Understanding how external stimuli are encoded in distributed neural activity is of significant interest in clinical and basic neuroscience. To address this need, it is essential to develop analytical tools capable of handling limited data and the intrinsic stochasticity present in neural data. In this study, we propose a straightforward Bayesian time series classifier (BTsC) model that tackles these challenges whilst maintaining a high level of interpretability. We demonstrate the classification capabilities of this approach by utilizing neural data to decode colors in a visual task. The model exhibits consistent and reliable average performance of 75.55% on 4 patients' dataset, improving upon state-of-the-art machine learning techniques by about 3.0 percent. In addition to its high classification accuracy, the proposed BTsC model provides interpretable results, making the technique a valuable tool to study neural activity in various tasks and categories. The proposed solution can be applied to neural data recorded in various tasks, where there is a need for interpretable results and accurate classification accuracy.

翻译：理解外部刺激如何在分布式神经活动中编码，在临床和基础神经科学领域具有重要意义。为满足这一需求，必须开发能够处理有限数据以及神经数据固有随机性的分析工具。本研究提出了一种简洁的贝叶斯时间序列分类器（BTsC）模型，在保持高度可解释性的同时应对上述挑战。我们通过利用神经数据解码视觉任务中的颜色，展示了该方法的分类能力。该模型在4名患者的数据集上实现了75.55%的一致且可靠的平均性能，比现有最优机器学习技术提升约3.0%。除高分类准确率外，所提出的BTsC模型还提供可解释的结果，使其成为研究多种任务和分类中神经活动的宝贵工具。该解决方案可应用于不同任务中记录的神经数据，尤其适用于需要可解释结果和精确分类准确率的场景。