Fusion with language models improves spelling accuracy for ERP-based brain computer interface spellers.
Fusion with language models improves spelling accuracy for ERP-based brain computer interface spellers.
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与语言模型的融合可提高基于ERP的大脑计算机接口拼写拼写的拼写精度。
DOI：
10.1109/iembs.2011.6091429
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Fried-Oken M
中科院分区：
文献类型：
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作者：
Orhan U;Erdogmus D;Roark B;Purwar S;Hild KE 2nd;Oken B;Nezamfar H;Fried-Oken M
事件相关电位（ERP）对应于脑电（EEG）中的刺激，可用于检测脑机接口（BCI）的人的意图。这种范例被广泛用于使用BCI构建逐个字母的文本输入系统。然而，使用仅依赖于EEG响应的BCI打字机通常对于单次试验操作将不够准确，并且现有系统利用多次试验方案以速度为代价来实现准确性。因此，结合基于先验或额外证据的语言模型对于提高准确性和速度至关重要。在本文中，我们研究了贝叶斯融合的n-gram语言模型与正则化判别分析ERP检测器的EEG为基础的脑机接口的影响。字母分类准确性进行了严格的评估，不同的语言模型的顺序，以及ERP诱导试验的数量。结果表明，语言模型对字母分类的准确性有显著的贡献。具体来说，我们发现，BCI拼写支持的4-gram语言模型可以实现相同的性能，使用3次试验ERP分类的单词的初始字母，并使用单次试验ERP分类的后续。总的来说，从EEG和语言模型的证据融合产生了一个显着的机会，以增加基于BCI的打字系统的字率。
Event related potentials (ERP) corresponding to a stimulus in electroencephalography (EEG) can be used to detect the intent of a person for brain computer interfaces (BCI). This paradigm is widely utilized to build letter-by-letter text input systems using BCI. Nevertheless using a BCI-typewriter depending only on EEG responses will not be sufficiently accurate for single-trial operation in general, and existing systems utilize many-trial schemes to achieve accuracy at the cost of speed. Hence incorporation of a language model based prior or additional evidence is vital to improve accuracy and speed. In this paper, we study the effects of Bayesian fusion of an n-gram language model with a regularized discriminant analysis ERP detector for EEG-based BCIs. The letter classification accuracies are rigorously evaluated for varying language model orders as well as number of ERP-inducing trials. The results demonstrate that the language models contribute significantly to letter classification accuracy. Specifically, we find that a BCI-speller supported by a 4-gram language model may achieve the same performance using 3-trial ERP classification for the initial letters of the words and using single trial ERP classification for the subsequent ones. Overall, fusion of evidence from EEG and language models yields a significant opportunity to increase the word rate of a BCI based typing system.