The Bayesian brain framework has been proposed to explain how the brain processes and interprets sensory information. Magnetoencephalography (MEG) and electroencephalography (EEG) are two neuroimaging techniques commonly used with decoding models to study neural responses to auditory, visual and somatosensory stimuli. Our study aims to investigate neural responses to auditory stimuli using MEG data and to determine which temporal components in MEG data are sufficient for decoding surprise based on Bayesian models. MEG data acquired from 18 subjects during an auditory binary oddball task was used. The data were pre-processed, and features were selected from different time windows. Five Bayesian learning models were applied to the experimental task stimuli, and each single trial's surprise value was calculated. The relationship between the extracted features in MEG data and the surprise regressors was investigated using linear regression and 5-fold cross-validation. The results showed that the middle and late components of the MEG evoked potentials were significantly more informative than the early components. The results indicated that the Dirichlet-Categorical model outperformed the other model's decoding performance as demonstrated by higher R-squared values and lower MSE and BIC values. The findings of this study provide evidence for the existence of a neural network that generates surprise in the human brain and highlight the importance of the middle and late components of the MEG evoked potentials for decoding the surprise value of auditory stimuli.

中文翻译：

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使用贝叶斯模型解码成人脑磁图数据中的听觉惊喜

贝叶斯大脑框架已被提出来解释大脑如何处理和解释感官信息。脑磁图 (MEG) 和脑电图 (EEG) 是两种常与解码模型一起使用的神经影像技术，用于研究对听觉、视觉和体感刺激的神经反应。我们的研究旨在使用 MEG 数据研究对听觉刺激的神经反应，并确定 MEG 数据中的哪些时间成分足以基于贝叶斯模型解码惊喜。使用了在听觉二元奇怪任务中从 18 名受试者获得的 MEG 数据。对数据进行预处理，并从不同时间窗口选择特征。将五种贝叶斯学习模型应用于实验任务刺激，并计算每个试验的惊喜值。使用线性回归和 5 折交叉验证研究了 MEG 数据中提取的特征与意外回归量之间的关系。结果表明，MEG 诱发电位的中、晚期成分明显比早期成分含有更多信息。结果表明，狄利克雷分类模型的解码性能优于其他模型，如较高的 R 平方值和较低的 MSE 和 BIC 值所示。这项研究的结果为人类大脑中产生惊喜的神经网络的存在提供了证据，并强调了MEG诱发电位的中后期成分对于解码听觉刺激的惊喜值的重要性。