Cecotti Hubert, Ries Anthony J
Faculty of Computing and Engineering, Ulster University, Magee campus, Londonderry BT48 7JL, Northern Ireland, UK.
Human Research and Engineering Directorate, US Army Research Laboratory, Aberdeen, Proving Ground, MD 21005, United States.
Int J Psychophysiol. 2017 Jan;111:156-169. doi: 10.1016/j.ijpsycho.2016.07.500. Epub 2016 Jul 22.
The detection of event-related potentials (ERPs) in the electroencephalogram (EEG) signal is a fundamental component in non-invasive brain-computer interface (BCI) research, and in modern cognitive neuroscience studies. Whereas the grand average response across trials provides an estimation of essential characteristics of a brain-evoked response, an estimation of the differences between trials for a particular type of stimulus can provide key insight about the brain dynamics and possible origins of the brain response. The research in ERP single-trial detection has been mainly driven by applications in biomedical engineering, with an interest from machine learning and signal processing groups that test novel methods on noisy signals. Efficient single-trial detection techniques require processing steps that include temporal filtering, spatial filtering, and classification. In this paper, we review the current state-of-the-art methods for single-trial detection of event-related potentials with applications in BCI. Efficient single-trial detection techniques should embed simple yet efficient functions requiring as few hyper-parameters as possible. The focus of this paper is on methods that do not include a large number of hyper-parameters and can be easily implemented with datasets containing a limited number of trials. A benchmark of different classification methods is proposed on a database recorded from sixteen healthy subjects during a rapid serial visual presentation task. The results support the conclusion that single-trial detection can be achieved with an area under the ROC curve superior to 0.9 with less than ten sensors and 20 trials corresponding to the presentation of a target. Whereas the number of sensors is not a key element for efficient single-trial detection, the number of trials must be carefully chosen for creating a robust classifier.
在脑电图（EEG）信号中检测事件相关电位（ERP）是无创脑机接口（BCI）研究以及现代认知神经科学研究的一个基本组成部分。虽然跨试验的总体平均反应提供了对脑诱发反应基本特征的估计，但对特定类型刺激的试验间差异的估计可以提供有关脑动力学和脑反应可能起源的关键见解。ERP单次试验检测的研究主要由生物医学工程中的应用推动，机器学习和信号处理团队对在噪声信号上测试新方法很感兴趣。高效的单次试验检测技术需要包括时间滤波、空间滤波和分类在内的处理步骤。在本文中，我们回顾了用于BCI应用中事件相关电位单次试验检测的当前最先进方法。高效的单次试验检测技术应嵌入简单而高效的功能，所需的超参数尽可能少。本文的重点是不包含大量超参数且可以在包含有限试验次数的数据集上轻松实现的方法。在一项快速序列视觉呈现任务中，从16名健康受试者记录的数据库上提出了不同分类方法的基准。结果支持这样的结论：使用少于10个传感器和对应于目标呈现的20次试验，ROC曲线下面积优于0.9时可以实现单次试验检测。虽然传感器数量不是高效单次试验检测的关键因素，但必须仔细选择试验次数以创建一个稳健的分类器。
