Institute of Neural Engineering, TU Graz, Stremayrgasse 16/4, Graz, 8010 Styria, Austria.
BioTechMed-Graz, Graz, Styria, Austria.
J Neural Eng. 2024 Sep 12;21(5). doi: 10.1088/1741-2552/ad7762.
. Over the last decades, error-related potentials (ErrPs) have repeatedly proven especially useful as corrective mechanisms in invasive and non-invasive brain-computer interfaces (BCIs). However, research in this context exclusively investigated the distinction of discrete events intoorto the present day. Due to this predominant formulation as a binary classification problem, classical ErrP-based BCIs fail to monitor tasks demanding quantitative information on error severity rather than mere qualitative decisions on error occurrence. As a result, fine-tuned and natural feedback control based on continuously perceived deviations from an intended target remains beyond the capabilities of previously used BCI setups.To address this issue for future BCI designs, we investigated the feasibility of regressing rather than classifying error-related activity non-invasively from the brain.Using pre-recorded data from ten able-bodied participants in three sessions each and a multi-output convolutional neural network, we demonstrated the above-chance regression of ongoing target-feedback discrepancies from brain signals in a pseudo-online fashion. In a second step, we used this inferred information about the target deviation to correct the initially displayed feedback accordingly, reporting significant improvements in correlations between corrected feedback and target trajectories across feedback conditions.Our results indicate that continuous information on target-feedback discrepancies can be successfully regressed from cortical activity, paving the way to increasingly naturalistic, fine-tuned correction mechanisms for future BCI applications.
在过去的几十年里，错误相关电位（ErrPs）已经被证明在侵入性和非侵入性脑机接口（BCI）中作为校正机制特别有用。然而，在这方面的研究仅将离散事件的区分进行到今天。由于这种主要的表述形式是一个二进制分类问题，基于经典 ErrP 的 BCI 无法监测需要关于错误严重程度的定量信息的任务，而不仅仅是关于错误发生的定性决策。因此，基于连续感知到的与预期目标的偏差的精细调整和自然反馈控制仍然超出了以前使用的 BCI 设置的能力范围。为了解决未来 BCI 设计中的这个问题，我们研究了从大脑中进行错误相关活动的回归而不是分类的可行性。我们使用了来自十名健康参与者的三 sessions 中的预记录数据和一个多输出卷积神经网络，以伪在线方式展示了从脑信号中进行目标反馈差异的高于机会的回归。在第二步中，我们使用关于目标偏差的推断信息来相应地校正初始显示的反馈，报告了在反馈条件下校正反馈和目标轨迹之间的相关性的显著提高。我们的结果表明，可以成功地从皮质活动中回归目标反馈差异的连续信息，为未来 BCI 应用的越来越自然、精细调整的校正机制铺平了道路。
