在XR-based SSVEP BCIs的刺激生成中系统效应的在线补偿
Leopoldo Angrisani1, Egidio De Benedetto1, Matteo D'Iorio1
1Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio n.21, 80125 Naples, Italy.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了一种新的方法,通过补偿显示刷新率变化来改善扩展现实 (XR) 中的大脑计算机接口 (BCI) 的性能. 这提高了稳定状态视觉唤起潜力 (SSVEP) 分类的准确性,而不需要额外的培训.
科学领域:
- 神经科学是一个神经科学.
- 人与计算机的交互
- 可穿戴技术可穿戴技术
背景情况:
- 使用稳态视觉唤起潜能 (SSVEP) 的脑计算机接口 (BCI) 显示出可穿戴应用的前景.
- 扩展现实 (XR) 环境对BCI分类提出了挑战,原因是刺激呈现不一致,特别是显示刷新率变化.
研究的目的:
- 引入和评估XR显示刷新率 (RR) 变化的新型在线补偿方法.
- 提高基于SSVEP的BCI在XR环境中的分类准确度,无需额外的培训或侵入性程序.
主要方法:
- 开发了一种非侵入性监控模块,用于检测和补偿XR显示器中的率偏差.
- 使用过器银行法定相关性分析 (FBCCA) 评估分类性能.
- 使用了两个数据集,分别包括9名和30名受试者,采集使用Moverio BT-350和HoloLens 2.
主要成果:
- 补偿方法显著提高了SSVEP分类准确性,改善与每秒 (fps) 偏差的大小相关.
- 在某些情况下,分类准确度提高了高达300%.
- 统计分析证实了该方法在各个受试者和数据集中的可靠性.
结论:
- 拟议的方法有效地增强了在XR环境中运行的基于SSVEP的BCI.
- 这种方法为可靠和实用的XR BCI应用提供了坚实的基础.
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