Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity

TL;DR

This paper introduces a Bayesian time series classifier that effectively decodes visual stimuli from intracranial neural data, achieving high accuracy and interpretability, and outperforming existing machine learning methods.

Contribution

The study presents a simple, interpretable Bayesian classifier that improves decoding accuracy of neural responses to visual stimuli over state-of-the-art techniques.

Findings

Achieved 75.55% accuracy in decoding colors from neural data.

Outperformed existing machine learning methods by approximately 3%.

Provided interpretable results for neural activity analysis.

Abstract

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.