Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity
This provides an interpretable tool for neuroscientists to study neural encoding in clinical and basic research, though it is incremental as it builds on existing methods with a modest performance gain.
The authors tackled the problem of decoding visual stimuli from intracranial neural activity by developing a Bayesian time-series classifier, achieving an average accuracy of 75.55% on a dataset from 4 patients, which improved upon state-of-the-art methods by about 3.0%.
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.