2022
DOI: 10.1109/tnsre.2022.3149654
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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 (EEG-BCI 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… Show more

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Cited by 8 publications
(2 citation statements)
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References 48 publications
(71 reference statements)
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“…positioning and decoding, etc) to physical movements [4]. In previous studies, by proposing and validating the capacity of the electroencephalogram (EEG)-Gang neuron algorithm in decomposing the brain into motor components, Liu et al also confirmed the contributions of the concentrated brain activity to the generation of fine hand intention [5]. These consistently indicated that recent researchers have paid great attention to the interaction within and between the central nervous system and peripheral motor nerves [6][7][8], and specifically, the potential interactions between the cortex and the muscle within the beta band during muscle contraction have been already reported [9,10].…”
Section: Introductionmentioning
confidence: 92%
“…positioning and decoding, etc) to physical movements [4]. In previous studies, by proposing and validating the capacity of the electroencephalogram (EEG)-Gang neuron algorithm in decomposing the brain into motor components, Liu et al also confirmed the contributions of the concentrated brain activity to the generation of fine hand intention [5]. These consistently indicated that recent researchers have paid great attention to the interaction within and between the central nervous system and peripheral motor nerves [6][7][8], and specifically, the potential interactions between the cortex and the muscle within the beta band during muscle contraction have been already reported [9,10].…”
Section: Introductionmentioning
confidence: 92%
“…In order to extend the concept of Granger causality to highdimensional human brain systems, there have been numerous statistics-based measures proposed, such as the directed transfer function [6], partial directed coherence [7] and spectral Granger causality [8]. In addition, data-driven machine-learning algorithms, including Dendrite Net and EEGG, have been introduced to model the brain as a white box [9,10]. Despite the existence of numerous methods, the information theory has achieved great success in uncovering the fundamental computational components in complex systems [11].…”
Section: Introductionmentioning
confidence: 99%