Department of Informatics, Faculty of Engineering, Yamagata University, Japan.
Hotokukai Utsunomiya Hospital, Utsunomiya, Tochigi 320-8521, Japan.
Comput Methods Programs Biomed. 2016 Mar;125:26-36. doi: 10.1016/j.cmpb.2015.11.006. Epub 2015 Nov 22.
In 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.
We 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.
A 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.
We 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.
在临床检查和脑-机接口（BCI）研究中，短时间的脑电图（EEG）测量是理想的。事件相关电位（ERP）的使用依赖于估计精度和处理时间。我们测试了一种粒子滤波器，该滤波器使用大量粒子来构建概率分布。
我们构建了一个简单的 EEG 记录模型，该模型由三个组件组成：通过趋势模型近似的 ERP、通过自回归模型构建的背景波和噪声。我们基于均方误差（MSE）、P300 峰幅值和潜伏期评估了粒子滤波器的性能。然后，我们将滤波器与卡尔曼滤波器和传统的简单平均方法进行了比较。为了确认滤波器的功效，我们使用它来估计 P300 BCI 拼写器诱发的 ERP。
400 个粒子滤波器产生了最佳的 MSE。我们发现，当原始波形已经具有低信噪比（即 ERP 和背景 EEG 之间的功率比）时，滤波器的优点会增加。我们计算了在应用产生与传统平均相当的结果的粒子滤波器后所需的平均次数，并确定粒子滤波器可将测量时间最多减少 42.8%。在低 SNR 下，粒子滤波器在 MSE 和 P300 峰幅值和潜伏期方面均优于卡尔曼滤波器和传统平均。对于 P300 拼写器产生的 EEG 数据，我们能够使用滤波器获得与传统平均方法产生的平均值相比更稳定的 ERP 波形，而与平均次数无关。
我们证实，粒子滤波器在模拟低 SNR 时可以有效地减少所需的测量时间。此外，粒子滤波器可以对 P300 拼写器产生的 EEG 数据进行稳健的 ERP 估计。
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