Department of Electrical Engineering, Federal University of Viçosa, Viçosa, MG, Brazil.
Center for Exact and Technological Sciences, Federal University of Recôncavo da Bahia, Cruz das Almas, BA, Brazil.
Comput Methods Programs Biomed. 2018 Aug;162:87-91. doi: 10.1016/j.cmpb.2018.05.010. Epub 2018 May 7.
The local spectral F-test (SFT) corresponds to a statistical way of assessing whether the spectrum of a signal is flat in the vicinity of a specific frequency. The power of this univariate test (comparing one frequency component against its neighbours using only one signal) depends on the signal-to-noise ratio, which is fixed in the case of electroencephalogram (EEG) analysis. However, this limitation could be overcome by considering more signals in the analysis. Thus, this work presents an alternative multivariate approach for estimating the local SFT.
Probabilities of detection and false alarm studies were performed for this new detector using Monte Carlo simulations and theoretically whenever possible. The application was illustrated in recorded EEG data collected during photic stimulation.
The results showed that it is worth using more channels if available, since the probability of detecting a response tends to increase with increasing number of signals. In the application to the EEG during photic stimulation, the best results were obtained by using N > 2 signals (around 30% more accurate when compared with the univariate case. The false positive levels were maintained below 5%).
Consequently, it is conjectured that it is always better to apply the proposed method if more than one EEG signal with the same signal-to-noise ratio (SNR) is available. For the case where the SNRs are different, a guideline has been given to improve the detection.
局部谱 F 检验（SFT）是一种评估信号在特定频率附近的频谱是否平坦的统计方法。这种单变量检验（仅使用一个信号比较一个频率分量与其相邻频率分量）的功效取决于信噪比，而在脑电图（EEG）分析中，信噪比是固定的。然而，通过在分析中考虑更多的信号，可以克服这一局限性。因此，本研究提出了一种估计局部 SFT 的替代多元方法。
使用蒙特卡罗模拟和理论上尽可能的方法对这种新的检测器进行了检测概率和误报概率研究。该应用在光刺激期间记录的 EEG 数据中进行了说明。
结果表明，如果有更多的通道可用，那么使用更多的通道是值得的，因为检测到响应的概率随着信号数量的增加而增加。在光刺激期间的 EEG 应用中，通过使用 N>2 个信号（与单变量情况相比，准确率提高约 30%）可以获得最佳结果。假阳性水平保持在 5%以下。
因此，如果有相同信噪比（SNR）的多个 EEG 信号可用，那么可以推测出总是最好应用所提出的方法。对于 SNR 不同的情况，给出了一个提高检测性能的指导方针。
