Using the detectability index to predict P300 speller performance.
Using the detectability index to predict P300 speller performance.
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DOI：
10.1088/1741-2560/13/6/066007
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发表时间：
2016-12
影响因子：
4
通讯作者：
Throckmorton CS
中科院分区：
文献类型：
--
作者：
Mainsah BO;Collins LM;Throckmorton CS
P300拼写器是一种流行的脑机接口（BCI）系统，已被研究为具有严重神经肌肉限制的个体的潜在通信替代方案。为了达到可接受的通信精度水平，该系统需要在给定的信号条件下重复进行数据测量，以提高引起的大脑反应的信噪比。这些被用作控制信号的引发的大脑反应被嵌入在嘈杂的脑电图（EEG）数据中。目标和非目标EEG响应之间的可辨别性定义了用户对系统的性能。先前的P300拼写器模型已经被提出来估计给定一定量的数据收集的系统准确性。然而，该方法仅限于静态停止算法，即在固定数量的测量值上取平均值，以及行-列范例。一个通用的方法，也适用于动态停止算法和其他刺激范例是可取的。我们开发了一种新的基于概率模型的方法来预测BCI性能，其中性能函数可以通过解析或蒙特卡洛方法来推导。在这个框架内，我们引入了一个新的模型与贝叶斯动态停止（DS）算法的P300拼写，通过简化一个多假设的二元假设问题，使用似然比检验。在正态性假设下，贝叶斯算法的性能函数可以用可检测性指数参数化，可检测性指数是量化目标和非目标EEG响应之间的可辨别性的度量。模拟与合成和经验数据提供了初步验证的贝叶斯DS使用的可探测性指数估计性能的方法。从以前的在线研究的结果分析验证了所提出的方法。所提出的方法可以作为一个有用的工具，初步评估BCI的性能，而无需广泛的在线测试，以估计所需的数据量，以达到所需的准确性水平。
The P300 speller is a popular brain-computer interface (BCI) system that has been investigated as a potential communication alternative for individuals with severe neuromuscular limitations. To achieve acceptable accuracy levels for communication, the system requires repeated data measurements in a given signal condition to enhance the signal-to-noise ratio of elicited brain responses. These elicited brain responses, which are used as control signals, are embedded in noisy electroencephalography (EEG) data. The discriminability between target and non-target EEG responses defines a user’s performance with the system. A previous P300 speller model has been proposed to estimate system accuracy given a certain amount of data collection. However, the approach was limited to a static stopping algorithm, i.e. averaging over a fixed number of measurements, and the row-column paradigm. A generalized method that is also applicable to dynamic stopping algorithms and other stimulus paradigms is desirable. We developed a new probabilistic model-based approach to predicting BCI performance, where performance functions can be derived analytically or via Monte Carlo methods. Within this framework, we introduce a new model for the P300 speller with the Bayesian dynamic stopping (DS) algorithm, by simplifying a multi-hypothesis to a binary hypothesis problem using the likelihood ratio test. Under a normality assumption, the performance functions for the Bayesian algorithm can be parameterized with the detectability index, a measure which quantifies the discriminability between target and non-target EEG responses. Simulations with synthetic and empirical data provided initial verification of the proposed method of estimating performance with Bayesian DS using the detectability index. Analysis of results from previous online studies validated the proposed method. The proposed method could serve as a useful tool to initially asses BCI performance without extensive online testing, in order to estimate the amount of data required to achieve a desired accuracy level.
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影响因子：
3.7
作者：
Kübler A;Holz EM;Riccio A;Zickler C;Kaufmann T;Kleih SC;Staiger-Sälzer P;Desideri L;Hoogerwerf EJ;Mattia D
通讯作者：
Mattia D
DOI：
10.1109/tvlsi.2007.912191
发表时间：
2008-02-01
影响因子：
2.8
作者：
Nadarajah, Saralees;Kotz, Samuel
通讯作者：
Kotz, Samuel
DOI：
10.1109/tnsre.2004.841878
发表时间：
2005-03-01
影响因子：
4.9
作者：
Serby, H;Yom-Tov, E;Inbar, GF
通讯作者：
Inbar, GF
基于自适应P300的控制系统。
影响因子：
4
作者：
Jin J;Allison BZ;Sellers EW;Brunner C;Horki P;Wang X;Neuper C
通讯作者：
Neuper C
DOI：
10.1109/tnsre.2014.2321290
发表时间：
2014-07
期刊：
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
影响因子：
--
作者：
Mainsah BO;Colwell KA;Collins LM;Throckmorton CS
通讯作者：
Throckmorton CS