  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.
