用于优化 P300 拼写器系统中信号采集的 EEG 电极数量和配置的方法。
A method for optimizing EEG electrode number and configuration for signal acquisition in P300 speller systems.
机构信息
University of California, Los Angeles, Department of Bioengineering, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; University of California, Los Angeles, Medical Imaging Informatics Group, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
University of California, Los Angeles, Department of Bioengineering, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
出版信息
Clin Neurophysiol. 2015 Jun;126(6):1171-1177. doi: 10.1016/j.clinph.2014.09.021. Epub 2014 Sep 28.
OBJECTIVE
The P300 speller is intended to restore communication to patients with advanced neuromuscular disorders, but clinical implementation may be hindered by several factors, including system setup, burden, and cost. Our goal was to develop a method that can overcome these barriers by optimizing EEG electrode number and placement for P300 studies within a population of subjects.
METHODS
A Gibbs sampling method was developed to find the optimal electrode configuration given a set of P300 speller data. The method was tested on a set of data from 15 healthy subjects using an established 32-electrode pattern. Resulting electrode configurations were then validated using online prospective testing with a naïve Bayes classifier in 15 additional healthy subjects.
RESULTS
The method yielded a set of four posterior electrodes (PO₈, PO₇, POZ, CPZ), which produced results that are likely sufficient to be clinically effective. In online prospective validation testing, no significant difference was found between subjects' performances using the reduced and the full electrode configurations.
CONCLUSIONS
The proposed method can find reduced sets of electrodes within a subject population without reducing performance.
SIGNIFICANCE
Reducing the number of channels may reduce costs, set-up time, signal bandwidth, and computation requirements for practical online P300 speller implementation.
目的
P300 拼写器旨在为患有晚期神经肌肉疾病的患者恢复交流能力，但临床实施可能会受到多种因素的阻碍，包括系统设置、负担和成本。我们的目标是开发一种方法，通过优化 P300 研究中的 EEG 电极数量和位置，克服这些障碍。
方法
开发了一种 Gibbs 抽样方法，用于在一组 P300 拼写器数据中找到最佳电极配置。该方法在 15 名健康受试者的一组数据上进行了测试，使用了已建立的 32 电极模式。然后，使用基于朴素贝叶斯分类器的在线前瞻性测试，在另外 15 名健康受试者中对所得电极配置进行了验证。
结果
该方法产生了一组四个后电极（PO₈、PO₇、POZ、CPZ），其结果可能足以达到临床效果。在在线前瞻性验证测试中，使用减少和完整电极配置的受试者表现之间未发现显著差异。
结论
所提出的方法可以在不降低性能的情况下在受试者群体中找到减少的电极集。
意义
减少通道数量可以降低实际在线 P300 拼写器实现的成本、设置时间、信号带宽和计算要求。
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