Utilizing a language model to improve online dynamic data collection in P300 spellers.
Utilizing a language model to improve online dynamic data collection in P300 spellers.
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DOI：
10.1109/tnsre.2014.2321290
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发表时间：
2014-07
期刊：
影响因子：
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通讯作者：
Throckmorton CS
中科院分区：
文献类型：
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作者：
Mainsah BO;Colwell KA;Collins LM;Throckmorton CS
P300拼写器为那些有严重身体缺陷的人提供了一种交流方式，特别是那些患有闭锁综合征的人，如肌萎缩侧索硬化症（ALS）。然而，P300拼写者的使用仍然受到相对较低的通信速率的限制，这是由于需要多个数据测量来提高事件相关电位的信噪比以提高准确性。因此，数据收集的数量对准确性和拼写速度产生了竞争性影响。在字符选择之前自适应地改变数据收集的量已经被证明可以提高拼写准确性和速度。本研究的目标是优化以前开发的动态停止算法，该算法使用贝叶斯方法通过语言模型结合先验知识来控制数据收集。参与者（n = 17）完成在线拼写任务，使用动态停止算法，有和没有一个语言模型。语言模型的添加导致参与者的表现从88.89%准确度下的平均理论比特率46.12比特/分钟提高到90.36%准确度下的54.42比特/分钟（p < 0.0065）。
P300 spellers provide a means of communication for individuals with severe physical limitations, especially those with locked-in syndrome, such as amyotrophic lateral sclerosis (ALS). However, P300 speller use is still limited by relatively low communication rates due to the multiple data measurements that are required to improve the signal-to-noise ratio of event-related potentials for increased accuracy. Therefore, the amount of data collection has competing effects on accuracy and spelling speed. Adaptively varying the amount of data collection prior to character selection has been shown to improve spelling accuracy and speed. The goal of this study was to optimize a previously developed dynamic stopping algorithm that uses a Bayesian approach to control data collection by incorporating a priori knowledge via a language model. Participants (n = 17) completed online spelling tasks using the dynamic stopping algorithm, with and without a language model. The addition of the language model resulted in improved participant performance from a mean theoretical bit rate of 46.12 bits/min at 88.89% accuracy to 54.42 bits/min (p < 0.0065) at 90.36% accuracy.