Retraction to using a deep recurrent neural network with EEG signal to detect Parkinson’s disease

Xu, Shixiao; Wang, Zhihua; Sun, Jingfan; Zhang, Zhiqiang; Wu, Zhaoyun; Tiezhao, Yang; Xue, Gang; Cheng, Chuance

doi:10.21037/atm-2021-25

Cited by 1 publication

(1 citation statement)

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“…Various DNN models have been utilized to decode neural signals originating from the motor cortex [31][32][33][34]. Nonetheless, a few models, particularly those that focus on motor activity during a Virtual Reality simulation, may be helpful for time series datasets.…”

Section: Introductionmentioning

confidence: 99%

Can Brain–Computer Interfaces Replace Virtual Reality Controllers? A Machine Learning Movement Prediction Model during Virtual Reality Simulation Using EEG Recordings

Kritikos,

Makrypidis,

Alevizopoulos

et al. 2023

Virtual Worlds

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Brain–Machine Interfaces (BMIs) have made significant progress in recent years; however, there are still several application areas in which improvement is needed, including the accurate prediction of body movement during Virtual Reality (VR) simulations. To achieve a high level of immersion in VR sessions, it is important to have bidirectional interaction, which is typically achieved through the use of movement-tracking devices, such as controllers and body sensors. However, it may be possible to eliminate the need for these external tracking devices by directly acquiring movement information from the motor cortex via electroencephalography (EEG) recordings. This could potentially lead to more seamless and immersive VR experiences. There have been numerous studies that have investigated EEG recordings during movement. While the majority of these studies have focused on movement prediction based on brain signals, a smaller number of them have focused on how to utilize them during VR simulations. This suggests that there is still a need for further research in this area in order to fully understand the potential for using EEG to predict movement in VR simulations. We propose two neural network decoders designed to predict pre-arm-movement and during-arm-movement behavior based on brain activity recorded during the execution of VR simulation tasks in this research. For both decoders, we employ a Long Short-Term Memory model. The study’s findings are highly encouraging, lending credence to the premise that this technology has the ability to replace external tracking devices.

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Section: Introductionmentioning

confidence: 99%

Can Brain–Computer Interfaces Replace Virtual Reality Controllers? A Machine Learning Movement Prediction Model during Virtual Reality Simulation Using EEG Recordings

Kritikos,

Makrypidis,

Alevizopoulos

et al. 2023

Virtual Worlds

View full text Add to dashboard Cite

show abstract

Retraction to using a deep recurrent neural network with EEG signal to detect Parkinson’s disease

Abstract: to using a deep recurrent neural network with EEG signal to detect Parkinson's disease.

Cited by 1 publication

References 1 publication

Can Brain–Computer Interfaces Replace Virtual Reality Controllers? A Machine Learning Movement Prediction Model during Virtual Reality Simulation Using EEG Recordings

Can Brain–Computer Interfaces Replace Virtual Reality Controllers? A Machine Learning Movement Prediction Model during Virtual Reality Simulation Using EEG Recordings

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