神经工程与脑-机接口

神经工程是近年来在生物医学工程领域备受关注的学科发展新方向。它运用神经科学和工程学的方法来分析神经功能并为神经功能缺失与紊乱的修复提供新的解决问题的方案;而脑-机接口则是当前神经工程领域中最活跃的研究方向之一。脑-机接口是在脑与计算机或其他外部设备之间建立的直接的通信和交流通道。在脑-机接口系统中,具有特定模式的脑信号携带着受试者希望表达的意愿,计算机将接收到的脑信号转换成相应的控制命令,于是那些有运动障碍的残疾人就可以利用脑-机接口系统来实现与外界的交流与对外部设备的控制。在基于脑电信号的脑-机接口系统中,受试者产生的脑信号大致可以分为内源性（endogenous）和外源性（exogenous）两类。其中外源性的成分主要取决于外部物理刺激（视觉、听觉或触觉）的参数而与认知行为无关;而内源性成分则主要由认知行为产生而与外部的物理刺激无关。在许多情况下,脑－机接口中的瞬态诱发电位通常都同时包含着内源性和外源性两种成分。寻找新的脑－机接口模式使之能显著提升记录脑电信号中的内源性与外源性成分在脑－机接口研究中具有重要意义。本文中将介绍一种基于运动起始时刻（motion—onset）的新的脑-机接口实验范式。本文的最后还探讨了脑－机接口未来发展的趋势与展望。     

2023年脑机接口领域发展态势

脑机接口是人脑与计算机或其他电子设备之间建立的直接交流与控制通道。由于其战略重要性,脑机接口已经成为各国战略布局的重点。本文简要介绍了脑机接口领域的发展历程,重点从战略布局、科研进展与产业应用等角度系统梳理了2023年该领域的最新进展,并展望未来发展趋势。2023年,神经编解码算法、神经探针与芯片等脑机接口领域核心技术取得重要进展,功能性超声脑机接口等新型脑机接口不断涌现,应用领域已经从医疗扩展到科研、娱乐等领域,重要企业取得多项里程碑进展。未来,脑机接口硬件将向小型化、高通量、柔性化发展,编解码效率和质量将大幅度提升,促进脑机接口功能从替换和恢复向改善、补充和增强转变,应用领域将进一步拓展。随着脑机接口的广泛应用,其伦理安全问题将受到重视。     

基于节律性脑电信号的脑-机接口

脑-机接口系统是一个不依靠外周神经和肌肉组织等而实现大脑和外界装置之间直接的交流和控制的通道。它为那些运动障碍的残疾人表达自己的意愿和实现对外部设备的控制提供了一种新的强大的技术支持。基于脑电的脑-机接口作为一种非侵入型的技术引起了该领域很多人的关注。基于脑电的脑-机接口采用了很多种类型的脑电信号。其中,振荡性的脑电图由于有较高的幅值和对噪声不敏感等特性而体现出极大的优势。也是由于这些原因,振荡性的脑电图变成了脑-机接口的应用中非常成功的设计之一。本文要介绍主要的基于脑电的脑-机接口中的两种,分别是稳态视觉诱发电位和基于运动本体感觉节律的脑-机接口。作者将详细的叙述该研究的生理背景、脑-机接口的参数,以及该系统的构造及信号处理的方法,并且会演示一些具有潜在应用价值的科研成果。     

昆虫机器混合系统研究进展

昆虫机器混合系统是以昆虫为载体,运动性能优异的新型昆虫机器人.本文首先提出昆虫机器混合系统的定义,分析其主要特点;进而结合蜜蜂机器人的研制,评述该领域的主要研究成果.在此基础上,概括了昆虫机器混合系统的研究框架,从神经生理机制、行为刺激方法、电极组织接口、刺激控制微系统和无线数据传输等几个方面归纳其主要研究挑战,分析其发展趋势,并展望了实现生物智能和机器智能融合的昆虫机器混合系统.     

2022年脑科学与类脑智能发展态势

脑科学是理解人和自然的“终极疆域”,现已成为重要的科学和技术前沿领域之一。该文主要从研究计划、研究进展、产品研发与产业发展的视角分析2022年脑科学与类脑智能领域的进展,并对未来发展趋势加以展望。2022年,美国、中国等国家/地区的脑计划取得较大进展;在脑图谱绘制、脑发育与脑认知功能解析、脑疾病机制探索及类脑智能技术与产品开发方面取得重要研究突破;脑疾病药物研发管线丰富,将推动相关产业快速发展。未来,借助各类新技术,研究人员将在多尺度解析脑认知功能,识别其中的功能障碍,开发出多种有效的脑疾病治疗药物;脑科学与人工智能等领域相互借鉴,推动类脑智能领域快速发展并广泛应用;数据治理和伦理安全的突破有助于脑科学与类脑智能研究的良性发展。     

聚3，4乙烯二氧噻吩修饰的新型碳纳米管纤维电极及其电化学性能评价

目的 植入式脑机接口在神经疾病的治疗方面已经得到了广泛应用，治疗的效果依赖于与神经组织接触的电极。与刚性材料制作的电极相比，碳基微纤维电极尺度小、生物兼容性好、组织炎症反应小，可以减少植入后的异物反应，改善神经记录信号的信噪比，可以长期保持稳定的电极特性。方法 本文设计了一种柔性碳纳米管（carbon nanotubes，CNTs）纤维电极的修饰方法，该方法采用电化学聚合的方式可以将聚3,4-乙烯二氧噻吩（poly（3,4-ethylenedioxythiophene），PEDOT）薄膜沉积到CNTs纤维电极上，作为微电极涂层。为了证明修饰涂层在电极表面具有良好的机械稳定性，对修饰电极进行了超声处理。此外，本文将PEDOT薄膜沉积到ITO玻璃上，评价了PEDOT薄膜的生物相容性。结果 恒电流方式在CNTs纤维电极表面沉积的PEDOT涂层降低了电极的电化学阻抗，提高了电极的电化学性能，且PEDOT沉积的时间越长阻抗减少的幅度越明显。对电极进行超声处理后，电极的电化学阻抗没有产生显著变化，说明超声处理后PEDOT涂层剥离较少，证明了修饰涂层在电极表面具有良好的机械稳定性。最后，细胞实验表明，PEDOT薄膜具有与ITO导电玻璃相当的细胞相容性。结论 PEDOT薄膜可以提高CNTs纤维电极的稳定性，有望提高脑机接口系统的寿命和可靠性，具有应用于长时间记录神经电信号的前景。     

脑机接口专利情报研究

脑机接口是在无外周神经系统和肌肉组织参与的条件下,通过计算机等电子设备输出控制信号,进而与外界环境进行交流的全新通讯和控制技术。它的发展依赖于神经科学、心理学、工程学、康复医学和计算机等学科专家间的密切合作,具有非常重要的学术价值、科学意义和广阔的应用前景,是当今世界研究的热点。本文利用新一代专利分析平台和工具Innography,结合专利情报分析理论,在对世界范围内脑机接口技术的专利进行申请趋势分析、区域分析、IPC分析、专利权人分析、诉讼专利和核心专利分析以及重点技术文本聚类分析的基础上,了解国内该技术发展态势,并尝试为脑机接口领域的发展提供有用的竞争情报参考。     

脑机接口：现状，问题与展望

本文综述现有脑机接口技术的最新发展,并讨论这些脑机接口技术的局限和存在的问题,如高估人类个体大脑的功能、对大脑信息存储方式缺乏了解等.基于大脑信息存储的"二维码"模型,我们认为目前的脑机接口技术方案仅适用于一些简单的应用场景,如了解受测者的情绪变化、生命活动的状态,以及控制体外器械等,而无法通过脑机接口技术获取脑内诸如记忆与思考等信息的精准细节.我们也提出,向大脑输入信息的脑机接口技术有较大的发展空间,比如发展具有多种调控效果、物理和生化技术结合的深脑刺激装置,有可能广泛应用于抑郁症、癫痫等脑疾病的治疗,以及应用于短期脑力的增强.本文对于目前的脑机接口研究领域具有一定的警示和启发意义.     

脑电事件相关去同步化和同步化的神经元群模型

利用基于丘脑-皮层网络的神经元群模型,研究被试者在某种认知状态下脑功能区的连接状态。模型包括三个模块,分别对应脑电头皮电极C3、Cz、C4记录的三个皮质区。模型外部输入包括用高斯白噪声表示的上行传入感受器信号、用直流偏移表示的皮质对丘脑的兴奋性输入、用指数衰减表示的来自脑千和前脑基底神经元的调制信号。模型输出的兴奋性神经元群的平均膜电位反映脑电记录的局部电位。改变模型输入,进行多次仿真试验并进行线性和非线性分析。研究结果显示：仿真输出信号的alpha频带功率谱有与实际脑机接口实验一致的事件相关去同步化和同步化现象;模型中功能相近的区域间有更强的耦合,随着耦合强度的增加,输出信号间的相关性和同步性均增加。     

一种基于支持向量机技术的癫痫脑电棘尖波识别方法

支持向量机是一种基于统计学习理论的新型学习机。文章提出一种基于支持向量机的癫痫脑电特征提取与识别方法,充分发挥其泛化能力强的特点,在与神经网络方法的比较中,表现出较低的漏检率和较好的鲁棒性,有深入研究的价值和良好的应用前景。     

Using Reinforcement Learning to Provide Stable Brain-Machine Interface Control Despite Neural Input Reorganization

Brain-machine interface (BMI) systems give users direct neural control of robotic, communication, or functional electrical stimulation systems. As BMI systems begin transitioning from laboratory settings into activities of daily living, an important goal is to develop neural decoding algorithms that can be calibrated with a minimal burden on the user, provide stable control for long periods of time, and can be responsive to fluctuations in the decoder’s neural input space (e.g. neurons appearing or being lost amongst electrode recordings). These are significant challenges for static neural decoding algorithms that assume stationary input/output relationships. Here we use an actor-critic reinforcement learning architecture to provide an adaptive BMI controller that can successfully adapt to dramatic neural reorganizations, can maintain its performance over long time periods, and which does not require the user to produce specific kinetic or kinematic activities to calibrate the BMI. Two marmoset monkeys used the Reinforcement Learning BMI (RLBMI) to successfully control a robotic arm during a two-target reaching task. The RLBMI was initialized using random initial conditions, and it quickly learned to control the robot from brain states using only a binary evaluative feedback regarding whether previously chosen robot actions were good or bad. The RLBMI was able to maintain control over the system throughout sessions spanning multiple weeks. Furthermore, the RLBMI was able to quickly adapt and maintain control of the robot despite dramatic perturbations to the neural inputs, including a series of tests in which the neuron input space was deliberately halved or doubled.     

Automatic Navigation for Rat-Robots with Modeling of the Human Guidance

A bio-robot system refers to an animal equipped with Brain-Computer Interface (BCI), through which the outer stimulation is delivered directly into the animal's brain to control its behaviors. The development of bio-robots suffers from the dependency on real-time guidance by human operators. Because of its inherent difficulties, there is no feasible method for automatic controlling of bio-robots yet. In this paper, we propose a new method to realize the automatic navigation for bio-robots. A General Regression Neural Network (GRNN) is adopted to analyze and model the controlling procedure of human operations. Comparing to the traditional approaches with explicit controlling rules, our algorithm learns the controlling process and imitates the decision-making of human-beings to steer the rat-robot automatically. In real-time navigation experiments, our method successfully controls bio-robots to follow given paths automatically and precisely. This work would be significant for future applications of bio-robots and provide a new way to realize hybrid intelligent systems with artificial intelligence and natural biological intelligence combined together.     

A review on directional information in neural signals for brain-machine interfaces

Brain-machine interfaces (BMIs) can be characterized by the technique used to measure brain activity and by the way different brain signals are translated into commands that control an effector. We give an overview of different approaches and focus on a particular BMI approach: the movement of an artificial effector (e.g. arm prosthesis to the right) by those motor cortical signals that control the equivalent movement of a corresponding body part (e.g. arm movement to the right). This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single-units. Here, we review recent findings showing that analog neuronal population signals, ranging from intracortical local field potentials over epicortical ECoG to non-invasive EEG and MEG, can also be used to decode movement direction and continuous movement trajectories. Therefore, these signals might provide additional or alternative control for this BMI approach, with possible advantages due to reduced invasiveness.     

Distribution and Morphology of Calcium-Binding Proteins Immunoreactive Neurons following Chronic Tungsten Multielectrode Implants

The development of therapeutic approaches to improve the life quality of people suffering from different types of body paralysis is a current major medical challenge. Brain-machine interface (BMI) can potentially help reestablishing lost sensory and motor functions, allowing patients to use their own brain activity to restore sensorimotor control of paralyzed body parts. Chronic implants of multielectrodes, employed to record neural activity directly from the brain parenchyma, constitute the fundamental component of a BMI. However, before this technique may be effectively available to human clinical trials, it is essential to characterize its long-term impact on the nervous tissue in animal models. In the present study we evaluated how chronic implanted tungsten microelectrode arrays impact the distribution and morphology of interneurons reactive to calcium-binding proteins calbindin (CB), calretinin (CR) and parvalbumin (PV) across the rat’s motor cortex. Our results revealed that chronic microelectrode arrays were well tolerated by the nervous tissue, with recordings remaining viable for up to 6 months after implantation. Furthermore, neither the morphology nor the distribution of inhibitory neurons were broadly impacted. Moreover, restricted microglial activation was observed on the implanted sites. On the whole, our results confirm and expand the notion that tungsten multielectrodes can be deemed as a feasible candidate to future human BMI studies.     

Childhood intelligence and adult obesity

Objective: Recent studies conclude childhood intelligence has no direct effect on adult obesity net of education, but evolutionary psychological theories suggest otherwise. Design and Methods: A population (n = 17,419) of British babies has been followed since birth in 1958 in a prospectively longitudinal study. Childhood general intelligence is measured at 7, 11, and 16, and adult BMI and obesity are measured at 51. Results: Childhood general intelligence has a direct effect on adult BMI, obesity, and weight gain, net of education, earnings, mother's BMI, father's BMI, childhood social class, and sex. More intelligent children grow up to eat more healthy foods and exercise more frequently as adults. Conclusion: Childhood intelligence has a direct effect on adult obesity unmediated by education or earnings. General intelligence decreases BMI only in adulthood when individuals have complete control over what they eat.     

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.     

Intelligence Test Score and Educational Level in Relation to BMI Changes and Obesity

Objective: To investigate whether intelligence and education are related to subsequent BMI changes and development and persistence of obesity in men from young adulthood through middle‐age. Research Methods and Procedures: Subjects were selected among men (median age, 19 years; examined between 1956 and 1977) appearing at Danish draft boards: a group with juvenile‐onset obesity, including all men with a BMI of ≥31.0 kg/m²; and a nonobese group randomly selected as a 1% sample of the study population. The obese group and 50% of the nonobese group were invited to participate in follow‐up studies between 1982 and 1984 and between 1992 and 1994. Among 907 men with juvenile‐onset obesity and 883 nonobese men, age, examination region, intelligence test score, education, and BMI from baseline to first follow‐up were analyzed by multiple linear and logistic regressions analyses. Results: Education and intelligence, analyzed separately, were inversely related to BMI changes in both groups and to the development of obesity in the nonobese group. When adjusted for education, the association between intelligence score and BMI changes and development of obesity vanished, whereas the inverse relationship for education persisted only for BMI changes. Intelligence score was not associated with the persistence of obesity in the obese group, whereas inverse relationships were found for education. Discussion: Intelligence test score was inversely related to risk of BMI changes and the risk of development of obesity, perhaps with education acting as a mediator or indicator of cognitive ability. Education, but not intelligence, was inversely associated with risk of remaining obese.     

从一般系统论到后基因组时代的系统生物学

论述了贝塔朗菲的一般系统论的思想起源、主要内容,基于一般系统论的系统生物学的产生、研究思路和方法,阐述了生物学由还原论的研究方法过渡到系统论的研究方法,以及系统生物学未来的发展进行了评价。     

Cognitive abilities, androgen levels, and body mass index in 5-year-old children

This study explores the potential relationship between a series of cognitive abilities and testosterone, dehydroepiandrosterone (DHEA), androstenedione, and body mass index (BMI) measurements in 5-year-old children. 60 boys and 69 girls were administered a test (K-BIT) which provided measurements of fluid intelligence (Matrices subtest), crystallized intelligence (Vocabulary subtest), and IQ composite (the combination of the two subtests); a sub-sample of 48 boys and 61 girls was also subjected to diverse tests related to theory of mind (affective labeling, appearance-reality distinction, display rules, and false belief). Testosterone, DHEA, and androstenedione levels were measured using an enzyme immunoassay technique in saliva samples. An analysis of variance failed to reveal any significant differences between boys and girls in any of the cognitive abilities assessed. The correlation analysis revealed a positive relationship between fluid intelligence and testosterone levels in boys, a negative relationship between crystallized intelligence and androstenedione levels in girls, and between affective labeling and androstenedione levels in boys. A multiple regression analysis indicated that androstenedione and BMI were the best predictors for some of the cognitive abilities assessed.     

The evolution of brain size and intelligence in man

The brain size of hominids has increased approximately threefold during the evolution of the hominids fromAustralopithecus toHomo sapiens. It is proposed that the principal reason for this increase is that larger brains conferred greater intelligence, and greater intelligence conferred a selection advantage. A number of anthropologists have difficulty accepting this thesis because they believe that brain size is not associated with intelligence in man. Evidence is reviewed, and new evidence from two studies is presented, to show that brain size as measured by head size is positively correlated with intelligence as measured by intelligence tests. On two recent samples statistically significant correlations of .21 and .30 were obtained between estimates of brain size and IQ. It is considered that brain size is positively associated with intelligence in man and that this is the major reason for the increase in brain size of the hominids during the last 3.2 million years.