Lateral ventricular brain–computer interface system with lantern-inspired electrode for stable performance and memory-guided decoding

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Authors: Yike Sun, Yaxuan Gao, Kewei Wang, Jingnan Sun, Yuzhen Chen, Changxing Huang, Xiaoyang Li, Yanan Yang, Tianhua Zhao, Haochen Zhu, R. T. Liu, Xiaogang Chen
+2 more Bai Lu, Xiaorong Gao
Cited by: 0
References: 48

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 electrocorticography (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 the 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.

Fields of Study & Subjects

Neuroscience and Neural Engineering EEG and Brain-Computer Interfaces Neural dynamics and brain function

Publication details

Journal / source
National Science Review
Publication date
4 February 2026
Language
EN