EEGG: An analytic brain-computer interface algorithm

Abstract

<div><div><b>Objective.</b> In the traditional sense, the modeling approaches can be divided into white-box (physics-based), black-box (data-driven), and gray-box (the combination of physics-based and data-driven). Because the human brain is a black box itself, the EEG-BCI algorithm is generally a data-driven approach. It generates a black-box or gray-box (e.g., "Visualizing convolutional networks") model. However, one black- or gray-box cannot completely explain the brain. This paper presents the first analytic "white-box" EEG-BCI algorithm using Gang neurons (EEGG).</div><div><br></div><div><b>Approach.</b> Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relationship frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neuron. A total of 4,906 EEG data about motor imagery (MI) of left-hand and right-hand movements from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG's classification performance according to cross-subject accuracy. Secondly, this paper transformed the EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified through the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCI-based analysis of brain.</div><div><br></div><div><b>Main results.</b> (1) EEGG was more robust than typical "CSP+" algorithms for the data of poor quality [AUC:0.825±0.074(EEGG)>0.745±0.094(CSP+LDA)/0.591±0.104(CSP+Bayes)/0.750±0.091(CSP+SVM), p<0.001]. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that brain regions' interactive components put a brake on ERD/ERS effects for classification (p<0.001). This means that generating fine hand intention needs more centralized activation in the brain.</div><div><br></div><div><b>Significance.</b> EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting Taylor series, rather than the fuzzy interpretation of outputs, which offers a novel frame for analysis of the brain.</div></div><div><p></p></div>

It may be time to improve the neuron of artificial neural network

“…The key model in this paper(Gang neuron) This paper used Gang neuron. Study using Gang neurons [51]:"An Algorithm for Precipitation Correction in Flood Season Based on Dendritic Neural Network"(see Fig. 34)…”