Neuroergonomics: Opportunities and challenges of merging cognitive neuroscience with cognitive ergonomics

As with any merger of previously unrelated scientific disciplines, the usefulness of combining cognitive neuroscience and cognitive ergonomics to create the new field of 'neuroergonomics' depends on the significance of the research subjects that could not be addressed if the two fields remained separated. Situations characterized by a scarcity of overt operator behaviour and performance represent one driving force for combining the two fields. In those situations, neurobiological indices of cognitive processes and capacities may emerge as the main, or even exclusive, source of information about the operator's cognitive status and activities. This information is needed, for example, to support adaptive aiding and dynamic function allocation in the context of human-automation cooperation. The utilization of research approaches and measurements more typical of cognitive neuroscience will benefit the field of cognitive ergonomics to the extent that multidimensional psychological constructs (such as situation awareness and mental workload) are reduced to their underlying cognitive operations which are more isomorphic with activity changes in brain regions, circuits and distributed neuronal systems. The transformation of these constructs and paradigms will, in turn, enable cognitive ergonomics to contribute to progress in the field of cognitive neuroscience.     

Relative brain displacement and deformation during constrained mild frontal head impact

This study describes the measurement of fields of relative displacement between the brain and the skull in vivo by tagged magnetic resonance imaging and digital image analysis. Motion of the brain relative to the skull occurs during normal activity, but if the head undergoes high accelerations, the resulting large and rapid deformation of neuronal and axonal tissue can lead to long-term disability or death. Mathematical modelling and computer simulation of acceleration-induced traumatic brain injury promise to illuminate the mechanisms of axonal and neuronal pathology, but numerical studies require knowledge of boundary conditions at the brain–skull interface, material properties and experimental data for validation. The current study provides a dense set of displacement measurements in the human brain during mild frontal skull impact constrained to the sagittal plane. Although head motion is dominated by translation, these data show that the brain rotates relative to the skull. For these mild events, characterized by linear decelerations near 1.5g (g = 9.81 m s⁻²) and angular accelerations of 120–140 rad s⁻², relative brain–skull displacements of 2–3 mm are typical; regions of smaller displacements reflect the tethering effects of brain–skull connections. Strain fields exhibit significant areas with maximal principal strains of 5 per cent or greater. These displacement and strain fields illuminate the skull–brain boundary conditions, and can be used to validate simulations of brain biomechanics.     

How to make an autonomous robot as a partner with humans: design approach versus emergent approach

In this paper, we discuss what factors are important to realize an autonomous robot as a partner with humans. We believe that it is important to interact with people without boring them, using verbal and non-verbal communication channels. We have already developed autonomous robots such as AIBO and QRIO, whose behaviours are manually programmed and designed. We realized, however, that this design approach has limitations; therefore we propose a new approach, intelligence dynamics, where interacting in a real-world environment using embodiment is considered very important. There are pioneering works related to this approach from brain science, cognitive science, robotics and artificial intelligence. We assert that it is important to study the emergence of entire sets of autonomous behaviours and present our approach towards this goal.     

认知神经科学与人机交互的融合:人机交互研究的新趋势

目的厘清认知神经科学与人机交互研究融合的发展历程、存在的问题与其未来发展趋势。方法采用文献元分析法,分别对认知神经科学、人机交互、脑机接口等不同领域的相关文献进行了分析,首先简要介绍了认知神经科学的发展历程,叙述了脑功能成像技术的演化进程;其次论述了认知神经科学领域的研究结果对人机交互研究的支持作用;然后叙述了基于认知神经科学和脑成像技术的人机交互研究的认知模型发展历程,并简要介绍了一些从认知神经科学和脑成像角度开展的人机交互研究;接着重点介绍了认知神经科学与人机交互研究结合的典型领域,即脑机接口领域,介绍了脑机接口的发展历史、脑机接口的认知模型、脑机结构研究存在的问题;最后总结了认知神经科学与人机交互融合的必要性和可能性,在对目前现状进行总结的基础上预测了未来的发展趋势。结论认知神经科学与人机交互研究的融合,为人机交互研究提供了新的思路和方法,未来的基于认知神经科学的人机交互研究,可以更好地揭示人机交互过程中的用户特征和交互规律。     

Role of interdisciplinarity in computer sciences: quantification,impact and life trajectory

The tremendous advances in computer science in the last few decades have provided the platform to address and solve complex problems using interdisciplinary research. In this paper, we investigate how the extent of interdisciplinarity in computer science domain (which is further divided into 24 research fields) has changed over the last 50 years. To this end, we collect a massive bibliographic dataset with rich metadata information. We start with quantifying interdisciplinarity of a field in terms of the diversity of topics and citations. We then analyze the effect of interdisciplinary research on the scientific impact of individual fields and observe that highly disciplinary and highly interdisciplinary papers in general have a low scientific impact; remarkably those that are able to strike a balance between the two extremes eventually land up having the highest impact. Further, we study the reciprocity among fields through citation interactions and notice that links from one field to related and citation-intensive fields (fields producing large number of citations) are reciprocated heavily. A systematic analysis of the citation interactions reveals the life trajectory of a research field, which generally undergoes three phases—a growing phase, a matured phase and an interdisciplinary phase. The combination of metrics and empirical observations presented here provides general benchmarks for future studies of interdisciplinary research activities in other domains of science.     

Is science becoming more interdisciplinary? Measuring and mapping six research fields over time

In the last two decades there have been studies claiming that science is becoming ever more interdisciplinary. However, the evidence has been anecdotal or partial. Here we investigate how the degree of interdisciplinarity has changed between 1975 and 2005 over six research domains. To do so, we compute well-established bibliometric indicators alongside a new index of interdisciplinarity (Integration score, aka Rao-Stirling diversity) and a science mapping visualization method. The results attest to notable changes in research practices over this 30 year period, namely major increases in number of cited disciplines and references per article (both show about 50% growth), and co-authors per article (about 75% growth). However, the new index of interdisciplinarity only shows a modest increase (mostly around 5% growth). Science maps hint that this is because the distribution of citations of an article remains mainly within neighboring disciplinary areas. These findings suggest that science is indeed becoming more interdisciplinary, but in small steps — drawing mainly from neighboring fields and only modestly increasing the connections to distant cognitive areas. The combination of metrics and overlay science maps provides general benchmarks for future studies of interdisciplinary research characteristics.     

The sleeping brain as a complex system

'Complexity science' is a rapidly developing research direction with applications in a multitude of fields that study complex systems consisting of a number of nonlinear elements with interesting dynamics and mutual interactions. This Theme Issue 'The complexity of sleep' aims at fostering the application of complexity science to sleep research, because the brain in its different sleep stages adopts different global states that express distinct activity patterns in large and complex networks of neural circuits. This introduction discusses the contributions collected in the present Theme Issue. We highlight the potential and challenges of a complex systems approach to develop an understanding of the brain in general and the sleeping brain in particular. Basically, we focus on two topics: the complex networks approach to understand the changes in the functional connectivity of the brain during sleep, and the complex dynamics of sleep, including sleep regulation. We hope that this Theme Issue will stimulate and intensify the interdisciplinary communication to advance our understanding of the complex dynamics of the brain that underlies sleep and consciousness.     

Resonance of human brain under head acceleration

Although safety standards have reduced fatal head trauma due to single severe head impacts, mild trauma from repeated head exposures may carry risks of long-term chronic changes in the brain''s function and structure. To study the physical sensitivities of the brain to mild head impacts, we developed the first dynamic model of the skull–brain based on in vivo MRI data. We showed that the motion of the brain can be described by a rigid-body with constrained kinematics. We further demonstrated that skull–brain dynamics can be approximated by an under-damped system with a low-frequency resonance at around 15 Hz. Furthermore, from our previous field measurements, we found that head motions in a variety of activities, including contact sports, show a primary frequency of less than 20 Hz. This implies that typical head exposures may drive the brain dangerously close to its mechanical resonance and lead to amplified brain–skull relative motions. Our results suggest a possible cause for mild brain trauma, which could occur due to repetitive low-acceleration head oscillations in a variety of recreational and occupational activities.     

脑外科机器人结构分析与参数优化设计

为实现脑外科机器人在脑外科手术中辅助医生进行精确导航定位,进行了机器人结构灵活性及手术空间要求的研究.首先,研制了一种五自由度脑外科机器人结构,利用D-H方法计算了机器人运动学的正反解方程.其次,运用MATLAB软件对机器人的工作空间、灵活性及手术空间进行了可视化仿真,仿真的结果表明该机器人结构可以用来辅助医生选择最佳手术路径,并以上述分析为依据提出了一种机器人结构参数优化设计的方法.最后通过手术模拟试验验证了所设计的机器人灵活性满足脑外科手术要求.     

The dynamics of interdisciplinary research fields: the case of river research

Interdisciplinarity results from dynamics at two levels. Firstly, research questions are approached using inputs from a variety of disciplinary fields. Secondly, the results of this multidisciplinary research feed back into the various research fields. This may either contribute to the further development of these fields, or may lead to disciplinary reconfiguration. If the latter is the case, a new interdisciplinary field may emerge. Following this perspective, the scientific landscape of river research and river science is mapped to assess to which current river research is a multi-disciplinary endeavor, and to which extent it results in a new emerging (inter)disciplinary field of river science. The paper suggests that this two level approach is a useful method to study interdisciplinary research and, more generally, disciplinary dynamics. With respect to river research, we show that it is mainly performed in several fields (limnology, fisheries & fish research, hydrology & water resources, and geomorphology) that hardly exchange knowledge. The different river research topics are multidisciplinary in nature, as they are shared by different fields. However, river science does not emerge as an interdisciplinary field, and often-mentioned new interdisciplinary fields such as hydroecology or hydromorphology are not (yet) visible. There is hardly any involvement of social within river research. Finally, the field of ecology occupies a central position within river research, whereas an expected engineering field is shown absent. This together may signal the acceptance of the ecosystem-based paradigm in river management, replacing the traditional engineering paradigm.     

Artificial intelligence adoption in the physical sciences,natural sciences,life sciences,social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021

Analysing historical patterns of artificial intelligence (AI) adoption can inform decisions about AI capability uplift, but research to date has provided a limited view of AI adoption across different fields of research. In this study we examine worldwide adoption of AI technology within 333 fields of research during 1960–2021. We do this by using bibliometric analysis with 137 million peer-reviewed publications captured in The Lens database. We define AI using a list of 214 phrases developed by expert working groups at the Organisation for Economic Cooperation and Development (OECD). We found that 3.1 million of the 137 million peer-reviewed research publications during the entire period were AI-related, with a surge in AI adoption across practically all research fields (physical science, natural science, life science, social science and the arts and humanities) in recent years. The diffusion of AI beyond computer science was early, rapid and widespread. In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to cover over half of all research fields by 1972, over 80% by 1986 and over 98% in current times. We note AI has experienced boom-bust cycles historically; the AI “springs” and “winters”. We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.     

EEG-BCI实验范式交互界面设计综述研究

随着人工智能在神经科学和人机交互等领域的深度交叉融合,脑-机接口(BCI)再次引起了国内外智能交互领域学者的关注.目的 为了提高脑-机接口系统效率,运用交互设计思维呈现符合被试心理预期的界面形式,减少被试视觉疲劳,更加高效、准确的获取实验数据.方法 对目前基于事件相关电位(ERP)、视觉稳态诱发电位(SSVEP)和运动想象(MI)等EEG-BCI实验范式的交互界面设计进行了详细地梳理和归纳.结论 通过综述为脑-机接口实验范式设计研究提供参考,改善目前EEG-BCI在交互控制领域的应用局限性,提高BCI系统可用性、易用性.更有助于促进工效学、设计学科、认知神经科学、信息科学等多学科的交叉融合,为基于脑-机交互的人工智能发展提供新方向和新思路.     

并联机器人技术分析及展望

近年来并联机器人的发展已成为机器人研究领域的热点之一,在某些方面它具有串联结构所无法相比的优点,因而扩大了机器人领域的应用范围。本文对并联机器人机构学、运动学、系统控制策略等关键技术做了概括性分析,同时还介绍了两种新型虎克铰链模型,为高精度并联机构的设计提供了有利条件。另外文中对并联机构研究的热点问题做了详细分析并提供了新的解决方向,如高精度点位控制策略、误差分析与补偿等。最后对目前并联机构的应用及发展趋势进行了阐述,并指出了我国现阶段并联机构产业化中存在的一些问题与挑战。     

Group characterization of impact-induced,in vivo human brain kinematics

Brain movement during an impact can elicit a traumatic brain injury, but tissue kinematics vary from person to person and knowledge regarding this variability is limited. This study examines spatio-temporal brain–skull displacement and brain tissue deformation across groups of subjects during a mild impact in vivo. The heads of two groups of participants were imaged while subjected to a mild (less than 350 rad s⁻²) impact during neck extension (NE, n = 10) and neck rotation (NR, n = 9). A kinematic atlas of displacement and strain fields averaged across all participants was constructed and compared against individual participant data. The atlas-derived mean displacement magnitude was 0.26 ± 0.13 mm for NE and 0.40 ± 0.26 mm for NR, which is comparable to the displacement magnitudes from individual participants. The strain tensor from the atlas displacement field exhibited maximum shear strain (MSS) of 0.011 ± 0.006 for NE and 0.017 ± 0.009 for NR and was lower than the individual MSS averaged across participants. The atlas illustrates common patterns, containing some blurring but visible relationships between anatomy and kinematics. Conversely, the direction of the impact, brain size, and fluid motion appear to underlie kinematic variability. These findings demonstrate the biomechanical roles of key anatomical features and illustrate common features of brain response for model evaluation.     

Optical positioning technology of an assisted puncture robot based on binocular vision

Percutaneous image-guided interventions are increasing in number in clinical practice because they are minimally invasive. Needle positioning placement is crucial and highly dependent on the physician's skills and experience, it is often the longest part of the intervention. Medical robotics and computer-assisted surgery are hotspots in the field of robotics and medicine, changing the essence of traditional surgery using a combination of robotic, image processing, and computer technologies. The present paper aimed to study the auxiliary puncture procedure using a robot based on optical positioning technology that can be used to mark points in puncturing operation. Binocular camera is used for image acquisition, and Zhang's calibration method is used to establish the binocular camera model. In addition, the circular markers are identified by the least square method detection circle, and the coordinate information of the markers in three-dimensional space is solved by using the visual depth information of binocular phases. This paper studies the verification of the three-dimensional bone model of the human body, which lays a foundation for the application of the assistant puncture robot.     

自由飞行空间机器人电联试实验平台的研制

为检验自由飞行空间机器人(FFSR)系统各个部件电接口传递信息的正确性及系统在轨执行任务的能力,采用数值模拟方法,通过软硬件结合的方式建立了FFSR电联试实验平台.该平台主要由主控计算机、主机系统、虚拟关节、虚拟视觉等4个功能模块组成.在此平台基础上建立了基于视觉反馈的机械臂运动控制算法和电机三环控制模型.该平台可用于对实际FFSR系统的数值仿真.对电联试实验平台的测试结果表明,该平台能够很好地完成对实际机器人系统通讯能力的模拟测试及系统控制算法的仿真验证.     

Can we talk? How the cognitive neuroscience of attention emerged from neurobiology and psychology, 1980–2005

This study uses author co-citation analysis to trace prospectively the development of the cognitive neuroscience of attention between 1980 and 2005 from its precursor disciplines: cognitive psychology, single cell neurophysiology, neuropsychology, and evoked potential research. The author set consists of 28 authors highly active in attentional research in the mid-1980s. PFNETS are used to present the co-citation networks. Authors are clustered via the single-link clustering intrinsic to the PFNET algorithm. By 1990 a distinct cognitive neuroscience specialty cluster emerges, dominated by authors engaged in brain imaging research.     

Improved fuzzy entropy clustering algorithm for MRI brain image segmentation

Magnetic resonance imaging (MRI) brain image segmentation is essential at preliminary stage in the neuroscience research and computer‐aided diagnosis. However, presence of noise and intensity inhomogeneity in MRI brain images leads to improper segmentation. The fuzzy entropy clustering (FEC) is often used to deal with noisy data. One major disadvantage of the FEC algorithm is that it does not consider the local spatial information. In this article, we have proposed an improved fuzzy entropy clustering (IFEC) algorithm by introducing a new fuzzy factor, which incorporates both local spatial and gray‐level information. The IFEC algorithm is insensitive to noise, preserves the image detail during clustering, and is free of parameter selection. The efficacy of IFEC algorithm is demonstrated by comparing it quantitatively with the state‐of‐the‐art segmentation approaches in terms of similarity index on publically available real and simulated MRI brain images.     

Universities’ structural commitment to interdisciplinary research

Scientometrics - In recent years, science policy experts have been promoting interdisciplinary research (IDR) in order to foster innovation and address grand scientific challenges. But to date we...