Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
J Neural Eng. 2021 Mar 29;18(4):046022. doi: 10.1088/1741-2552/abd1eb.
For brain-computer interface (BCI) users, the awareness of an error is associated with a cortical signature known as an error-related potential (ErrP). The incorporation of ErrP detection into BCIs can improve their performance.
This work has three main aims. First, we investigate whether an ErrP classifier is transferable from able-bodied participants to participants with a spinal cord injury (SCI). Second, we test this generic ErrP classifier with SCI and control participants, in an online experiment without offline calibration. Third, we investigate the morphology of ErrPs in both groups of participants.
We used previously recorded electroencephalographic data from able-bodied participants to train an ErrP classifier. We tested the classifier asynchronously, in an online experiment with 16 new participants: 8 participants with SCI and 8 able-bodied control participants. The experiment had no offline calibration and participants received feedback regarding the ErrP detections from the start. To increase the fluidity of the experiment, feedback regarding false positive ErrP detections was not presented to the participants, but these detections were taken into account in the evaluation of the classifier. The generic classifier was not trained with the user's brain signals. However, its performance was optimized during the online experiment by the use of personalized decision thresholds. The classifier's performance was evaluated using trial-based metrics, which considered the asynchronous detection of ErrPs during the entire trial's duration.
Participants with SCI presented a non-homogenous ErrP morphology, and four of them did not present clear ErrP signals. The generic classifier performed better than chance in participants with clear ErrP signals, independently of the SCI (11 out of 16 participants). Three out of the five participants that obtained chance level results with the generic classifier would have not benefitted from the use of a personalized classifier.
This work shows the feasibility of transferring an ErrP classifier from able-bodied participants to participants with SCI, for asynchronous detection of ErrPs in an online experiment without offline calibration, which provided immediate feedback to the users.
对于脑机接口（BCI）用户来说，对错误的意识与一种称为错误相关电位（ErrP）的皮层特征有关。将 ErrP 检测纳入 BCI 可以提高其性能。
这项工作有三个主要目标。首先，我们研究 ErrP 分类器是否可以从健全参与者转移到脊髓损伤（SCI）参与者。其次，我们在没有离线校准的在线实验中使用 SCI 和对照组参与者来测试此通用 ErrP 分类器。第三，我们研究两组参与者的 ErrP 形态。
我们使用以前从健全参与者记录的脑电图数据来训练 ErrP 分类器。我们异步测试了分类器，在一个有 16 名新参与者的在线实验中：8 名 SCI 参与者和 8 名健全对照组参与者。该实验没有离线校准，参与者从一开始就收到有关 ErrP 检测的反馈。为了增加实验的流畅性，没有向参与者显示 ErrP 假阳性检测的反馈，但在评估分类器时考虑了这些检测。通用分类器没有使用用户的脑信号进行训练。然而，它的性能通过在线实验中使用个性化决策阈值进行了优化。使用基于试验的指标评估分类器的性能，该指标考虑了整个试验持续时间内 ErrP 的异步检测。
患有 SCI 的参与者呈现出非同质的 ErrP 形态，其中 4 名参与者没有呈现出清晰的 ErrP 信号。在具有清晰 ErrP 信号的 SCI 参与者中，通用分类器的性能优于随机水平，独立于 SCI（16 名参与者中的 11 名）。在使用通用分类器获得随机水平结果的五名参与者中，有三名不会受益于使用个性化分类器。
这项工作表明，可以将 ErrP 分类器从健全参与者转移到 SCI 参与者，用于在没有离线校准的在线实验中异步检测 ErrP，这为用户提供了即时反馈。
