P300拼写器的混合色范式:与灰度拼写器的比较

Mina R. Meshriky, S. Eldawlatly, G. Aly
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引用次数: 7

摘要

P300拼写系统是脑机接口(bci)最基本的应用之一。传统的P300拼写器由6 × 6的字符网格组成,网格中的每一列或每一行随机增强。在这个强化过程中,被试的脑电图(EEG)数据被记录和分析,以确定要拼写的字符。在本文中,我们演示了如何通过研究在拼写器字符网格(即红、绿、蓝)的列和行中加入不同颜色亮度的影响来改进传统的P300拼写器,而不是传统的一种颜色(即灰度)亮度。在我们的分析中,我们使用Emotiv神经耳机记录了从大脑额、顶叶和枕叶区域获得的头皮脑电图。我们研究了四种不同的特征提取技术以及两种分类器,即线性判别分析(LDA)和线性支持向量机(LSVM)。对四名受试者进行的离线和在线测试表明,与灰度亮度相比,混合颜色亮度情况下的性能显著提高(高达16%)。这些结果表明了在P300拼写器界面中加入颜色的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Intermixed Color Paradigm for P300 Spellers: A Comparison with Gray-Scale Spellers
P300 speller systems represent one of the most basic applications of Brain-Computer Interfaces (BCIs). A traditional P300 speller consists of a 6 by 6 grid of characters in which each column or row in this grid intensifies at random. During such intensification process, the electroencephalography (EEG) data of the subject is recorded and analyzed to determine the character to be spelled. In this paper, we demonstrate how to improve on the traditional P300 speller by investigating the effects of incorporating different color luminance in the columns and rows of the spellers grid-of-characters (i.e. red, green and blue) as opposed to the conventional one-color (i.e. gray-scale) luminance. In our analysis, we used the Emotiv Neuroheadset to record scalp EEG obtained from the frontal, parietal and occipital brain regions. We examine four different feature extraction techniques in addition to two classifiers, namely, Linear Discriminant Analysis (LDA) and Linear Support vector machines (LSVM). Offline and online tests conducted on four subjects demonstrate a significant performance increase (up to 16%) for the intermixed color luminance case compared to the gray luminance one. These results indicate the efficacy of incorporating colors into P300 spellers interface.
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