基于核范数正则化的结构化脑电张量分类:改进P300分类

B. Hunyadi, Marco Signoretto, S. Debener, S. Huffel, M. Vos
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引用次数: 6

摘要

选择合适的方法进行单次脑电分类是脑机接口(bci)的关键。在这里,我们考虑一个听觉怪异的范式,记录在正常的室内和室外步行条件。感兴趣的信号,即事件相关电位(ERP)的P300分量,与噪声不同,是由通道、时间和频率或可能的其他类型特征所跨越的多维空间中的结构化信号。因此,我们使用核范数对脑电图数据的张量表示应用谱正则化。由于核范数惩罚传递的先验结构信息,我们期望与传统方法相比,特别是在噪声条件下和小样本量的情况下,性能得到改善。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Structured EEG Tensors Using Nuclear Norm Regularization: Improving P300 Classification
Choosing an appropriate approach for single-trial EEG classification is a key factor in brain computer interfaces (BCIs). Here we consider an auditory oddball paradigm, recorded in normal indoor and walking outdoor conditions. The signal of interest, namely the P300 component of the event related potential (ERP), unlike noise, is a structured signal in the multidimensional space spanned by channels, time and frequency or possibly other types of features. Therefore, we apply spectral regularization using nuclear norm on a tensorial representation of the EEG data. Due to the a-priori structural information conveyed by the nuclear norm penalty, we expect an improved performance compared to traditional approaches, especially under noisy conditions and in case of small sample sizes.
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