Lateral Ventricular Brain-Computer Interface System with Lantern-Inspired Electrode for Stable Performance and Memory-Guided Decoding

Abstract We present a lateral ventricular brain-computer interface (LV-BCI) that deploys an expandable, flexible electrode into the lateral ventricle through a minimally invasive external ventricular drainage pathway. Inspired by the framework of traditional Chinese lanterns, the electrode expands uniformly within the ventricle and conforms to the ependymal wall. Compared with conventional subdural ECoG electrodes, the LV-BCI shows superior signal stability and immunocompatibility. Resting-state spectral analyses revealed a maximum effective bandwidth comparable to subdural ECoG. In evoked potential tests, the LV-BCI maintained a consistently higher signal-to-noise ratio over 112 days without the decline typically associated with scarring or other immune responses. Immunohistochemistry showed only a transient, early microglial activation after implantation, returning to control levels and remaining stable through 168 days. We further designed an “action-memory T-maze” task and developed a microstate sequence classifier (MSSC) to predict rats’ left/right turn decisions. The LV-BCI achieved prediction accuracy up to 98%, significantly outperforming subdural ECoG, indicating enhanced access to decision-related information from deep structures such as the hippocampus. These results establish the lateral ventricle as a viable route for neural signal acquisition. Using a lantern-inspired flexible electrode, we achieve long-term stable recordings and robust memory-guide decision decoding from within the ventricular system, opening new directions for BCI technology and systems neuroscience.

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Publication Details

Journal
National Science Review
Published
2026-02-04
DOI
https://doi.org/10.1093/nsr/nwag081
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
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article

Lateral Ventricular Brain-Computer Interface System with Lantern-Inspired Electrode for Stable Performance and Memory-Guided Decoding

Yuzhen Chen, R. T. Liu, Bingxiong Lu, Yike Sun et al.
National Science Review
EEG and Brain-Computer Interfaces
article

Lateral Ventricular Brain-Computer Interface System with Lantern-Inspired Electrode for Stable Performance and Memory-Guided Decoding

Yuzhen Chen, R. T. Liu, Bingxiong Lu, Yike Sun, Xi Chen, KeWei Wang, Tianhua Zhao, Xiaoyang Li, Changxing Huang, Yaxuan Gao, Xiaorong Gao, Jingnan Sun, Haochen Zhu, Yanan Yang
article en

Abstract

Abstract We present a lateral ventricular brain-computer interface (LV-BCI) that deploys an expandable, flexible electrode into the lateral ventricle through a minimally invasive external ventricular drainage pathway. Inspired by the framework of traditional Chinese lanterns, the electrode expands uniformly within the ventricle and conforms to the ependymal wall. Compared with conventional subdural ECoG electrodes, the LV-BCI shows superior signal stability and immunocompatibility. Resting-state spectral analyses revealed a maximum effective bandwidth comparable to subdural ECoG. In evoked potential tests, the LV-BCI maintained a consistently higher signal-to-noise ratio over 112 days without the decline typically associated with scarring or other immune responses. Immunohistochemistry showed only a transient, early microglial activation after implantation, returning to control levels and remaining stable through 168 days. We further designed an “action-memory T-maze” task and developed a microstate sequence classifier (MSSC) to predict rats’ left/right turn decisions. The LV-BCI achieved prediction accuracy up to 98%, significantly outperforming subdural ECoG, indicating enhanced access to decision-related information from deep structures such as the hippocampus. These results establish the lateral ventricle as a viable route for neural signal acquisition. Using a lantern-inspired flexible electrode, we achieve long-term stable recordings and robust memory-guide decision decoding from within the ventricular system, opening new directions for BCI technology and systems neuroscience.

National Science Review
Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Beihang University (CN), Tsinghua University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 65%
EEG and Brain-Computer Interfaces
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