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From Perception to Action:brain-to-brain Information Transmission of Pigeons

Journal of Bionic Engineering 2024年第6期21卷 2913-2923页

作者： Lifang Yang Long Yang Haofeng Wang Mengmeng Li Zhigang Shang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou450001China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001China

Along with the flourishing of brain-computer interface technology,the brain-to-brain information transmission between different organisms has received high attention in recent ***,specific information transmission mode and implementation technology need to be further *** this paper,we constructed a brain-to-brain information transmission system between pigeons based on the neural information decoding and electrical stimulation encoding *** system consists of three parts:(1)the“perception pigeon”learns to distinguish different visual stimuli with two discrepant frequencies,(2)the computer decodes the stimuli based on the neural signals recorded from the“perception pigeon”through a frequency identification algorithm(neural information decoding)and encodes them into different kinds of electrical pulses,(3)the“action pigeon”receives the Intracortical Microstimulation(ICMS)and executes corresponding key-pecking actions through discriminative learning(electrical stimulation encoding).The experimental results show that our brain-to-brain system achieves information transmission from perception to action between two pigeons with the average accuracy of about 72%.Our study verifies the feasibility of information transmission between inter-brain based on neural information decoding and ICMS encoding,providing important technical methods and experimental program references for the development of brain-to-brain communication technology.

关键词： Pigeon Neural Information Decoding Electrical Stimulation Encoding Intracortical Microstimulation brain-to-brain Information Transmission

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Relationship Between the Long-Term Practice of Tai Chi Stance with Neural Patterns of the brain: An Exploratory Study

Relationship Between the Long-Term Practice of Tai Chi Stanc...

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2024 China Automation Congress, CAC 2024

作者： Li, Mengmeng Zhang, Jianbo Wan, Hong School of Electrical and Information Engineering Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350368604

Research has shown that practicing Tai Chi can alleviate brain tension and enhance the activity of the autonomic nervous system in the human body. However, there is limited research on the effects of Tai Chi Stance on brain function. This study aims to analyze the electroencephalogram (EEG) characteristics of long-term Tai Chi Zhuang practitioners and investigate the changes in brain dynamic neural patterns during different stages of Tai Chi Stance, together with the differences between long-term practitioners and a control group. In this study, we first analyzed the changes in EEG spectral characteristics during different stages of Tai Chi Stance practice of long-term practitioners. We found that the alpha (8-13 Hz) power significantly increased during the stance, indicating a state of deep relaxation during the Tai Chi practice. The experimental group also exhibited significant changes in brain functional network parameters in the alpha band during the stance, including increased clustering coefficient and global efficiency. Finally, inter-group differences in EEG characteristic parameters were analyzed between the experimental group and control group. The results showed significant differences in relative power and functional connectivity in the alpha band between the two groups, indicating that long-term Tai Chi Stance practice can effectively enhance brain network efficiency and improve information integration capabilities. This study provides scientific evidence for Tai Chi Stance training to enhance brain health. © 2024 IEEE.

关键词： Electroencephalography

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Digital interpretation of Tai Chi basic skills based on surface electromyography feature analysis 4

Digital interpretation of Tai Chi basic skills based on surf...

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4th International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Li, Mengmeng Gu, Zhiyuan Wan, Hong School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

ISBN: (纸本)9781510682443

Basic Tai Chi exercises serve as the foundation and core of Tai Chi practice. A deep understanding of the movement principles and essential characteristics is crucial to ensuring the effectiveness of training. However, practitioners often struggle to grasp the essentials, resulting in minimal progress or even injuries that may lead them to abandon the practice. To achieve a digital interpretation of the movement transitions and force application principles in basic Tai Chi exercises, we introduced surface electromyography (sEMG) detection into basic Tai Chi in this study. Eight Tai Chi experts were invited to construct an expert subject dataset. Then we extracted and analyzed the integral whole electric value and contribution rates of relevant muscles in basic Kai He Gong (KHG) exercise, as well as the dynamic changes on both sides of the body. An improved Teager-Kaiser Energy (TKE) algorithm was used to accurately identify the muscle force sequencing in expert-level and trainee-level basic movement. The results revealed that the cooperated relationship the left and right body sides and the crucial role of the erector spinae in the waist connecting the upper and lower limbs in the KHG exercises, demonstrating the Tai Chi principle of "controlling from the waist". In all, we explored the sEMG activity patterns in basic Tai Chi exercises in this study, enabling a digital interpretation of movement transitions and muscle force application principles. The results could provide references for the training and teaching of basic Tai Chi exercises. © 2024 SPIE.

关键词： Electromyography

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Striatal Local Field Potential Dynamics During Decision-Making Learning in Pigeons 24

Striatal Local Field Potential Dynamics During Decision-Maki...

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16th International Conference on Bioinformatics and Biomedical technology, ICBBT 2024

作者： Jin, Fuli Wang, Yinghui Shang, Zhigang School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

ISBN: (纸本)9798400717666

Decision-making is a complex cognitive process vital for the survival and adaptive behavior of species, and the neural mechanisms behind this behavior have been the focus of extensive *** striatum is a key region in the brain that plays a crucial role in the decision-making process, making it an ideal candidate for investigating the neural correlates of learning and *** study aims to explore the changes in the striatum and its local field potentials (LFPs) during the learning of decision-making, providing potential insights into the role of the striatum in decision-making in *** study simultaneously collected behavior data and striatal LFPs from 4 pigeons during a three-armed bandit *** pigeons' learning process, behavioral accuracy, reward probabilities, and"win-stay-lose-shift" characteristics were *** power changes in six different frequency bands of striatal LFPs between the early and late stages of learning were *** pigeons' learning process significantly modulated the power in the Beta frequency band, indicating its crucial role in ***, the power of the Theta, Alpha, and Beta bands correlated with choices suggesting a high reward probability, emphasizing the relationship between reward probability and the modulation of power in the *** study sheds light on the neural processes underlying decision-making in pigeons, demonstrating the modulation of LFP power in the striatum and its relationship to reward *** findings contribute to our understanding of decision-making processes in animals. © 2024 Copyright held by the owner/author(s).

关键词： Decision making

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Two-step Thompson Sampling for Multi-Armed Bandit Problem 43

Two-step Thompson Sampling for Multi-Armed Bandit Problem

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43rd Chinese Control Conference, CCC 2024

作者： Shang, Zhigang Yang, Lifang Li, Mengmeng Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

ISBN: (纸本)9789887581581

An important feature of sequential decision-making under uncertain conditions is to strike a balance between exploration (obtaining information about the value of actions) and utilization (selecting the best action based on existing knowledge). The multi-armed bandit is a classic task for balancing their relationship. As the agent has learnt the relationship between two options in a 2-armed bandit task, it will no longer explore but instead use them directly. Inspired by the research, an algorithm based on two-step Thompson sampling (T-TS) is proposed to solve multi-armed bandit problem. First, posterior Beta distributions for the reward of each arm and the probability of the superior arm were established respectively. In the first-step TS, Thompson sampling was used to extract the maximum value from the posterior distribution of probability for the superior arm, determining it as the selected arm. In the two-step TS, Thompson sampling was used to sample from the distributions of the reward. During the updating, the posterior distribution of the probability for the superior arm was updated based on observations including actions and reward, and the distribution of the reward was updated based on the reward feedback. We performed the comparative experiments to solve the static multi-bandit tasks with 10, 20, 40, and 80 arms respectively by the state-of-the-art B-UCB algorithm, Thompson sampling and the proposed T-TS. The results shown that our proposed T-TS can learn the superior action more quickly with the best robustness. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Importance sampling

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Visual numerical cognition in pigeons: conformity to the Weber–Fechner law

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Animal Cognition 2025年第1期28.0卷 1-13页

作者： Wu, Peng Zhu, Juncai He, Qingzhi Wang, Zhizhong Shi, Li School of Electrical and Information Engineering Zhengzhou University Zhengzhou 450001 China Department of Automation Tsinghua University Beijing 100084 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou 450001 China

As representatives of a basal bird lineage, pigeons have exhibited remarkable visual numerical cognition, comparable even to that of monkeys. Nevertheless, whether visual numerical cognition in pigeons conforms to the Weber–Fechner law remains unknown. To address this, we designed a fully automated apparatus tailored for pigeons and used it to train them to perform a delayed match-to-numerosity task. The results showed that on a linear scale, pigeons represented smaller numerosities with higher precision and larger numerosities with lower precision, exhibiting a numerical magnitude effect. When the linear scale was compressed into a logarithmic scale, this magnitude effect was offset, resulting in similar representational characteristics across different numerosities. This finding suggests that the mental number line of pigeons is logarithmic rather than linear, consistent with the Weber–Fechner law. While biological brains seek precision in representing numerical information, they must also take computational load into account. This representational strategy may be the optimal outcome of the trade-off between computational precision and computational load that biological brains have achieved through long-term evolution. © The Author(s) 2025.

关键词： Birds Logarithm Mental number line Numerosity Pigeons

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Visual cognition of birds and its underlying neural mechanism:A review

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Avian Research 2022年第2期13卷 216-225页

作者： Xiaoke Niu Zhenyang Jiang Yanyan Peng Shuman Huang Zhizhong Wang Li Shi Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical EngineeringZhengzhou UniversityZhengzhou450001China Department of Automation Tsinghua UniversityBeijing100084China

Birds have acute vision and many remarkable visual cognition abilities,due to their unique living *** underlying neural mechanisms have also attracted interests of researchers in ***,we firstly summarize the visual cognition abilities of birds,and make a comparison with ***,the underlying neural mechanisms are presented,including histological structure of avian brain and visual pathways,typical experimental results and conclusions in electrochemistry and *** latter mainly focuses on several higher brain areas related to visual cognition,including mesopallium ventrolaterale,entopallium,visual Wulst,and nidopallium ***,we make a conclusion and provide a suggestion about future studies on revealing the neural mechanisms of avian visual *** review presents a detailed understanding of avian visual cognition and would be helpful in ornithology studies in the field of cognitive neuroscience.

关键词： Avian Electrophysiology Neural mechanism Visual cognition

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Multi-domain feature fusion for target color decoding from multichannel neural recordings of pigeons

Multi-domain feature fusion for target color decoding from m...

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2023 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2023

作者： Li, Hongxun Liu, Ruiqing Niu, Xiaoke Wang, Zhizhong Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical Engineering Zhengzhou University Zhengzhou450001 China Department of Mechanical and Electrical Engineering Henan Light Industry Vocational College Zhengzhou450000 China

ISBN: (纸本)9781510666351

The entopallium is a high-level neural structure of the avian visual system that is related to the processing of color- and shape-related information during target recognition. The mechanism underlying it remains unclear and has drawn a considerable amount of research interest. Decoding has invariably been used in relevant studies to verify whether the extracted response-related features contain visual information. However, local field potentials (LFPs) often contain rich features. In this study, We combine the features of different domains to decode color information, including those in the time and frequency domains as well as features related to non-linear and functional network connectivity. To extract valuable response-related features, three types of feature selection methods are compared. The LFP data from the entopallium of a pigeon were recorded during periods of stimulus presentation in which two colors were randomly presented in each trial. The selected feature set was finally used to decode the colors by using several general classifiers. The results showed that the fusion of multi-domain features helped improve decoding performance, and the optimal feature set was a fusion of multiple domains of features. These results improve our understanding of the avian neural mechanisms involved in encoding color-related information. © 2023 SPIE.

关键词： Color

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Attention Detection Using EEG Signals and Machine Learning: A Review

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Machine Intelligence Research 2025年第2期22卷 219-238页

作者： Qianru Sun Yueying Zhou Peiliang Gong Daoqiang Zhang Key Laboratory of Brain-Machine Intelligence Technology Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Attention detection using electroencephalogram(EEG)signals has become a popular ***,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG ***,this survey outlines recent advances in EEG-based attention detection within the past five years,with a primary focus on auditory attention detection(AAD)and attention level ***,we provide a brief overview of commonly used paradigms,preprocessing techniques,and artifact-handling methods,as well as listing accessible datasets used in these ***,we summarize the machine learning methods for classification in this field and divide them into two categories:traditional machine learning methods and deep learning *** also analyse the most frequently used methods and discuss the factors influencing each technique′s performance and ***,we discuss the existing challenges and future trends in this field.

关键词： Attention detection electroencephalogram(EEG) machine learning deep learning brain-computer interface.

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Two-step Thompson Sampling for Multi-Armed Bandit Problem

Two-step Thompson Sampling for Multi-Armed Bandit Problem

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第43届中国控制会议

作者： Zhigang Shang Lifang Yang Mengmeng Li School of Electrical and Information Engineering Zhengzhou University Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

An important feature of sequential decision-making under uncertain conditions is to strike a balance between exploration(obtaining information about the value of actions) and utilization(selecting the best action based on existing knowledge). The multi-armed bandit is a classic task for balancing their relationship. As the agent has learnt the relationship between two options in a 2-armed bandit task,it will no longer explore but instead use them directly. Inspired by the research, an algorithm based on two-step Thompson sampling(T-TS)is proposed to solve multi-armed bandit problem. First, posterior Beta distributions for the reward of each arm and the probability of the superior arm were established respectively. In the first-step TS, Thompson sampling was used to extract the maximum value from the posterior distribution of probability for the superior arm, determining it as the selected arm. In the two-step TS, Thompson sampling was used to sample from the distributions of the reward. During the updating, the posterior distribution of the probability for the superior arm was updated based on observations including actions and reward, and the distribution of the reward was updated based on the reward feedback. We performed the comparative experiments to solve the static multi-bandit tasks with 10, 20, 40, and 80 arms respectively by the state-of-the-art B-UCB algorithm, Thompson sampling and the proposed T-TS. The results shown that our proposed T-TS can learn the superior action more quickly with the best robustness.

关键词： Thompson sampling multi-armed bandit Beta distribution cumulative regret cumulative reward

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