文章基本信息

标题：A Combination of Pre- and Postprocessing Techniques to Enhance Self-Paced BCIs
本地全文：下载
作者：Raheleh Mohammadi ; Ali Mahloojifar ; Damien Coyle 等
期刊名称：Advances in Human-Computer Interaction
印刷版ISSN：1687-5893
电子版ISSN：1687-5907
出版年度：2012
卷号：2012
DOI：10.1155/2012/185320
出版社：Hindawi Publishing Corporation
摘要：Mental task onset detection from the continuous electroencephalogram (EEG) in real time is a critical issue in self-paced brain computer interface (BCI) design. The paper shows that self-paced BCI performance can be significantly improved by combining a range of simple techniques including (1) constant-Q filters with varying bandwidth size depending on the center frequency, instead of constant bandwidth filters for frequency decomposition of the EEG signal in the 6 to 36 Hz band; (2) subject-specific postprocessing parameter optimization consisting of dwell time and threshold, and (3) debiasing before postprocessing by readjusting the classification output based on the current and previous brain states, to reduce the number of false detections. This debiasing block is shown to be optimal when activated only in special cases which are predetermined during the training phase. Analysis of the data recorded from seven subjects executing foot movement shows a statistically significant 10% () average improvement in true positive rate (TPR) and a 1% reduction in false positive rate (FPR) detections compared with previous work on the same data.