脑电分析窗口定位对汉语口语单音节分类的影响

Mingtao Li, Shangdi Liao, S. Pun, Fei Chen
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引用次数: 0

摘要

具有自定步范式的直接语音脑机接口(DS-BCIs)比具有一般同步范式的间接脑机接口更有前途和实用性。由于理想的ds - bci想象语音中难以实现分析窗口的准确起始位置和偏移位置,因此本研究以可听输出清晰的口语为媒介,研究分析窗口准确位置对自定节奏bci的影响。本研究旨在利用移位的分析窗口来模拟基于脑电图的汉语口语单音节元音和词性声调分类中不同程度的分析窗口起始位置误差情况。分析窗口(基于可用显性言语的持续时间)从真实的起始位置偏移。采用黎曼流形方法对采集到的脑电信号进行特征提取,并采用线性判别分析(LDA)对不同的元音和词性音调进行分类。元音和音调分类的结果分别为70.7%和54.9%,处于总体最佳转移水平。研究发现,元音和词性语调分类在不同的分析窗口移动水平上表现最佳。在选择合适的分析窗口时,未移位的脑电信号更适合对元音进行分类,远离起始位置的脑电信号更有利于声调分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effects of EEG Analysis Window Location on Classifying Spoken Mandarin Monosyllables
The direct-speech brain-computer interfaces (DS-BCIs) with self-paced paradigms are much more promising and practical than indirect BCIs with general synchronous paradigms. As the exact onset and offset locations of analysis window are hard to achieve in the imagined speech of ideal DS-BCIs, spoken speech with clear audible output in this study is used as a medium to study the impact of exact location of analysis window in self-paced BCIs. This work aimed to use shifted analysis windows to simulate the situations with different levels of onset location errors of analysis window in the EEG-based classification of spoken Mandarin monosyllables carrying vowels and lexical tones. The analysis window (based on the duration of the available overt speech) was shifted from the true onset location. The Riemannian manifold method was used to extract features for the collected EEG signals, and a linear discriminant analysis (LDA) was employed to classify different vowels and lexical tones. The results in vowel and tone classifications were 70.7% and 54.9%, respectively, at an overall best-shifted level. It was found that vowel and lexical tone classifications reached their best performances at different shifting levels of analysis window. When choosing a suitable analysis window, the EEG signals without shift were more suitable to classify vowels, and those EEG signals away from the onset location were found to benefit tone classification.
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