即插即用p300为基础的BCI零训练应用程序。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Jongsu Kim;Sung-Phil Kim
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引用次数: 0

摘要

长期以来,基于p300的脑机接口(bci)的实际部署一直受到用户特定校准和多重刺激重复需求的阻碍。在本研究中,我们构建并验证了一个即插即用、零训练的P300 BCI系统,该系统使用预训练的xDAWN空间滤波器和深度卷积神经网络在单次试验环境中运行。在不进行任何科目适配的情况下,参与者可以通过BCI系统实时控制物联网设备,解码准确率达到85.2%,与线下基准的87.8%相当,证明了即插即用BCI实现的可行性。离线分析显示,一小组顶叶和枕叶电极对解码性能贡献最大,支持低密度、高精度BCI配置的可行性。数据充分性模拟为预训练数据集大小提供了定量指导,误差试验分析表明,刺激时间和准备注意状态都会影响实时解码性能。总之,这些结果证明了在单次试验基础上运行的完全预训练的、零训练的P300脑机接口的实时验证,没有刺激重复或用户特定的校准,并为开发可扩展的、健壮的和用户友好的脑机接口系统提供了实用的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Plug-and-Play P300-Based BCI With Zero-Training Application
The practical deployment of P300-based brain–computer interfaces (BCIs) has long been hindered by the need for user-specific calibration and multiple stimulus repetitions. In this study, we build and validate a plug-and-play, zero-training P300 BCI system that operates in a single-trial setting using a pre-trained xDAWN spatial filter and a deep convolutional neural network. Without any subject-specific adaptation, participants could control an IoT device via the BCI system in real time, with decoding accuracy reaching 85.2% comparable to the offline benchmark of 87.8%, demonstrating the feasibility of realizing a plug-and-play BCI. Offline analyses revealed that a small set of parietal and occipital electrodes contributed most to decoding performance, supporting the viability of low-density, high-accuracy BCI configurations. A data sufficiency simulation provided quantitative guidelines for pre-training dataset size, and an error trial analysis showed that both stimulus timing and preparatory attentional state influenced real-time decoding performance. Together, these results demonstrate the real-time validation of a fully pre-trained, zero-training P300 BCI operating on a single-trial basis, without stimulus repetition or user-specific calibration, and offer practical insights for developing scalable, robust, and user-friendly BCI systems.
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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