Bhattacharyya Saugat, Konar Amit, Tibarewala D N, Hayashibe Mitsuhiro
CAMIN Team, INRIA-LIRMM, University of MontpellierMontpellier, France.
Department of Electronics and Telecommunication Engineering, Jadavpur UniveristyKolkata, India.
Front Neurosci. 2017 May 2;11:226. doi: 10.3389/fnins.2017.00226. eCollection 2017.
Reliable detection of error from electroencephalography (EEG) signals as feedback while performing a discrete target selection task across sessions and subjects has a huge scope in real-time rehabilitative application of Brain-computer Interfacing (BCI). Error Related Potentials (ErrP) are EEG signals which occur when the participant observes an erroneous feedback from the system. ErrP holds significance in such closed-loop system, as BCI is prone to error and we need an effective method of systematic error detection as feedback for correction. In this paper, we have proposed a novel scheme for online detection of error feedback directly from the EEG signal in a transferable environment (i.e., across sessions and across subjects). For this purpose, we have used a P300-speller dataset available on a BCI competition website. The task involves the subject to select a letter of a word which is followed by a feedback period. The feedback period displays the letter selected and, if the selection is wrong, the subject perceives it by the generation of ErrP signal. Our proposed system is designed to detect ErrP present in the EEG from new independent datasets, not involved in its training. Thus, the decoder is trained using EEG features of 16 subjects for single-trial classification and tested on 10 independent subjects. The decoder designed for this task is an ensemble of linear discriminant analysis, quadratic discriminant analysis, and logistic regression classifier. The performance of the decoder is evaluated using accuracy, F1-score, and Area Under the Curve metric and the results obtained is 73.97, 83.53, and 73.18%, respectively.
在跨会话和跨受试者执行离散目标选择任务时，将脑电图（EEG）信号中的错误可靠检测作为反馈，在脑机接口（BCI）的实时康复应用中具有巨大的应用前景。错误相关电位（ErrP）是参与者观察到系统的错误反馈时出现的EEG信号。ErrP在这种闭环系统中具有重要意义，因为BCI容易出错，我们需要一种有效的系统错误检测方法作为校正反馈。在本文中，我们提出了一种新颖的方案，用于在可转移环境（即跨会话和跨受试者）中直接从EEG信号在线检测错误反馈。为此，我们使用了BCI竞赛网站上提供的P300拼写器数据集。该任务要求受试者选择一个单词中的字母，随后是反馈期。反馈期显示所选字母，如果选择错误，受试者会通过产生ErrP信号感知到。我们提出的系统旨在从新的独立数据集中检测EEG中存在的ErrP，这些数据集未参与其训练。因此，解码器使用16名受试者的EEG特征进行单试验分类训练，并在10名独立受试者上进行测试。为此任务设计的解码器是线性判别分析、二次判别分析和逻辑回归分类器的集成。使用准确率、F1分数和曲线下面积指标评估解码器的性能，得到的结果分别为73.97%、83.53%和73.18%。
