Objective.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 (EEGBCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. Approach. 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 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 BCIbased analysis of the brain. Main results. (1) EEGG was more robust than typical “CSP+” algorithms for the poorquality 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. Significance. 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.

Artificial neural networks (ANNs) have won numerous contests in pattern recognition, machine learning, and artificial intelligence in recent years. The neuron of ANNs was designed by the stereotypical knowledge of biological neurons 70 years ago. Artificial Neuron is expressed as f(wx+b) or f(WX). This design does not consider dendrites’ information processing capacity. However, some recent studies show that biological dendrites participate in the pre-calculation of input data. Concretely, biological dendrites play a role in extracting the interaction information among inputs (features). Therefore, it may be time to improve the neuron of ANNs. In this study, some dendritic modules with excellent properties are proposed and added to artificial neurons to form new neurons named Gang neurons. E.g., The dendrite function can be expressed as Wi,i-1Ai-1 ○ A0|1|2|…|i-1 . The generalized new neuron can be expressed as f(W(Wi,i-1Ai-1 ○ A0|1|2|…|i-1)).The simplified new neuron be expressed as f(∑(WA ○ X)). After improving the neurons, many networks can be tried. This paper shows some basic architecture for reference in the future. Up to now, others and the author have applied Gang neurons to various fields, and Gang neurons show excellent performance in the corresponding fields. Interesting things: (1) The computational complexity of dendrite modules (Wi,i-1Ai-1 ○ Ai-1) connected in series is far lower than Horner’s method. Will this speed up the calculation of basic functions in computers? (2) The range of sight of animals has a gradient, but the convolution layer does not have this characteristic. This paper proposes receptive fields with a gradient. (3) The networks using Gang neurons can delete Fully-connected Layer. In other words, the parameters in Fully-connected Layers are assigned to a single neuron, which reduces the parameters of a network for the same mapping capacity. (4) ResDD(ResDD modules+One Linear module) can replace the current ANNs’ Neurons. ResDD has controllable precision for better generalization capability. Gang neuron code is available at https://github.com/liugang1234567/Gang-neuron.