A Multimodal Neural Network for Contact State Recognition during Probe Implantation into Skull Holes
文献类型：会议论文
| 作者 | Song YJ(宋雨佳) ; Wang XF(王啸峰); Zhang DP(张大朋)
|
| 出版日期 | 2023 |
| 会议日期 | 2023-8 |
| 会议地点 | 新西兰 |
| 英文摘要 | Brain-machine interfaces (BMIs) have attracted wide attention, where invasive BMIs can obtain higher-quality signals compared to non-invasive ones. In invasive BMIs, flexible electrodes for acquiring signals are usually implanted with the assistance of probes. However, due to the orientation error, the probe may collide with the skull wall during implantation.
Unlike typical insertion problems, it is difficult to model the interaction forces due to the low stiffness of the probe. To avoid physical modeling, previous approaches leverage force sensor data to identify contact states, thus can adjust the orientation. However, solely relying on the force sensor is insufficient to accurately distinguish the contact states of probes. Therefore, we propose the multimodal Contact State Recognition Network
(multimodal CSRNet) that incorporates both binocular RGB images and force sensor data as input. Notably, our paper is the first to investigate the problem of contact state recognition during probe implantation into skull holes. Besides, experiment results show that the proposed multimodal CSRNet relatively enhances the performance by 28.8% and 61.1% than its image based and force-based counterparts. By performing few-shot transfer learning on unseen holes, it can achieve an accuracy of 89.9% with only 90 samples in about 43s training time. |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/57617]  |
| 专题 | 脑机接口与融合智能 |
| 通讯作者 | Zhang DP(张大朋) |
| 作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
| 推荐引用方式 GB/T 7714 | Song YJ,Wang XF,Zhang DP. A Multimodal Neural Network for Contact State Recognition during Probe Implantation into Skull Holes[C]. 见:. 新西兰. 2023-8. |
入库方式： OAI收割
来源：自动化研究所
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