BCI based hybrid interface for 3D object control in virtual reality

Chun, Jinsung; Bae, Byeonguk; Jo, Sungho

doi:10.1109/iww-bci.2016.7457461

Cited by 24 publications

(4 citation statements)

References 5 publications

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“…It is worth noting that BCI function relies on both user ability (imagination) and technology aspects (eyes' position). This lightened the workload regarding control tasks, and allowed users interact more easily [ 57 ].…”

Section: Integration Of Virtual Environments and Brain-computer Inmentioning

confidence: 99%

Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments

Alonso-Valerdi

Rodrigo

2017

Computational Intelligence and Neuroscience

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Tridimensional representations stimulate cognitive processes that are the core and foundation of human-computer interaction (HCI). Those cognitive processes take place while a user navigates and explores a virtual environment (VE) and are mainly related to spatial memory storage, attention, and perception. VEs have many distinctive features (e.g., involvement, immersion, and presence) that can significantly improve HCI in highly demanding and interactive systems such as brain-computer interfaces (BCI). BCI is as a nonmuscular communication channel that attempts to reestablish the interaction between an individual and his/her environment. Although BCI research started in the sixties, this technology is not efficient or reliable yet for everyone at any time. Over the past few years, researchers have argued that main BCI flaws could be associated with HCI issues. The evidence presented thus far shows that VEs can (1) set out working environmental conditions, (2) maximize the efficiency of BCI control panels, (3) implement navigation systems based not only on user intentions but also on user emotions, and (4) regulate user mental state to increase the differentiation between control and noncontrol modalities.

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Section: Integration Of Virtual Environments and Brain-computer Inmentioning

confidence: 99%

Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments

Alonso-Valerdi

Rodrigo

2017

Computational Intelligence and Neuroscience

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“…The accuracy of classifying BCI signals is obtained by the controller at the WES to predict the actions of the users based on the collected BCI signals. We select the VR delay and classification accuracy as our main metrics because they have been commonly used to design frameworks that eliminate potential VR sickness or fatigue of the users [8], [10], [32]. Moreover, we consider the classification setting on the BCI signals because if we can successfully predict the actions of the users, it is possible to extend the setting to a general scenario in the Metaverse where intelligent human-like DAs can accurately behave like humans with controlled permissions, e.g., imagined speech communications [15], adaptive VR environment rendering [31], and anomalous states and errorrelated behaviors detection [33].…”

Section: A System Operationmentioning

confidence: 99%

MetaChain: A Novel Blockchain-based Framework for Metaverse Applications

Nguyen

Hoang

Nguyen

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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Decoding brain signals can not only reveal Metaverse users' expectations but also early detect error-related behaviors such as stress, drowsiness, and motion sickness. For that, this article proposes a pioneering framework using wireless/overthe-air Brain-Computer Interface (BCI) to assist creation of virtual avatars as human representation in the Metaverse. Specifically, to eliminate the computational burden for Metaverse users' devices, we leverage Wireless Edge Servers (WES) that are popular in 5G architecture and therein URLLC, enhanced broadband features to obtain and process the brain activities, i.e., electroencephalography (EEG) signals (via uplink wireless channels). As a result, the WES can learn human behaviors, adapt system configurations, and allocate radio resources to create individualized settings and enhance user experiences. Despite the potential of BCI, the inherent noisy/fading wireless channels and the uncertainty in Metaverse users' demands and behaviors make the related resource allocation and learning/classification problems particularly challenging. We formulate the joint learning and resource allocation problem as a Quality-of-Experience (QoE) maximization problem that takes into the latency, brain classification accuracy, and resources of the system. To tackle this mixed integer programming problem, we then propose two novel algorithms that are (i) a hybrid learning algorithm to maximize the user QoE and (ii) a meta-learning algorithm to exploit the neurodiversity of the brain signals among multiple Metaverse users. The extensive experiment results with different BCI datasets show that our proposed algorithms can not only provide low delay for virtual reality (VR) applications but also can achieve high classification accuracy for the collected brain signals.

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“…They used the evoked potential component P300-based BCI for selecting different target surfaces of geometrical objects in the CAD systems. Some other important functions for CAD application such as creating models or manipulating models via BCI have been studied by other researchers [9,28,58,59,105]. Esfahani and Sundararajan [58] also carried out experiments to distinguish between different primitive shapes based on users' EEG activity, including cube, sphere, cylinder, pyramid and cone shapes.…”

Section: Bci-based Interaction For Cadmentioning

confidence: 99%

Multimodal Natural Human–Computer Interfaces for Computer-Aided Design: A Review Paper

et al. 2022

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Computer-aided design (CAD) systems have advanced to become a critical tool in product design. Nevertheless, they still primarily rely on the traditional mouse and keyboard interface. This limits the naturalness and intuitiveness of the 3D modeling process. Recently, a multimodal human–computer interface (HCI) has been proposed as the next-generation interaction paradigm. Widening the use of a multimodal HCI provides new opportunities for realizing natural interactions in 3D modeling. In this study, we conducted a literature review of a multimodal HCI for CAD to summarize the state-of-the-art research and establish a solid foundation for future research. We explore and categorize the requirements for natural HCIs and discuss paradigms for their implementation in CAD. Following this, factors to evaluate the system performance and user experience of a natural HCI are summarized and analyzed. We conclude by discussing challenges and key research directions for a natural HCI in product design to inspire future studies.

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BCI based hybrid interface for 3D object control in virtual reality

Cited by 24 publications

References 5 publications

Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments

Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments

MetaChain: A Novel Blockchain-based Framework for Metaverse Applications

Multimodal Natural Human–Computer Interfaces for Computer-Aided Design: A Review Paper

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