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基于性能引导约束的通用 P300 脑-机接口呈现范式。

A general P300 brain-computer interface presentation paradigm based on performance guided constraints.

机构信息

Algoma University, Sault Ste. Marie, Ontario P6A 2G4, Canada.

出版信息

Neurosci Lett. 2012 Dec 7;531(2):63-8. doi: 10.1016/j.neulet.2012.08.041. Epub 2012 Aug 29.

Abstract

An electroencephalographic-based brain-computer interface (BCI) can provide a non-muscular method of communication. A general model for P300-based BCI stimulus presentations is introduced--the "m choose n" or C(m (number of flashes per sequence), n (number of flashes per item)) paradigm, which is a universal extension of the previously reported checkerboard paradigm (CBP). C(m,n) captures all possible (unconstrained) ways to flash target items, and then applies constraints to enhance ERP's produced by attended matrix items. We explore a C(36,5) instance of C(m,n) called the "five flash paradigm" (FFP) and compare its performance to the CBP. Eight subjects were tested in each paradigm, counter-balanced. Twelve minutes of calibration data were used as input to a stepwise linear discriminant analysis to derive classification coefficients used for online classification. Accuracy was consistently high for FFP (88%) and CBP (90%); information transfer rate was significantly higher for the FFP (63 bpm) than the CBP (48 bpm). The C(m,n) is a novel and effective general strategy for organizing stimulus groups. Appropriate choices for "m," "n," and specific constraints can improve presentation paradigms by adjusting the parameters in a subject specific manner. This may be especially important for people with neuromuscular disabilities.

摘要

基于脑电图的脑机接口(BCI)可以提供一种非肌肉的通讯方法。引入了一种基于 P300 的 BCI 刺激呈现的通用模型——“m 选 n”或 C(m(每个序列中的闪烁次数),n(每个项目中的闪烁次数))范式,这是先前报道的棋盘格范式(CBP)的通用扩展。C(m,n) 捕获了闪烁目标项目的所有可能(无约束)方式,然后应用约束来增强被注意的矩阵项目产生的 ERP。我们探索了 C(m,n)的一个 C(36,5)实例,称为“五闪烁范式”(FFP),并将其性能与 CBP 进行了比较。每个范式都有 8 名受试者进行测试,平衡了。使用 12 分钟的校准数据作为输入,进行逐步线性判别分析,得出用于在线分类的分类系数。FFP 的准确性始终很高(88%),CBP(90%);FFP 的信息传输率(63 bpm)明显高于 CBP(48 bpm)。C(m,n) 是一种新颖有效的组织刺激组的通用策略。通过以特定于受试者的方式调整参数,可以选择合适的“m”、“n”和特定的约束,从而改善呈现范式。对于患有神经肌肉障碍的人来说,这可能尤为重要。

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