Retrospectively supervised click decoder calibration for self-calibrating point-and-click brain-computer interfaces.
Retrospectively supervised click decoder calibration for self-calibrating point-and-click brain-computer interfaces.
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DOI：
10.1016/j.jphysparis.2017.03.001
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Hochberg LR
中科院分区：
文献类型：
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作者：
Jarosiewicz B;Sarma AA;Saab J;Franco B;Cash SS;Eskandar EN;Hochberg LR
脑机接口（BCI）旨在通过允许控制计算机屏幕上的光标或其他具有神经活动的效应器来恢复严重运动障碍者的独立性。然而，神经信号中的生理和/或记录相关的非平稳性可能限制长期解码稳定性，并且每当神经控制降级以执行解码器重新校准例程时，用户暂停BCI的使用将是乏味的。我们最近证明了运动学解码器（即控制光标移动的解码器）可以使用在BCI的实际点击控制期间获取的数据通过基于用户的后续选择回顾性地推断用户的预期移动方向来重新校准。在这里，我们扩展了这些方法，以允许点击解码器也使用在实际BCI使用期间获取的数据进行重新校准。我们回顾性地将神经数据模式标记为对应于在所有时间段期间的“点击”，其中点击对数似然（使用线性判别分析或LDA解码）已经高于在实时神经控制期间使用的点击阈值。我们标记为“非点击”的运动解码器的回溯目标推理（RTI）的示意图确定与预期的光标移动一致的那些时期。一旦这些神经活动模式被标记，点击解码器就使用标准的监督分类器训练方法进行校准。结合实时偏差校正和基线放电率跟踪，这组“回顾性标记”解码器校准方法使患有肌萎缩侧索硬化症（T9）的BrainGate参与者能够在29天的11个研究会话中自由打字，保持对光标移动和点击的高性能神经控制，而无需中断虚拟键盘使用明确的校准任务。通过消除对规定目标和预先指定的点击时间的繁琐校准任务的需要，这种方法提高了皮质内脑机接口对严重运动残疾患者的潜在临床实用性。
Brain-computer interfaces (BCIs) aim to restore independence to people with severe motor disabilities by allowing control of a cursor on a computer screen or other effectors with neural activity. However, physiological and/or recording-related nonstationarities in neural signals can limit long-term decoding stability, and it would be tedious for users to pause use of the BCI whenever neural control degrades to perform decoder recalibration routines. We recently demonstrated that a kinematic decoder (i.e. a decoder that controls cursor movement) can be recalibrated using data acquired during practical point-and-click control of the BCI by retrospectively inferring users’ intended movement directions based on their subsequent selections. Here, we extend these methods to allow the click decoder to also be recalibrated using data acquired during practical BCI use. We retrospectively labeled neural data patterns as corresponding to “click” during all time bins in which the click log-likelihood (decoded using linear discriminant analysis, or LDA) had been above the click threshold that was used during real-time neural control. We labeled as “non-click” those periods that the kinematic decoder’s retrospective target inference (RTI) heuristics determined to be consistent with intended cursor movement. Once these neural activity patterns were labeled, the click decoder was calibrated using standard supervised classifier training methods. Combined with real-time bias correction and baseline firing rate tracking, this set of “retrospectively labeled” decoder calibration methods enabled a BrainGate participant with amyotrophic lateral sclerosis (T9) to type freely across 11 research sessions spanning 29 days, maintaining high-performance neural control over cursor movement and click without needing to interrupt virtual keyboard use for explicit calibration tasks. By eliminating the need for tedious calibration tasks with prescribed targets and pre-specified click times, this approach advances the potential clinical utility of intracortical BCIs for individuals with severe motor disability.