基于点击的大脑皮层电图脑机接口可实现长期高性能开关扫描拼写。

IF 5.4 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Daniel N. Candrea, Samyak Shah, Shiyu Luo, Miguel Angrick, Qinwan Rabbani, Christopher Coogan, Griffin W. Milsap, Kevin C. Nathan, Brock A. Wester, William S. Anderson, Kathryn R. Rosenblatt, Alpa Uchil, Lora Clawson, Nicholas J. Maragakis, Mariska J. Vansteensel, Francesco V. Tenore, Nicolas F. Ramsey, Matthew S. Fifer, Nathan E. Crone
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引用次数: 0

摘要

背景:脑机接口(BCI)可通过神经控制电脑打字应用,恢复运动和/或语言障碍患者的交流。单指令点击检测器提供了一种基本但功能强大的能力:我们试图在一名患有肌萎缩性脊髓侧索硬化症的人类临床试验参与者(ClinicalTrials.gov,NCT03567213)身上测试使用长期植入的高密度皮质电图(ECoG)BCI(覆盖感觉运动皮层)进行点击解码的性能和长期稳定性。我们使用少量训练数据训练了参与者的点击检测器(结果:通过使用点击检测器来浏览开关扫描拼写界面,研究对象可以保持每分钟 10.2 个字符的中位拼写速度。虽然信号功率调制的瞬时降低可能会中断固定模型的使用,但新的点击检测器在使用更少数据进行训练的情况下也能获得相当的性能(结论:新的点击检测器在使用更少数据进行训练的情况下也能获得相当的性能):这些结果表明,点击检测器可以使用较小的心电图数据集进行训练,同时在较长时间内保持稳健的性能,从而为 BCI 用户提供基于文本的功能性通信。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch scan spelling
Brain-computer interfaces (BCIs) can restore communication for movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command click detectors provide a basic yet highly functional capability. We sought to test the performance and long-term stability of click decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis. We trained the participant’s click detector using a small amount of training data (<44 min across 4 days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. Using a click detector to navigate a switch scanning speller interface, the study participant can maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation can interrupt usage of a fixed model, a new click detector can achieve comparable performance despite being trained with even less data (<15 min, within 1 day). These results demonstrate that a click detector can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users. Amyotrophic lateral sclerosis (ALS) is a progressive disease of the nervous system that causes muscle weakness and leads to paralysis. People living with ALS therefore struggle to communicate with family and caregivers. We investigated whether the brain signals of a participant with ALS could be used to control a spelling application. Specifically, when the participant attempted a grasping movement, a computer method detected increased brain signals from electrodes implanted on the surface of his brain, and thereby generated a mouse-click. The participant clicked on letters or words from a spelling application to type sentences. Our method was trained using 44 min’ worth of brain signals and performed reliably for three months without any retraining. This approach can potentially be used to restore communication to other severely paralyzed individuals over an extended time period and after only a short training period. Candrea et al. develop a brain-computer interface click detection algorithm using electrocorticographic signals. Using this click detector, a clinical trial participant with amyotrophic lateral sclerosis was able to control a switch-scanning spelling application and achieve high rates of spelling without model retraining.
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