Error detection and correction in intracortical brain-machine interfaces controlling two finger groups.

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2023-08-25 DOI:10.1088/1741-2552/acef95
Dylan M Wallace, Miri Benyamini, Sam Nason-Tomaszewski, Joseph T Costello, Luis H Cubillos, Matthew J Mender, Hisham Temmar, Matthew S Willsey, Parag G Patil, Cynthia A Chestek, Miriam Zacksenhouse
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Abstract

Objective.While brain-machine interfaces (BMIs) are promising technologies that could provide direct pathways for controlling the external world and thus regaining motor capabilities, their effectiveness is hampered by decoding errors. Previous research has demonstrated the detection and correction of BMI outcome errors, which occur at the end of trials. Here we focus on continuous detection and correction of BMI execution errors, which occur during real-time movements.Approach.Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex. The monkeys performed single or two-finger group BMI tasks where a Kalman filter decoded binned spiking-band power into intended finger kinematics. Neural activity was analyzed to determine how it depends not only on the kinematics of the fingers, but also on the distance of each finger-group to its target. We developed a method to detect erroneous movements, i.e. consistent movements away from the target, from the same neural activity used by the Kalman filter. Detected errors were corrected by a simple stopping strategy, and the effect on performance was evaluated.Mainresults.First we show that including distance to target explains significantly more variance of the recorded neural activity. Then, for the first time, we demonstrate that neural activity in motor cortex can be used to detect execution errors during BMI controlled movements. Keeping false positive rate below5%, it was possible to achieve mean true positive rate of28.1%online. Despite requiring 200 ms to detect and react to suspected errors, we were able to achieve a significant improvement in task performance via reduced orbiting time of one finger group.Significance.Neural activity recorded in motor cortex for BMI control can be used to detect and correct BMI errors and thus to improve performance. Further improvements may be obtained by enhancing classification and correction strategies.

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控制两指群的脑机接口的错误检测与校正。
目的:虽然脑机接口是一种很有前途的技术,可以为控制外部世界提供直接途径,从而恢复运动能力,但它们的有效性受到解码错误的阻碍。先前的研究已经证明了BMI结果错误的检测和纠正,这些错误发生在试验结束时。在这里,我们重点关注实时运动过程中发生的BMI执行错误的连续检测和校正。方法:两只成年雄性恒河猴在运动皮层植入犹他阵列。猴子执行单个或两个手指组的BMI任务,其中卡尔曼滤波器将装箱的尖峰带功率解码为预期的手指运动学。分析了神经活动,以确定它不仅取决于手指的运动学,还取决于每个手指组到目标的距离。我们开发了一种方法来从卡尔曼滤波器使用的相同神经活动中检测错误的运动,即远离目标的一致运动。通过简单的停止策略纠正检测到的错误,并评估对性能的影响。主要结果。首先,我们发现,包括到目标的距离可以显著解释记录的神经活动的更多方差。然后,我们首次证明,运动皮层的神经活动可以用来检测BMI控制的运动中的执行错误。将假阳性率控制在5%以下,可以实现平均真阳性率281%的在线。尽管需要200 ms来检测和应对可疑错误,我们能够通过减少一个手指组的轨道运行时间来显著提高任务性能。意义。运动皮层记录的用于控制BMI的神经活动可用于检测和纠正BMI错误,从而提高表现。可以通过增强分类和校正策略来获得进一步的改进。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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