A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface

Deng, Xinyang; Yu, Zhu Liang; Lin, Canguang; Gu, Zhenghui; Li, Yuanqing

doi:10.1109/tnsre.2019.2958076

Cited by 51 publications

(26 citation statements)

References 42 publications

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“…Studies demonstrate that MI may enhance functional recovery of paralyzed limbs [ 2 ], since similar activation sequences occur in the motor cortex during both MI and actual motor execution (ME) [ 3 ]. A brain–machine interface (BMI) allows users to interact with the external world through their brain signals instead of their peripheral muscles [ 4 ]. Extensive research has been conducted to exploit BMIs for post-stroke rehabilitation, as they assist in the restoration of motional ability [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern

2022

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To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a “follow-up” pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs.

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Section: Introductionmentioning

confidence: 99%

Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern

2022

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“…upper extremity rehabilitation [30], ankle rehabilitation robot [31], which can stimulate impaired muscles to perform more precise motion tasks that the patient cannot perform on his/her own. Assistance applications have the same working principle with rehabilitation equipment, but their output commands are used to control aided peripheral experiment like wheelchair [4], speller [5] or meal assistance robot [32].…”

Section: Healthcare Applicationsmentioning

confidence: 99%

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

et al. 2021

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Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signalto-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed.

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“…In particular, where humans and robots interact in complex scenarios where high performance is required [48,49], several strategies have been introduced, such as virtual environments [48], teleoperation with joysticks [50], interfaces with virtual impedance [50], and approaches to force feedback [51]. Thus, these kinds of methods have, for example, been used to interpret navigation commands and monitor robotic systems such as wheelchairs, exoskeletons, and mobile robots [52][53][54] cooperatively. Some are presented to contextualize these proposed solutions with the strategies commonly used in smart walkers (SWs).…”

Section: Related Workmentioning

confidence: 99%

Semi-Remote Gait Assistance Interface: A Joystick with Visual Feedback Capabilities for Therapists

Gomez-Vargas

et al. 2021

Sensors

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The constant growth of pathologies affecting human mobility has led to developing of different assistive devices to provide physical and cognitive assistance. Smart walkers are a particular type of these devices since they integrate navigation systems, path-following algorithms, and user interaction modules to ensure natural and intuitive interaction. Although these functionalities are often implemented in rehabilitation scenarios, there is a need to actively involve the healthcare professionals in the interaction loop while guaranteeing safety for them and patients. This work presents the validation of two visual feedback strategies for the teleoperation of a simulated robotic walker during an assisted navigation task. For this purpose, a group of 14 clinicians from the rehabilitation area formed the validation group. A simple path-following task was proposed, and the feedback strategies were assessed through the kinematic estimation error (KTE) and a usability survey. A KTE of 0.28 m was obtained for the feedback strategy on the joystick. Additionally, significant differences were found through a Mann–Whitney–Wilcoxon test for the perception of behavior and confidence towards the joystick according to the modes of interaction (p-values of 0.04 and 0.01, respectively). The use of visual feedback with this tool contributes to research areas such as remote management of therapies and monitoring rehabilitation of people’s mobility.

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A Bayesian Shared Control Approach for Wheelchair Robot With Brain Machine Interface

Cited by 51 publications

References 42 publications

Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern

Intelligent Classification Technique of Hand Motor Imagery Using EEG Beta Rebound Follow-Up Pattern

Data Analytics in Steady-State Visual Evoked Potential-Based Brain–Computer Interface: A Review

Semi-Remote Gait Assistance Interface: A Joystick with Visual Feedback Capabilities for Therapists

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