Abstract
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.