用多谐波模型分析脑机接口信号，判断活跃脑区数量
Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface
- 基于多谐波信号建模，解析非侵入式脑机接口数据
- 可有效识别大脑中活跃的信号源数量，提升解码精度
- 适用于脑机接口信号源定位，对神经科学应用有参考价值
本文简要介绍时间序列展开方法背后的数学理论。所提出的算法为分析脑机接口采集的数据提供了有价值的数学与分析工具。本研究基于多谐波信号构建数学模型，用于解释脑机接口传感器的数据。分析基于多谐波信号形式的信号数学模型。主要关注于评估信号源数量或活跃脑振荡器的问题。该方法的有效性通过作者开发的非侵入式脑机接口记录的数据分析得到验证。
原文摘要 · Abstract (English)
This paper provides a brief introduction of the mathematical theory behind the time series unfolding method. The algorithms presented serve as a valuable mathematical and analytical tool for analyzing data collected from brain-computer interfaces. In our study, we implement a mathematical model based on polyharmonic signals to interpret the data from brain-computer interface sensors. The analysis of data coming to the brain-computer interface sensors is based on a mathematical model of the signal in the form of a polyharmonic signal. Our main focus is on addressing the problem of evaluating the number of sources, or active brain oscillators. The efficiency of our approach is demonstrated through analysis of data recorded from a non-invasive brain-computer interface developed by the author.
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