Post-Training Quantization in Brain-Computer Interfaces based on Event-Related Potential Detection

TL;DR

This paper evaluates post-training quantization for brain-computer interface models, demonstrating significant size reduction with minimal accuracy loss in event-related potential detection.

Contribution

It provides the first assessment of PTQ's impact on BCI models, showing effective size reduction without substantial accuracy degradation.

Findings

Model size reduced by approximately 15 times.

Area under ROC curve decreases slightly from 0.861 to 0.825.

PTQ maintains comparable accuracy while optimizing model size.

Abstract

Post-training quantization (PTQ) is a technique used to optimize and reduce the memory footprint and computational requirements of machine learning models. It has been used primarily for neural networks. For Brain-Computer Interfaces (BCI) that are fully portable and usable in various situations, it is necessary to provide approaches that are lightweight for storage and computation. In this paper, we propose the evaluation of post-training quantization on state-of-the-art approaches in brain-computer interfaces and assess their impact on accuracy. We evaluate the performance of the single-trial detection of event-related potentials representing one major BCI paradigm. The area under the receiver operating characteristic curve drops from 0.861 to 0.825 with PTQ when applied on both spatial filters and the classifier, while reducing the size of the model by about $\times$ 15. The results support the conclusion that PTQ can substantially reduce the memory footprint of the models while keeping roughly the same level of accuracy.