Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
Department of Electrical Engineering & Electronics, University of Liverpool, Liverpool, United Kingdom.
Comput Methods Programs Biomed. 2020 Jul;191:105419. doi: 10.1016/j.cmpb.2020.105419. Epub 2020 Feb 27.
An Error related Potential (ErrP) can be noninvasively and directly measured from the scalp through electroencephalography (EEG), as response, when a person realizes they are making an error during a task (as a consequence of a cognitive error performed from the user). It has been shown that ErrPs can be automatically detected with time-discrete feedback tasks, which are widely applied in the Brain-Computer Interface (BCI) field for error correction or adaptation. In this work, a semi-supervised algorithm, namely the Functional Source Separation (FSS), is proposed to estimate a spatial filter for learning the ErrPs and to enhance the evoked potentials.
EEG data recorded on six subjects were used to evaluate the proposed method based on FFS algorithm in comparison with the xDAWN algorithm. FSS- and xDAWN-based methods were compared also to the Cz and FCz single channel. Single-trial classification was considered to evaluate the performances of the approaches. (Both the approaches were evaluated on single-trial classification of EEGs.) RESULTS: The results presented using the Bayesian Linear Discriminant Analysis (BLDA) classifier, show that FSS (accuracy 0.92, sensitivity 0.95, specificity 0.81, F1-score 0.95) overcomes the other methods (Cz - accuracy 0.72, sensitivity 0.74, specificity 0.63, F1-score 0.74; FCz - accuracy 0.72, sensitivity 0.75, specificity 0.61, F1-score 0.75; xDAWN - accuracy 0.75, sensitivity 0.79, specificity 0.61, F1-score 0.79) in terms of single-trial classification.
The proposed FSS-based method increases the single-trial detection accuracy of ErrPs with respect to both single channel (Cz, FCz) and xDAWN spatial filter.
当一个人在任务中意识到自己犯了错误（由于用户执行的认知错误）时，可以通过脑电图（EEG）从头皮上非侵入性地直接测量到与错误相关的潜在错误（ErrP）。已经表明，可以使用时间离散反馈任务自动检测到 ErrP，该任务广泛应用于脑机接口（BCI）领域以进行错误纠正或适应。在这项工作中，提出了一种半监督算法，即功能源分离（FSS），用于估计学习 ErrP 的空间滤波器并增强诱发的电位。
使用六名受试者的 EEG 数据评估基于 FFS 算法的提出方法，并与 xDAWN 算法进行比较。还将 FSS 和 xDAWN 方法与 Cz 和 FCz 单通道进行了比较。考虑了单试分类，以评估方法的性能。（两种方法都在 EEG 的单试分类中进行了评估。）
使用贝叶斯线性判别分析（BLDA）分类器呈现的结果表明，FSS（准确性 0.92，敏感性 0.95，特异性 0.81，F1 得分 0.95）优于其他方法（Cz-准确性 0.72，敏感性 0.74，特异性 0.63，F1 得分 0.74；FCz-准确性 0.72，敏感性 0.75，特异性 0.61，F1 得分 0.75；xDAWN-准确性 0.75，敏感性 0.79，特异性 0.61，F1 得分 0.79）在单试分类方面。
与单通道（Cz、FCz）和 xDAWN 空间滤波器相比，提出的基于 FSS 的方法提高了 ErrP 的单试检测准确性。
