使用阿尔法节奏和听觉菜单的辅助机器人机械手的手动3D控制:概念验证

Signals Pub Date : 2022-06-16 DOI:10.3390/signals3020024
Ana S. Santos Cardoso, R. L. Kæseler, M. Jochumsen, Lotte N. S. Andreasen Struijk
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引用次数: 0

摘要

脑机接口(bci)被认为是严重运动障碍患者的潜在工具,例如那些患有肌萎缩侧索硬化症的人,这些人使得依赖运动的接口无法使用。本研究的目的是开发一个依赖的脑机接口系统,用于机械臂的手动终点控制。一个概念验证系统被设计使用顶枕α波调制和循环菜单与听觉线索。用户选择要执行的动作,并在必要时异步停止该动作。容忍间隔允许用户取消或确认操作。八名身体健全的受试者使用该系统执行捡放任务。为了研究潜在的学习效果,实验在连续两天的过程中进行了两次。受试者获得满意的完成率(第一天和第二天分别为84.0±15.0%和74.4±34.5%)和高路径效率(88.9±11.7%和92.2±9.6%)。受试者完成每项任务的平均时间为439.7±203.3秒,但机器人只有10%的时间处于运动状态。这两天的表现没有显著差异。开发的控制方案为用户提供了直观的控制,但在等待正确的目标(听觉提示)上花费了相当多的时间。执行其他大脑信号可能会加快其速度。
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Manual 3D Control of an Assistive Robotic Manipulator Using Alpha Rhythms and an Auditory Menu: A Proof-of-Concept
Brain–Computer Interfaces (BCIs) have been regarded as potential tools for individuals with severe motor disabilities, such as those with amyotrophic lateral sclerosis, that render interfaces that rely on movement unusable. This study aims to develop a dependent BCI system for manual end-point control of a robotic arm. A proof-of-concept system was devised using parieto-occipital alpha wave modulation and a cyclic menu with auditory cues. Users choose a movement to be executed and asynchronously stop said action when necessary. Tolerance intervals allowed users to cancel or confirm actions. Eight able-bodied subjects used the system to perform a pick-and-place task. To investigate the potential learning effects, the experiment was conducted twice over the course of two consecutive days. Subjects obtained satisfactory completion rates (84.0 ± 15.0% and 74.4 ± 34.5% for the first and second day, respectively) and high path efficiency (88.9 ± 11.7% and 92.2 ± 9.6%). Subjects took on average 439.7 ± 203.3 s to complete each task, but the robot was only in motion 10% of the time. There was no significant difference in performance between both days. The developed control scheme provided users with intuitive control, but a considerable amount of time is spent waiting for the right target (auditory cue). Implementing other brain signals may increase its speed.
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CiteScore
3.20
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审稿时长
11 weeks
期刊最新文献
Detection of Movement and Lead-Popping Artifacts in Polysomnography EEG Data. Development of an Integrated System of sEMG Signal Acquisition, Processing, and Analysis with AI Techniques Correction: Martin et al. ApeTI: A Thermal Image Dataset for Face and Nose Segmentation with Apes. Signals 2024, 5, 147–164 On the Impulse Response of Singular Discrete LTI Systems and Three Fourier Transform Pairs Noncooperative Spectrum Sensing Strategy Based on Recurrence Quantification Analysis in the Context of the Cognitive Radio
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