BACKGROUND The accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) in measuring neural evoked responses (ERs) is challenged by overlapping neural sources. This lack of accuracy is a severe limitation to the application of ERs to clinical diagnostics. NEW METHOD We here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural assemblies, and a spike density component analysis (SCA) method for isolating specific neural sources. The method is tested in three empirical studies with 564 cases of ERs to auditory stimuli from 94 humans, each measured with 60 EEG electrodes and 306 MEG sensors, and a simulation study with 12,300 ERs. RESULTS The first study showed that neural sources (but not non-encephalic artifacts) in individual averaged MEG/EEG waveforms are modelled accurately with temporal Gaussian probability density functions (median 99.7 %-99.9 % variance explained). The following studies confirmed that SCA can isolate an ER, namely the mismatch negativity (MMN), and that SCA reveals inter-individual variation in MMN amplitude. Finally, SCA reduced errors by suppressing interfering sources in simulated cases. COMPARISON WITH EXISTING METHODS We found that gamma and sine functions fail to adequately describe individual MEG/EEG waveforms. Also, we observed that principal component analysis (PCA) and independent component analysis (ICA) does not consistently suppress interference from overlapping brain activity in neither empirical nor simulated cases. CONCLUSIONS These findings suggest that the overlapping neural sources in single-subject or patient data can be more accurately separated by applying SCA in comparison to PCA and ICA.

中文翻译：

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将随机峰值训练理论应用于高精度人类MEG / EEG。

背景技术脑电图（EEG）和磁脑图（MEG）在测量神经诱发反应（ER）中的准确性受到重叠神经源的挑战。准确性的缺乏严重限制了ER在临床诊断中的应用。新方法我们在这里介绍用于描述神经装配中大规模尖峰活动的随机神经元尖峰定时概率密度理论，以及用于分离特定神经源的尖峰密度分量分析（SCA）方法。该方法已在三项实证研究中进行了测试，其中有564例ER对94位人类的听觉刺激，分别用60个EEG电极和306个MEG传感器进行了测量，并在12300个ER中进行了模拟研究。结果首次研究表明，使用时态高斯概率密度函数（解释了中位数为99.7％-99.9％）对单个平均MEG / EEG波形中的神经源（而非非脑伪影）进行了精确建模。以下研究证实，SCA可以分离出一个ER，即失配负性（MMN），并且SCA揭示了MMN振幅的个体差异。最后，SCA通过在模拟情况下抑制干扰源来减少错误。与现有方法的比较我们发现，伽马和正弦函数无法充分描述单个MEG / EEG波形。此外，我们观察到，无论是经验案例还是模拟案例，主成分分析（PCA）和独立成分分析（ICA）都不能始终如一地抑制来自重叠大脑活动的干扰。