Magnetoencephalography (MEG) is a persuasive tool to study the human brain in physiology and psychology. It can be employed to obtain the inference of change between the external environment and the internal psychology, which requires us to recognize different single trial event-related magnetic fields (ERFs) originated from different functional areas of the brain. Current recognition methods for the single trial data are mainly used for event-related potentials (ERPs) in the electroencephalography (EEG). Although the MEG shares the same signal sources with the EEG, much less interference from the other brain tissues may give the MEG an edge in recognition of the ERFs. In this work, we propose a new recognition method for the single trial auditory evoked magnetic fields (AEFs) through enhancing the signal. We find that the signal strength of the single trial AEFs is concentrated in the primary auditory cortex of the temporal lobe, which can be clearly displayed in the 2D images. These 2D images are then recognized by an artificial neural network (ANN) with 100% accuracy, which realizes the automatic recognition for the single trial AEFs. The method not only may be combined with the source estimation algorithm to improve its accuracy but also paves the way for the implementation of the brain-computer interface (BCI) with the MEG.

中文翻译：

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听觉诱发磁场的一种新的识别方法

脑磁图（MEG）是一种有说服力的工具，可以研究人脑的生理学和心理学。它可以用来获得外部环境和内部心理之间变化的推断，这要求我们认识到源自大脑不同功能区域的不同的与单一试验事件相关的磁场（ERF）。当前用于单个试验数据的识别方法主要用于脑电图（EEG）中的事件相关电位（ERP）。尽管MEG与EEG共享相同的信号源，但是来自其他脑组织的干扰要少得多，这可能会使MEG在识别ERF方面具有优势。在这项工作中，我们提出了一种通过增强信号来识别单次尝试听觉诱发磁场（AEF）的新方法。我们发现，单个试验AEF的信号强度集中在颞叶的主要听觉皮层中，可以在2D图像中清晰显示。然后，这些2D图像将由人工神经网络（ANN）进行100％准确的识别，从而实现对单个试验AEF的自动识别。该方法不仅可以与源估计算法相结合以提高其准确性，而且还为通过MEG实现脑机接口（BCI）铺平了道路。