Natural language processing with dynamic classification improves P300 speller accuracy and bit rate.

Natural language processing with dynamic classification improves P300 speller accuracy and bit rate.
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DOI:
10.1088/1741-2560/9/1/016004
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发表时间:
2012-02
影响因子:
4
通讯作者:
Pouratian N
Pouratian N
中科院分区:
工程技术2区
文献类型:
--
作者:
Speier W;Arnold C;Lu J;Taira RK;Pouratian N

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P300拼写器是脑机接口的一个例子,可以恢复神经肌肉疾病受害者的功能。虽然该系统最常见的应用是交流语言,但在解码与语言有关的大脑信号时,语言域的属性和约束迄今尚未被利用。我们假设,将标准逐步线性判别分析与朴素贝叶斯分类器和三元语言模型相结合,将提高P300拼写器的打字速度和准确性。通过整合自然语言处理,我们在试点研究中观察到所有六个受试者的准确性显著提高,比特率提高了40%-60%。这项研究表明,整合有关语言域的信息可以显着提高信号分类。
The P300 speller is an example of a brain-computer interface that can restore functionality to victims of neuromuscular disorders. Although the most common application of this system has been communicating language, the properties and constraints of the linguistic domain have not to date been exploited when decoding brain signals that pertain to language. We hypothesized that combining the standard stepwise linear discriminant analysis with a Naive Bayes classifier and a trigram language model would increase the speed and accuracy of typing with the P300 speller. With integration of natural language processing, we observed significant improvements in accuracy and 40%–60% increases in bit rate for all six subjects in a pilot study. This study suggests that integrating information about the linguistic domain can significantly improve signal classification.
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