BrainChat - A Collaborative Augmented Reality Brain Interface for Message Communication

Kerous, Bojan; Liarokapis, Fotis

doi:10.1109/ismar-adjunct.2017.91

Cited by 12 publications

(9 citation statements)

References 18 publications

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“…While VR immerses the user in a simulated environment, the Augmented Reality (AR) systems superimpose virtual elements in the real world thereby augmenting the view of the user in real-time. Virtual objects can vary from anthropomorphic objects, such as human avatars or limbs [58] – [60] , to non-anthropomorphic objects and graphical user interfaces [61] – [65] . For a review of application of VR system in neuroscience research and therapeutics, readers are directed to Bohil et al [66] .…”

Section: Overview Of State-of-the-art End Effectorsmentioning

confidence: 99%

A Roadmap Towards Standards for Neurally Controlled End Effectors

Paek

Brantley

Ravindran

et al. 2021

IEEE Open J. Eng. Med. Biol.

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The control and manipulation of various types of end effectors such as powered exoskeletons, prostheses, and 'neural' cursors by brain-machine interface (BMI) systems has been the target of many research projects. A seamless "plug and play" interface between any BMI and end effector is desired, wherein similar user's intent cause similar end effectors to behave identically. This report is based on the outcomes of an IEEE Standards Association Industry Connections working group on End Effectors for Brain-Machine Interfacing that convened to identify and address gaps in the existing standards for BMI-based solutions with a focus on the end-effector component. A roadmap towards standardization of end effectors for BMI systems is discussed by identifying current device standards that are applicable for end effectors. While current standards address basic electrical and mechanical safety, and to some extent, performance requirements, several gaps exist pertaining to unified terminologies, data communication protocols, patient safety and risk mitigation.

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Section: Overview Of State-of-the-art End Effectorsmentioning

confidence: 99%

A Roadmap Towards Standards for Neurally Controlled End Effectors

Paek

Brantley

Ravindran

et al. 2021

IEEE Open J. Eng. Med. Biol.

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“…One of the most important research trends to emerge from the availability of affordable VR/AR and EEG hardware has been the rise of the Brain Computer Interface (BCI), and its use in VEs [95] to facilitate interactions between a human and a computer. There are numerous examples of an EEG headset being used in VR and AR as a Brain-Computer Interface (BCI) [89,91,[96][97][98]. BCIs have the ability to translate a user's thought into action in the virtual world [96,99].…”

Section: The Case For Using Hyperscanning In Virtual Environmentsmentioning

confidence: 99%

“…Another interesting application of the BCI has been in communication between users in a VE. Kerous et al [98] have used the P300 speller as a means to implement communication between two people in a VE. The P300 speller relies on the evoked potential recorded approximately 300 ms after the onset of a stimulus of interest [102].…”

Section: The Case For Using Hyperscanning In Virtual Environmentsmentioning

confidence: 99%

A Review of Hyperscanning and Its Use in Virtual Environments

et al. 2020

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Hyperscanning is a technique which simultaneously records the neural activity of two or more people. This is done using one of several neuroimaging methods, such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS). The use of hyperscanning has seen a dramatic rise in recent years to monitor social interactions between two or more people. Similarly, there has been an increase in the use of virtual reality (VR) for collaboration, and an increase in the frequency of social interactions being carried out in virtual environments (VE). In light of this, it is important to understand how interactions function within VEs, and how they can be enhanced to improve their quality in a VE. In this paper, we present some of the work that has been undertaken in the field of social neuroscience, with a special emphasis on hyperscanning. We also cover the literature detailing the work that has been carried out in the human–computer interaction domain that addresses remote collaboration. Finally, we present a way forward where these two research domains can be combined to explore how monitoring the neural activity of a group of participants in VE could enhance collaboration among them.

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“…Augmented reality (AR) can be used to enhance brain–computer interaction, and in the future, the integration of AR wearable devices and BCI will be possible. AR-based steady-state visual evoked potential (SSVEP) BCI is receiving increased attention from researchers ( Lenhardt and Ritter, 2010 ; Kerous and Liarokapis, 2017 ; Si-Mohammed et al, 2018 ; Zhao et al, 2021 ). However, compared with a computer screen (CS) ( Lenhardt and Ritter, 2010 ) or a virtual reality (VR) device ( Rohani and Puthusserypady, 2015 ), the quality of AR stimuli superimposed on real scenes is susceptible to environmental influences, making target recognition difficult.…”

Section: Introductionmentioning

confidence: 99%

A separable convolutional neural network-based fast recognition method for AR-P300

Du²,

Zhao³

2022

Front. Hum. Neurosci.

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Augmented reality-based brain–computer interface (AR–BCI) has a low signal-to-noise ratio (SNR) and high real-time requirements. Classical machine learning algorithms that improve the recognition accuracy through multiple averaging significantly affect the information transfer rate (ITR) of the AR–SSVEP system. In this study, a fast recognition method based on a separable convolutional neural network (SepCNN) was developed for an AR-based P300 component (AR–P300). SepCNN achieved single extraction of AR–P300 features and improved the recognition speed. A nine-target AR–P300 single-stimulus paradigm was designed to be administered with AR holographic glasses to verify the effectiveness of SepCNN. Compared with four classical algorithms, SepCNN significantly improved the average target recognition accuracy (81.1%) and information transmission rate (57.90 bits/min) of AR–P300 single extraction. SepCNN with single extraction also attained better results than classical algorithms with multiple averaging.

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BrainChat - A Collaborative Augmented Reality Brain Interface for Message Communication

Cited by 12 publications

References 18 publications

A Roadmap Towards Standards for Neurally Controlled End Effectors

A Roadmap Towards Standards for Neurally Controlled End Effectors

A Review of Hyperscanning and Its Use in Virtual Environments

A separable convolutional neural network-based fast recognition method for AR-P300

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