Robust estimation of event-related potentials via particle filter
Robust estimation of event-related potentials via particle filter
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DOI：
10.1016/j.cmpb.2015.11.006
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发表时间：
2016-03
影响因子：
6.1
通讯作者：
T. Fukami;Jun Watanabe;F. Ishikawa
中科院分区：
文献类型：
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作者：
T. Fukami;Jun Watanabe;F. Ishikawa
背景与目的在临床检查和脑-机接口（BCI）研究中，要求脑电（EEG）测量时间短。事件相关电位（ERP）的使用依赖于估计精度和处理时间。我们测试了粒子滤波器，使用大量的粒子来构建一个probability distribution.MethodsWe构建了一个简单的模型，用于记录EEG，包括三个组成部分：ERP近似通过一个趋势模型，背景波构建通过自回归模型，和噪声。我们根据均方误差（MSE）、P300峰值幅度和延迟评估了粒子滤波器的性能。然后，我们比较了我们的滤波器与卡尔曼滤波器和一个传统的简单的平均方法。为了确认过滤器的功效，我们用它来估计ERP引起的P300 BCI speller.ResultsA 400粒子过滤器产生最好的MSE。我们发现，当原始波形已经具有低信噪比（SNR）（即，ERP和背景EEG之间的功率比）。我们计算了应用粒子滤波器后所需的平均值，该粒子滤波器产生的结果与传统平均值相当，并确定粒子滤波器在测量时间上最多减少了42.8%。在MSE和P300峰值幅度和延迟方面，粒子滤波器在低SNR下的表现优于卡尔曼滤波器和常规平均。对于P300拼写器产生的EEG数据，我们能够使用我们的过滤器，以获得ERP波形是稳定的，与平均值相比，由传统的平均方法，无论平均量。ConclusionsWe证实，粒子滤波器是有效的，减少在模拟过程中所需的测量时间与低信噪比。此外，粒子滤波器可以对通过P300拼写器产生的EEG数据执行鲁棒的ERP估计。
Background and objectiveIn clinical examinations and brain–computer interface (BCI) research, a short electroencephalogram (EEG) measurement time is ideal. The use of event-related potentials (ERPs) relies on both estimation accuracy and processing time. We tested a particle filter that uses a large number of particles to construct a probability distribution.MethodsWe constructed a simple model for recording EEG comprising three components: ERPs approximated via a trend model, background waves constructed via an autoregressive model, and noise. We evaluated the performance of the particle filter based on mean squared error (MSE), P300 peak amplitude, and latency. We then compared our filter with the Kalman filter and a conventional simple averaging method. To confirm the efficacy of the filter, we used it to estimate ERP elicited by a P300 BCI speller.ResultsA 400-particle filter produced the best MSE. We found that the merit of the filter increased when the original waveform already had a low signal-to-noise ratio (SNR) (i.e., the power ratio between ERP and background EEG). We calculated the amount of averaging necessary after applying a particle filter that produced a result equivalent to that associated with conventional averaging, and determined that the particle filter yielded a maximum 42.8% reduction in measurement time. The particle filter performed better than both the Kalman filter and conventional averaging for a low SNR in terms of both MSE and P300 peak amplitude and latency. For EEG data produced by the P300 speller, we were able to use our filter to obtain ERP waveforms that were stable compared with averages produced by a conventional averaging method, irrespective of the amount of averaging.ConclusionsWe confirmed that particle filters are efficacious in reducing the measurement time required during simulations with a low SNR. Additionally, particle filters can perform robust ERP estimation for EEG data produced via a P300 speller.