脑机接口竞赛2003——数据集Ib和IIb：利用连续小波变换和t值频谱图从事件相关脑电信号中提取特征

BCI Competition 2003--Data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram.

作者信息

Bostanov Vladimir

机构信息

Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, D-72074 Tübingen, Germany.

出版信息

IEEE Trans Biomed Eng. 2004 Jun;51(6):1057-61. doi: 10.1109/TBME.2004.826702.

DOI:10.1109/TBME.2004.826702

PMID:15188878

Abstract

The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. Applied to event-related brain potential (ERP) data in brain-computer interface (BCI) paradigms, the method provides fully automated detection and quantification of the ERP components that best discriminate between two samples of EEG signals and are, therefore, particularly suitable for classification of single-trial ERPs. A simple and fast CWT computation algorithm is proposed for the transformation of large data sets and single trials. The method was validated in the BCI Competition 2003, where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.

摘要

本文介绍了一种从生物信号中提取特征的新方法——t-CWT。它基于连续小波变换（CWT）和学生t统计量。该方法应用于脑机接口（BCI）范式中的事件相关脑电位（ERP）数据，能够对ERP成分进行全自动检测和量化，这些成分能最佳地区分两个脑电图信号样本，因此特别适用于单次试验ERP的分类。本文提出了一种简单快速的CWT计算算法，用于大数据集和单次试验的变换。该方法在2003年BCI竞赛中得到验证，在该竞赛中，它在两种不同BCI范式（P300拼写器和慢皮层电位（SCP）自我调节）获取的两个数据集上获胜（提供了最佳分类）。本文展示了这些结果。