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

• URL: http://arxiv.org/abs/2307.15672v1
• Date: Fri, 28 Jul 2023 17:04:06 GMT
• Title: Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity
• Authors: Navid Ziaei, Reza Saadatifard, Ali Yousefi, Behzad Nazari, Sydney S. Cash, Angelique C. Paulk
• Abstract summary: We propose a straightforward Bayesian time series classifier (BTsC) model that tackles 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 proposed solution can be applied to neural data recorded in various tasks, where there is a need for interpretable results.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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