使用通用分类器在线异步检测脊髓损伤参与者的错误相关电位
Online asynchronous detection of error-related potentials in participants with a spinal cord injury using a generic classifier
摘要：
BCI用户对错误的意识与名为错误相关电位（ErrP）的皮层签名相关联。将ErrPs的检测结合到BCI中可以提高BCI的性能。这项工作是三重的。首先，我们调查如果ErrP分类是从健全的参与者转移到脊髓损伤（SCI）的参与者。其次，我们测试这个通用ErrP分类与SCI和控制参与者，在没有离线校准的在线实验。第三，我们调查的ErrPs在两组参与者的形态。我们使用以前记录的脑电图（EEG）数据从健全的参与者训练ErrP分类。我们在一个有16名新参与者的在线实验中异步测试了分类器：8名SCI参与者和8名健全的对照参与者。该实验没有离线校准，参与者从一开始就收到了关于ErrPs检测的反馈。通用分类器不是用用户的大脑信号训练的。尽管如此，在使用个性化决策阈值的在线实验中，其性能得到了优化。SCI参与者呈现非均匀的ErrP形态，其中4人没有呈现清晰的ErrP信号。通用分类器在具有明确ErrP信号的参与者中的机会水平以上执行，独立于SCI（16名参与者中的11名）。使用通用分类器获得机会水平结果的五名参与者中有三名不会从使用个性化分类器中受益。这项工作表明，转移ErrP分类器从健全的参与者与SCI的参与者，在没有离线校准的在线实验中的ErrP的异步检测，提供即时反馈给用户的可行性。
英文摘要：
A BCI user awareness of an error is associated with a cortical signature named error-related potential (ErrP). The incorporation of ErrPs' detection in BCIs can improve BCIs' performance. This work is three-folded. First, we investigate if an ErrP classifier is transferable from able-bodied participants to participants with 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 (EEG) 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 ErrPs' detection from its start. The generic classifier was not trained with the user's brain signals. Still, its performance was optimized during the online experiment with the use of personalized decision thresholds. Participants with SCI presented a non-homogenous ErrP morphology, and four of them did not present clear ErrP signals. The generic classifier performed above chance level 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 benefited 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
