Department of Psychology, University of Konstanz, Konstanz, Germany.
Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
PLoS Comput Biol. 2018 Mar 12;14(3):e1005938. doi: 10.1371/journal.pcbi.1005938. eCollection 2018 Mar.
Single-trial analyses have the potential to uncover meaningful brain dynamics that are obscured when averaging across trials. However, low signal-to-noise ratio (SNR) can impede the use of single-trial analyses and decoding methods. In this study, we investigate the applicability of a single-trial approach to decode stimulus modality from magnetoencephalographic (MEG) high frequency activity. In order to classify the auditory versus visual presentation of words, we combine beamformer source reconstruction with the random forest classification method. To enable group level inference, the classification is embedded in an across-subjects framework. We show that single-trial gamma SNR allows for good classification performance (accuracy across subjects: 66.44%). This implies that the characteristics of high frequency activity have a high consistency across trials and subjects. The random forest classifier assigned informational value to activity in both auditory and visual cortex with high spatial specificity. Across time, gamma power was most informative during stimulus presentation. Among all frequency bands, the 75 Hz to 95 Hz band was the most informative frequency band in visual as well as in auditory areas. Especially in visual areas, a broad range of gamma frequencies (55 Hz to 125 Hz) contributed to the successful classification. Thus, we demonstrate the feasibility of single-trial approaches for decoding the stimulus modality across subjects from high frequency activity and describe the discriminative gamma activity in time, frequency, and space.
单试次分析有可能揭示出在平均试次时被掩盖的有意义的大脑动力学。然而，低信噪比（SNR）可能会阻碍单试次分析和解码方法的使用。在这项研究中，我们研究了从脑磁图（MEG）高频活动中解码刺激模式的单试次方法的适用性。为了对单词的听觉和视觉呈现进行分类，我们结合了束形成源重建和随机森林分类方法。为了进行组水平推断，分类被嵌入到跨被试者框架中。我们表明，单试次伽马 SNR 允许良好的分类性能（跨被试者的准确性：66.44%）。这意味着高频活动的特征在试次和被试者之间具有高度的一致性。随机森林分类器以高空间特异性将信息价值分配给听觉和视觉皮层的活动。随着时间的推移，伽马功率在刺激呈现期间最具信息量。在所有频带中，75 Hz 至 95 Hz 频带在视觉和听觉区域都是最具信息量的频带。特别是在视觉区域，广泛的伽马频率（55 Hz 至 125 Hz）有助于成功分类。因此，我们证明了从高频活动跨被试者解码刺激模式的单试次方法的可行性，并描述了时间、频率和空间上的判别性伽马活动。
