Virtual Reality Assisted Motor Imagery for Early Post-Stroke Recovery: A Review

Choy, Chi Sang; Cloherty, Shaun L.; Pirogova, Elena; Fang, Qiang

doi:10.1109/rbme.2022.3165062

Cited by 12 publications

(17 citation statements)

References 100 publications

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“…To increase the energy of EEG signals, it is recommended to use Virtual Reality (VR) applications that allow the patient to participate more in rehabilitation exercises (Saposnik et al 2016;Aida et al 2018;Vourvopoulos et al 2019a;Elor and Kurniawan 2020). The literature reviews by Camargo-Vargas et al (2021) and Choy et al (2023) show that the use of non-immersive VR technology experienced via 2D computer screens and fully-immersive VR technology experienced via headsets are the most common practices in post-stroke hand rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

Designing an immersive virtual reality environment for hand rehabilitation purposes: A preliminary study

Tastan,

Başpınar,

Hamurcu

et al. 2024

Preprint

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This study investigates the influence of immersive virtual reality environments and gamification on the classification of imaginary motor (MI) signals and the associated increase in energy in the motor cortex region for neurorehabilitation purposes. Two immersive virtual environments, indoor and outdoor, were selected, each with gamified and non-gamified scenarios. Event-Related Desynchronization (ERD) data underwent analyses to determine if there were significant differences in ERD levels between distinct age groups and whether Fully Immersive Virtual Reality (FIVR) environments induced notable energy increases. The initial analysis found no significant energy changes between age groups under constant environmental conditions. In the second analysis, FIVR environments did not lead to a statistically significant increase in cortical energy for the 21–24 age group (Group I). However, a notable difference in cortical energy increase was identified between gamified and non-gamified environments within the 32–43 age group (Group II). The study also explored the impact of environmental factors on MI signal classification using four deep learning algorithms. The Recurrent Neural Network (RNN) classifier exhibited the highest performance, with an average accuracy of 86.83%. Signals recorded indoors showed higher average classification performance, with a significant difference observed among age groups. Group I participants performed better in non-gamified environments (88.8%), while Group II achieved high performance indoors, especially in the gamified scenario (93.6%). Overall, the research underscores the potential of immersive virtual environments and gamification in enhancing MI signal classification and cortical energy increase, with age and environmental factors influencing the outcomes.

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Section: Introductionmentioning

confidence: 99%

Designing an immersive virtual reality environment for hand rehabilitation purposes: A preliminary study

Tastan,

Başpınar,

Hamurcu

et al. 2024

Preprint

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“…While extensively studied in BCI systems, P300 is susceptible to interference and prolonged fixation on light sources, while SSVEPs may lead to visual fatigue. In contrast, the spontaneous motor imagery (MI) signals, generated without external stimuli, have been widely used in EEG BCIs [30][31][32][33]. Despite various advantages over existing paradigms [34], MI signals exhibit individual variability in strength and location.…”

Section: Introductionmentioning

confidence: 99%

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface

Huang,

Wang,

Fan

et al. 2024

Brain Sciences

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In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time–frequency maps using continuous wavelet transform. Based on these time–frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.

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“…Virtual reality (VR) has emerged as one particular presentation modality that has shown to positively impact BCI performance (Vourvopoulos et al, 2016 ; Amini Gougeh and Falk, 2022a ; Arpaia et al, 2022 ; Choy et al, 2022 ). VR-based presentation can increase the sense of embodiment, improve immersion, and foster greater engagement levels, which, in turn, could lead to the generation of neural signals with increased discriminability (Škola and Liarokapis, 2018 ; Vourvopoulos et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing motor imagery detection efficacy using multisensory virtual reality priming

Gougeh

Falk

2023

Front. Neuroergonomics

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Brain-computer interfaces (BCI) have been developed to allow users to communicate with the external world by translating brain activity into control signals. Motor imagery (MI) has been a popular paradigm in BCI control where the user imagines movements of e.g., their left and right limbs and classifiers are then trained to detect such intent directly from electroencephalography (EEG) signals. For some users, however, it is difficult to elicit patterns in the EEG signal that can be detected with existing features and classifiers. As such, new user control strategies and training paradigms have been highly sought-after to help improve motor imagery performance. Virtual reality (VR) has emerged as one potential tool where improvements in user engagement and level of immersion have shown to improve BCI accuracy. Motor priming in VR, in turn, has shown to further enhance BCI accuracy. In this pilot study, we take the first steps to explore if multisensory VR motor priming, where haptic and olfactory stimuli are present, can improve motor imagery detection efficacy in terms of both improved accuracy and faster detection. Experiments with 10 participants equipped with a biosensor-embedded VR headset, an off-the-shelf scent diffusion device, and a haptic glove with force feedback showed that significant improvements in motor imagery detection could be achieved. Increased activity in the six common spatial pattern filters used were also observed and peak accuracy could be achieved with analysis windows that were 2 s shorter. Combined, the results suggest that multisensory motor priming prior to motor imagery could improve detection efficacy.

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Virtual Reality Assisted Motor Imagery for Early Post-Stroke Recovery: A Review

Cited by 12 publications

References 100 publications

Designing an immersive virtual reality environment for hand rehabilitation purposes: A preliminary study

Designing an immersive virtual reality environment for hand rehabilitation purposes: A preliminary study

Decoding Subject-Driven Cognitive States from EEG Signals for Cognitive Brain–Computer Interface

Enhancing motor imagery detection efficacy using multisensory virtual reality priming

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