Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
J Neural Eng. 2018 Jun;15(3):036031. doi: 10.1088/1741-2552/aab806. Epub 2018 Mar 20.
The detection of error-related potentials (ErrPs) in tasks with discrete feedback is well established in the brain-computer interface (BCI) field. However, the decoding of ErrPs in tasks with continuous feedback is still in its early stages.
We developed a task in which subjects have continuous control of a cursor's position by means of a joystick. The cursor's position was shown to the participants in two different modalities of continuous feedback: normal and jittered. The jittered feedback was created to mimic the instability that could exist if participants controlled the trajectory directly with brain signals.
This paper studies the electroencephalographic (EEG)-measurable signatures caused by a loss of control over the cursor's trajectory, causing a target miss.
In both feedback modalities, time-locked potentials revealed the typical frontal-central components of error-related potentials. Errors occurring during the jittered feedback (masked errors) were delayed in comparison to errors occurring during normal feedback (unmasked errors). Masked errors displayed lower peak amplitudes than unmasked errors. Time-locked classification analysis allowed a good distinction between correct and error classes (average Cohen-[Formula: see text], average TPR = 81.8% and average TNR = 96.4%). Time-locked classification analysis between masked error and unmasked error classes revealed results at chance level (average Cohen-[Formula: see text], average TPR = 60.9% and average TNR = 58.3%). Afterwards, we performed asynchronous detection of ErrPs, combining both masked and unmasked trials. The asynchronous detection of ErrPs in a simulated online scenario resulted in an average TNR of 84.0% and in an average TPR of 64.9%.
The time-locked classification results suggest that the masked and unmasked errors were indistinguishable in terms of classification. The asynchronous classification results suggest that the feedback modality did not hinder the asynchronous detection of ErrPs.
在脑机接口（BCI）领域，在具有离散反馈的任务中检测错误相关电位（ErrPs）已经得到很好的确立。然而，在具有连续反馈的任务中解码 ErrPs 仍处于早期阶段。
我们开发了一项任务，参与者通过操纵杆对光标位置进行连续控制。参与者以两种不同的连续反馈模式看到光标位置：正常模式和抖动模式。抖动反馈旨在模拟如果参与者直接使用脑信号控制轨迹可能存在的不稳定性。
本文研究了由于对光标轨迹失去控制而导致目标错过时产生的脑电图（EEG）可测量特征。
在两种反馈模式下，时锁电位揭示了与错误相关的潜在额前-中央典型成分。与正常反馈（未掩蔽错误）相比，在抖动反馈（掩蔽错误）中发生的错误延迟。掩蔽错误的峰值幅度低于未掩蔽错误。时锁分类分析允许在正确和错误类别之间进行很好的区分（平均 Cohen-[Formula: see text]，平均 TPR=81.8%，平均 TNR=96.4%）。掩蔽错误和未掩蔽错误类之间的时锁分类分析显示结果处于随机水平（平均 Cohen-[Formula: see text]，平均 TPR=60.9%，平均 TNR=58.3%）。之后，我们进行了 ErrPs 的异步检测，结合了掩蔽和未掩蔽的试验。在模拟在线场景中，异步检测 ErrPs 的平均 TNR 为 84.0%，平均 TPR 为 64.9%。
时锁分类结果表明，掩蔽和未掩蔽错误在分类方面无法区分。异步分类结果表明，反馈模式不阻碍 ErrPs 的异步检测。
