeol Seok Korea Advanced Institution of Science and Technology (KAIST)
  • Minjae Kim Korea Advanced Institution of Science and Technology (KAIST)
  • Chul Kim Korea Advanced Institution of Science and Technology (KAIST)
Keywords: biopotential recording, neural recording, stimulation artifact, motion artifact, electroencephalography

Abstract

This work aims to develop a neural interface system that enables electroencephalogram (EEG) measurement in wearable equipment and eliminates stimulation artifacts by processing in miniaturized neural stimulation modules. The ASIC implements a 4-channel analog-front-end (AFE) with a high input-Z buffer, 2nd order oversampling delta-sigma ADC, stimulation noise cancellation digital processors, and digitally controlled neural stimulators into the chip. The simplified stimulation artifact rejection algorithm implemented on the digital block allows users to acquire pure neural signals while stimulating. The 3 mm x 1 mm IC chips were fabricated through the TSMC 65 nm LP process.

Author Biographies

Geunchang Seong, Korea Advanced Institute of Science and Technology (KAIST)

Geunchang Seong received the B.S. degree in bio and brain engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea, in 2020 and an M.S. degree in bio and brain engineering from KAIST, in 2022. He is currently pursuing a Ph.D. degree in bio and brain engineering from KAIST. His research interests include bio-signal processing, and integrated circuit (IC) chip design for biomedical healthcare systems.

Dongyeol Seok, Korea Advanced Institution of Science and Technology (KAIST)

Dongyeol Seok received the B.S. degree in bio and brain engineering (major) and science and technology policy (minor) from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea, in 2020 and an M.S. degree in bio and Brain engineering from KAIST in 2022. He is currently pursuing a Ph.D. degree in bio and brain engineering, at KAIST. His research interests include EEG analog front-end, development of motion artifact-free EEG systems, and integrated circuit (IC) chip design for biomedical healthcare systems.

Minjae Kim, Korea Advanced Institution of Science and Technology (KAIST)

Minjae Kim received a B.S. degree in bio and brain engineering (major) and electrical engineering (minor) from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea, in 2021 and an M.S. degree in bio and brain engineering from KAIST, in 2023. He is currently pursuing a Ph.D. degree in bio and brain engineering at KAIST. His research interests include in-ear EEG technology, the development of motion artifact removal in EEG sensors, and integrated circuit (IC) chip design for biomedical healthcare systems.

Chul Kim, Korea Advanced Institution of Science and Technology (KAIST)

Chul Kim (Senior Member, IEEE) is an associate professor in the Department of Bio and Brain Engineering and the Program of Brain and Cognitive Engineering at Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea. He received the Ph.D. degree in  bioengineering, UC San Diego, La Jolla, CA, USA, in 2017 where he was a postdoctoral fellow from 2017 to 2019. From 2009 to 2012, he was with SK HYNIX, Icheon, South Korea, where he designed power management circuitry for dynamic random-access memory(DRAM). His current research interests include the design of energy-efficient integrated circuits and systems for fully wireless brain-machine interfaces and unobtrusive wearable sensors.

Homepage : https://beee.kaist.ac.kr/

Published
2025-01-01
How to Cite
Seong, G., Seok, D., Kim, M., & Kim, C. (2025). Low-Noise EEG Sensor and Neural Stimulator : For Motion and Stimulation Artifact Removal. Journal of Integrated Circuits and Systems, 11(1). https://doi.org/10.23075/jicas.2025.11.1.004
Section
Articles