Human‐Robot Interaction: Integrating BCI‐Controlled Virtual Robotic Arm With Manual Control in VR Environment

关键词 运动想象 运动观察 ERD/ERD Gabor 滤波器 神经生理 运动观察(movement/action

Objective. Motor imagery (MI)-based brain-computer interfaces (BCIs) provide an additional control pathway for people by decoding the intention of action imagination. The way people imagine greatly affects MI-BCI performance. Action itself is one of the factors that influence the way people imagine. Whether the different actions cause a difference in the MI performance is unknown. What is more important is how to manifest this action difference in the process of imagery, which has the potential to guide people to use their individualized actions to imagine more effectively. Approach. To explore action differences, this study proposes a novel paradigm named as action observation based delayed matching posture task. Ten subjects are required to observe, memorize, match, and imagine three types of actions (cutting, grasping and writing) given by visual images or videos, to accomplish the phases of encoding, retrieval and reinforcement of MI. Event-related potential (ERP), MI features, and classification accuracy of the left or the right hand are used to evaluate the effect of the action difference on the MI difference. Main results. Action differences cause different feature distributions, resulting in that the accuracy with high event-related (de)synchronization (ERD/ERS) is 27.75% higher than the ones with low ERD/ERS (p < 0.05), which indicates that the action difference has impact on the MI difference and the BCI performance. In addition, significant differences in the ERP amplitudes exists among the three actions: the amplitude of P300-N200 potential reaches 9.28 μV of grasping, 5.64 μV and 5.25 μV higher than the cutting and the writing, respectively (p < 0.05). Significance. The ERP amplitudes derived from the supplementary motor area shows positive correlation to the MI classification accuracy, implying that the ERP might be an index of the MI performance when the people is faced with action selection. This study demonstrates that the MI difference is related to the action difference, and can be manifested by the ERP, which is important for improving MI training by selecting suitable action; the relationship between the ERP and the MI provides a novel index to find the suitable action to set up an individualized BCI and improve the performance further.

EEG-based motor imagery classification using convolutional neural networks with local reparameterization trick

Resumo: O presente artigo descreve uma investigação que avaliou a utilização de ambientes virtuais de aprendizagem colaborativa no processo de construção de conhecimentos sobre ciências da natureza em um grupo de 16 estudantes do terceiro ano do ensino médio de uma escola pública localizada na região metropolitana de Porto Alegre. A pesquisa teve abordagem qualitativa do tipo estudo de caso e os relatos coletados foram analisados por Análise Textual Discursiva (ATD), com categorias emergentes. O ambiente virtual utilizado foi disponibilizado pela empresa Microsoft® na plataforma OneDrive®. Os participantes identificaram contribuições proporcionadas pelo uso do ambiente virtual e da tecnologia em sala de aula, assinalando seus benefícios para o processo educacional. Entre os argumentos encontrados nas respostas dos participantes, estão os seguintes: o aumento da mobilidade proporcionada pelos recursos tecnológicos; a capacidade de a virtualidade possibilitar o surgimento de múltiplos processos de experimentação; a redução da sensação de distanciamento dos educandos entre si, e destes em relação ao professor; a superação dos limites impostos pelo tempo e espaço da sala de aula transmissiva e; a potencialização do questionamento reconstrutivo, à medida que os recursos para a busca de informações ficam à disposição e podem ser prontamente acessados e questionados pelo grupo de colaboração. Como resultado, a investigação permitiu concluir que os participantes reconhecem que a aprendizagem colaborativa em ambientes virtuais possibilita a implementação de uma forma prática de educação pela pesquisa, pois se desenvolve ao longo de um processo que implica interação com fontes de consulta e com grupos de trabalho, abrindo a possibilidade do surgimento de novas compreensões e relações. Palavras-chave: Aprendizagem colaborativa. Ambientes virtuais.

Because of the redundant information contained in the EEG signals, the classification accuracy of motor imagery may be greatly reduced. The channel selection method helps to remove task-independent EEG signals, thereby improving the performance of the BCI system. However, the brain regions associated with motor imagery in different subjects are not exactly same, and the method of channel selection depends on the feedback of classification results. Aiming at the above problems, this paper proposed a new method of channel selection based on backward elimination and threshold. First, we calculated the variance of channels between categories based on the covariance matrix. The backward selection and threshold selection methods were then used to retain the highly differentiated channels. We also used different criterion to evaluate the discriminability between different classes. The selected EEG signals used common spatial pattern (CSP) and support vector machine (SVM) to calculate the classification accuracy. The efficacy of the proposed approach was examined using three data sets. The proposed approach has achieved 77.82% accuracy on the BCI Competition IV data set IIa, 86.02% accuracy on the BCI Competition III data set IIIa, and 86.86% accuracy on the binary class BCI Competition III data set IVa. The performance of the proposed algorithm is compared with other existing algorithms. The results of our experiments demonstrated that the proposed algorithm produces a higher classification accuracy compared to the other algorithms using lesser number of channels.

A cross-session motor imagery classification method based on Riemannian geometry and deep domain adaptation

Purpose This study aims to investigate the influence of the perceived value of AI on Cognitive load Reduction and Task performance efficiency through employee resilience. Design/methodology/approach This study used a cross-sectional research design; data were collected from the hotel industry employees and were analyzed using structural equation modeling Findings The findings indicated that the perceived value of AI showed a positive impact on cognitive load reduction and Task performance efficiency, also employee resilience partially mediated the relationship between the perceived value of AI with cognitive load reduction and Task performance efficiency. Practical implications This study presents a psychological viewpoint on AI adoption by stressing the mediating function of employee resilience, underscoring the significance of personal characteristics in reducing the effects of technological instruments. Originality/value This study addressed a gap in the literature; this focuses on how the perceived value of AI positively reduces the cognitive load among employees and enhances task performance efficiency. Additionally, it widens the discourse by examining the mediating effect of resilience.

The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain-computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.

Human spatial navigation: cognitive maps, sexual dimorphism, and neural substrates

Virtual reality (VR) collaborative manipulation requires a proper understanding of the user’s self, other collaborators, and shared objects in the virtual environment. Users must be aware of the movements of themselves and others to coordinate their actions when they hand over an object or move it together. The awareness of collaboration plays an essential role in virtual environments. However, previous studies have focused on providing awareness only for cooperative manipulation. This study proposes a novel framework for dual awareness that comprehensively manages the state of interactions between multiple users and shared virtual objects and provides distinct sensory cues to distinguish themselves from collaborators. The experimental results showed that participants could easily and quickly collaborate on manipulation using our feedback, and they also answered positively in the subjective evaluation.

The use of journal clubs and, more recently, case-based discussions in order to stimulate debate among orthopaedic surgeons lies at the heart of orthopaedic training and education. A virtual learning environment can be used as a platform to host virtual journal clubs and case-based discussions. This has many advantages in the current climate of constrained time and diminishing trainee and consultant participation in such activities. The virtual environment model opens up participation and improves access to journal clubs and case-based discussions, provides reusable educational content, establishes an electronic record of participation for individuals, makes use of multimedia material (including clinical imaging and photographs) for discussion, and finally, allows participants to link case-based discussions with relevant papers in the journal club. The Leicester experience highlights the many advantages and some of the potential difficulties in setting up such a virtual system and provides useful guidance for those considering such a system in their own training programme. As a result of the virtual learning environment, trainee participation has increased and there is a trend for increased consultant input in the virtual journal club and case-based discussions. It is likely that the use of virtual environments will expand to encompass newer technological approaches to personal learning and professional development.

We present in this paper researches focused on specification and production of virtual training environments. The use of Virtual Environments (VE) for training is strongly stimulated by important needs of training on sensitive equipment. But the development of such applications is generally done in new projects, where the reusability of existing developments is a major issue. We present here a platform of development, based on internal models that have been designed to achieve this reusability and standardization for the efficient development of new virtual environments. We defined a new generic model named STORM, used to describe reusable behaviors for 3D objects and reusable interactions between those objects or with humans. We also defined a scenario language named LORA, which allow non-computer scientists author various and complex sequences of tasks in a virtual scene. Based on those models, as an industrial validation with Nexter-Group, we have created 20 operational scenarios of maintenance virtual training on military equipments. We are also begining to create collaborative environments, where virtual and real humans have to collaborate to achieve their procedures.

Virtual environments have been widely used in motor neuroscience and rehabilitation, as they afford tight control of sensorimotor conditions and readily afford visual and haptic manipulations. However, typically, studies have only examined performance in the virtual testbeds, without asking how the simplified and controlled movement in the virtual environment compares to behavior in the real world. To test whether performance in the virtual environment was a valid representation of corresponding behavior in the real world, this study compared throwing in a virtual set-up with realistic throwing, where the task parameters were precisely matched. Even though the virtual task only required a horizontal single-joint arm movement, similar to many simplified movement assays in motor neuroscience, throwing accuracy and precision were significantly worse than in the real task that involved all degrees of freedom of the arm; only after 3 practice days did success rate and error reach similar levels. To gain more insight into the structure of the learning process, movement variability was decomposed into deterministic and stochastic contributions. Using the tolerance-noise-covariation decomposition method, distinct stages of learning were revealed: While tolerance was optimized first in both environments, it was higher in the virtual environment, suggesting that more familiarization and exploration was needed in the virtual task. Covariation and noise showed more contributions in the real task, indicating that subjects reached the stage of fine-tuning of variability only in the real task. These results showed that while the tasks were precisely matched, the simplified movements in the virtual environment required more time to become successful. These findings resonate with the reported problems in transfer of therapeutic benefits from virtual to real environments and alert that the use of virtual environments in research and rehabilitation needs more caution.NEW & NOTEWORTHY This study compared human performance of the same throwing task in a real and a matched virtual environment. With 3 days' practice, subjects improved significantly faster in the real task, even though the arm and hand movements were more complex. Decomposing variability revealed that performance in the virtual environment, despite its simplified hand movements, required more exploration. Additionally, due to fewer constraints in the real task, subjects could modify the geometry of the solution manifold, by shifting the release position, and thereby simplify the task.

This research develops and validates a dual mediation framework examining the pathways through which interaction quality in artificial intelligence educational systems is positively associated with deep learning outcomes via cognitive load reduction and motivational enhancement. Utilizing covariance-based structural equation modeling (CB-SEM), we analyzed survey data from 570 university teachers engaged with AI-powered learning platforms. Findings demonstrate that high-quality human-AI interaction significantly reduces cognitive burden, which in turn is positively related to learning motivation and shows pathways to deep learning approaches. Bootstrap procedures confirmed robust sequential mediation effects, with this pathway accounting for 53% of the total variance. The model achieved excellent fit indices and explained 31.5% of variance in deep learning outcomes. By synthesizing Cognitive Load Theory with Self-Determination Theory, this study contributes to educational technology scholarship by elucidating the psychological mechanisms linking interface design to learning depth. The empirical evidence provides actionable insights for developing AI educational systems that strategically minimize cognitive demands, foster motivational engagement, and support meaningful learning experiences.

In this paper, we introduce approaches to navigating and manipulating objects in a Collaborative Virtual Environment (CVE) that engage tangible objects and an interactive table interface. We also identify three design concerns that are common to physical-virtual connectivity for interaction with CVE systems. At last, we propose solutions to these issues within the context of CVEs.