一个基于SSVEP的BCI与112个目标使用频率空间复杂化
Yaru Liu1, Wei Dai1, Yadong Liu1
1College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410000, People's Republic of China.
Journal of neural engineering
|April 19, 2024
概括
本研究引入了一种用于大脑计算机接口 (BCI) 的新型频率空间复杂化方法,以增加目标分辨率. 该方法使用图形神经网络来实现稳定状态视觉唤起潜能 (SSVEP) 检测的高精度.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 脑计算机接口 (BCI) 系统在实现高目标分辨率方面面临挑战,这限制了它们的实际应用.
- 基于稳态视觉唤起潜力 (SSVEP) 的BCI为众多目标提供了潜力,但受到刺激竞争的阻碍.
- 改进目标解决方案对于推进BCI能力和满足应用需求至关重要.
研究的目的:
- 在基于SSVEP的BCI中克服刺激竞争的局限性.
- 提出和验证频率空间复杂化方法,以提高目标分辨率.
- 通过提高可访问命令的数量来提高BCI系统的性能.
主要方法:
- 通过将闪刺激安排为2x2矩阵在接口中,开发了一种频率空间复杂化范式.
- 使用拟议的范式设计和测试了三个不同的界面布局.
- 实现了一个图形神经网络,以根据EEG响应模式区分相同频率的目标.
主要成果:
- 11名受试者的实验验证表明了拟议方法的有效性.
- 在三个范式中,线下分类的平均准确率高达91.38%.
- 实现了高信息传输速率 (ITR),最高的信息传输速率为53.96比特/分钟.
结论:
- 频率空间复杂化方法通过利用刺激空间关系,成功地增加了SSVEPBCI中的目标分辨率.
- 开发的图形神经网络有效地区分目标,提高了分类准确性.
- 这种方法为SSVEP检测效率和BCI性能进一步提高提供了基础.
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