Optimizing motor imagery neurofeedback through the use of multimodal immersive virtual reality and motor priming

Vourvopoulos, Athanasios; Cardona, John Edison Muñoz; Badia, Sergi Bermúdez i

doi:10.1109/icvr.2015.7358592

Cited by 17 publications

(7 citation statements)

References 38 publications

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“…Thus, BCI experiments using immersive VR can be interfaced with a unique integrated software tool which allows the recording of multiple session parameters such as physiological signals, visual stimulus, game events, etc. [20].…”

Section: Related Workmentioning

confidence: 99%

PhysioVR: A novel mobile virtual reality framework for physiological computing

Muñoz

Paulino

Vasanth

et al. 2016

2016 IEEE 18th International Conference on E-Health Networking, Applications and Services (Healthcom)

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Virtual Reality (VR) is morphing into a ubiquitous technology by leveraging of smartphones and screenless cases in order to provide highly immersive experiences at a low price point. The result of this shift in paradigm is now known as mobile VR (mVR). Although mVR offers numerous advantages over conventional immersive VR methods, one of the biggest limitations is related with the interaction pathways available for the mVR experiences. Using physiological computing principles, we created the PhysioVR framework, an Open-Source software tool developed to facilitate the integration of physiological signals measured through wearable devices in mVR applications. PhysioVR includes heart rate (HR) signals from Android wearables, electroencephalography (EEG) signals from a lowcost brain computer interface and electromyography (EMG) signals from a wireless armband. The physiological sensors are connected with a smartphone via Bluetooth and the PhysioVR facilitates the streaming of the data using UDP communication protocol, thus allowing a multicast transmission for a third party application such as the Unity3D game engine. Furthermore, the framework provides a bidirectional communication with the VR content allowing an external event triggering using a real-time control as well as data recording options. We developed a demo game project called EmoCat Rescue which encourage players to modulate HR levels in order to successfully complete the in-game mission. EmoCat Rescue is included in the PhysioVR project which can be freely downloaded. This framework simplifies the acquisition, streaming and recording of multiple physiological signals and parameters from wearable consumer devices providing a single and efficient interface to create novel physiologically-responsive mVR applications.

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Section: Related Workmentioning

confidence: 99%

PhysioVR: A novel mobile virtual reality framework for physiological computing

Muñoz

Paulino

Vasanth

et al. 2016

2016 IEEE 18th International Conference on E-Health Networking, Applications and Services (Healthcom)

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“…Recent advances in technology make it far easier to create a rehabilitation environment that provides rich vivid cues, gives salient feedback and is motivating. For example, the rise of immersive technologies, including virtual reality (VR) and augmented reality (AR) platforms [45][46][47], allows for the creation of engaging visual experiences that have the potential to improve a patient's self-efficacy [48] and thereby encourage the patient to maintain the rehabilitation regime. One specific example of this is visually amplifying the movement made by a patient when the movement is of limited extent so that the patient can see their efforts are producing results [49].…”

Section: Introductionmentioning

confidence: 99%

Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review

Baniqued

Stanyer

Awais

et al. 2021

J NeuroEngineering Rehabil

138

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Background Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the first systematic examination of the literature on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profile these systems from a technical and clinical perspective. Methods A search for January 2010–October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation at different stages of development involving tests on healthy participants or people who have had a stroke. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures. Results 30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for three BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery. Conclusion 19 out of 30 studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. We identified large heterogeneity in reporting and emphasise the need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.

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“…VR-guided action offers the advantages of intuitive and specific actions and a strong sense of substitution (Velasquez-Martinez et al, 2020). Well-designed scene feedback can produce neural activation (Vourvopoulos et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation

Xie

Wang

et al. 2022

Front. Aging Neurosci.

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It is difficult for stroke patients with flaccid paralysis to receive passive rehabilitation training. Therefore, virtual rehabilitation technology that integrates the motor imagery brain-computer interface and virtual reality technology has been applied to the field of stroke rehabilitation and has evolved into a physical rehabilitation training method. This virtual rehabilitation technology can enhance the initiative and adaptability of patient rehabilitation. To maximize the deep activation of the subjects motor nerves and accelerate the remodeling mechanism of motor nerve function, this study designed a brain-computer interface rehabilitation training strategy using different virtual scenes, including static scenes, dynamic scenes, and VR scenes. Including static scenes, dynamic scenes, and VR scenes. We compared and analyzed the degree of neural activation and the recognition rate of motor imagery in stroke patients after motor imagery training using stimulation of different virtual scenes, The results show that under the three scenarios, The order of degree of neural activation and the recognition rate of motor imagery from high to low is: VR scenes, dynamic scenes, static scenes. This paper provided the research basis for a virtual rehabilitation strategy that could integrate the motor imagery brain-computer interface and virtual reality technology.

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Optimizing motor imagery neurofeedback through the use of multimodal immersive virtual reality and motor priming

Cited by 17 publications

References 38 publications

PhysioVR: A novel mobile virtual reality framework for physiological computing

PhysioVR: A novel mobile virtual reality framework for physiological computing

Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review

Research on Rehabilitation Training Strategies Using Multimodal Virtual Scene Stimulation

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