论文标题
一种用于连续脑电图信号中语言段的发作算法
An algorithm for onset detection of linguistic segments in continuous electroencephalogram signals
论文作者
论文摘要
基于想象的单词的大脑计算机接口可以解码主体正在通过大脑信号进行思考以控制外部设备的单词。为了基于脑电图信号中想象的单词来构建一个完全异步的大脑计算机界面，我们需要解决检测想象中词的开始的问题。尽管该领域有一些研究，但问题尚未得到充分解决。在本文中，我们提出了一种方法来解决此问题，通过使用统计，信息理论和混乱理论的值作为正确识别连续信号中想象的单词的发作的特征。在检测想象中的单词的侵害时，我们的方法获得的最高真实正率是使用基于广义HURST指数的特征获得的，这种真实的正速率分别为0.69和0.77，定时误差区域分别为3和4秒。
A Brain Computer Interface based on imagined words can decode the word a subject is thinking on through brain signals to control an external device. In order to build a fully asynchronous Brain Computer Interface based on imagined words in electroencephalogram signals as source, we need to solve the problem of detecting the onset of the imagined words. Although there has been some research in this field, the problem has not been fully solved. In this paper we present an approach to solve this problem by using values from statistics, information theory and chaos theory as features to correctly identify the onset of imagined words in a continuous signal. On detecting the onsets of imagined words, the highest True Positive Rate achieved by our approach was obtained using features based on the generalized Hurst exponent, this True Positive Rate was 0.69 and 0.77 with a timing error tolerance region of 3 and 4 seconds respectively.
