EEGG: An Analytic Brain-Computer Interface Algorithm.

Gang Liu,Jing Wang

doi:10.1109/tnsre.2022.3149654

Abstract

Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEG-BCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relation frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery (MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG's classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCI-based analysis of the brain. (1) EEGG was more robust than typical "CSP+" algorithms for the low-quality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (in analogy with the data-driven but human-readable Fourier transform and frequency spectrum), which offers a novel frame for analysis of the brain.

Highlights

Manuscript received 10 August, 2021; revised 18 January, 2022; accepted 04 February, 2022
EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series, which offers a novel frame for analysis of the brain
The variance of AUC of EEGG is smaller than others, which means that EEGG maybe has better robustness

Summary

Introduction

Manuscript received 10 August, 2021; revised 18 January, 2022; accepted 04 February, 2022. The model of the biological EEG-intention system is at the heart of BCI technology. The modeling approaches can be classified into the physics-based whitebox approach, data-driven black-box approach, and physicsand data-driven gray-box approach [5], [6]. Biologists and clinicians prefer the white-box models because they are easy to understand and can be used to analyze biological mechanisms [7]. Most of the existing EEG-based BCIs are datadriven because specific physical properties of intention are still a mystery. Restricted by data-driven basic machine learning (ML) algorithms(e.g., Support Vector Machines (SVM) [8], Neural Network (NN), and Bayesian Classifier [9]), traditional EEG-based BCIs usually model the biological EEG-intention system as a black box [8], [10]

Objectives

Methods

Results

Conclusion