Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface

TL;DR

This paper introduces a mathematical approach using time series unfolding and polyharmonic signals to evaluate the number of active brain sources in non-invasive brain-computer interfaces, aiding in signal analysis.

Contribution

The paper presents a novel mathematical model and algorithm for estimating the number of brain signal sources from non-invasive BCI data.

Findings

Effective in determining the number of active brain oscillators

Demonstrated on data from a non-invasive BCI system

Provides a new analytical tool for BCI signal analysis

Abstract

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.