EEG Analysis of Imagined Speech

Sadaf Iqbal, Muhammed Shanir P.P., Y. Khan, O. Farooq
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引用次数: 10

Abstract

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.
想象语音的脑电图分析
头皮脑电图(EEG)是脑机接口(bci)中获取EEG数据最常用的方法之一。在世界范围内,有许多人患有妨碍正常交流的残疾。沟通脑机接口是一个很好的工具,帮助患者与他人沟通。本文对头皮脑电数据进行分析,以区分想象元音/a/、/u/和无动作或休息作为控制状态。平均绝对偏差(MAD)和算术平均值作为特征将数据分类为/a/, /u/或rest类之一。该算法对两类问题的分类准确率为87.5-100%,对三类问题的分类准确率为78.33-96.67%,可用于通信bci、语音假体开发和合成心灵感应系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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