Error-related Potential Variability: Exploring the Effects on Classification and Transferability

TL;DR

This paper investigates how variability in error-related brain signals affects the accuracy and transferability of classifiers in brain-computer interfaces, focusing on different cognitive states and task types.

Contribution

It provides an empirical analysis of ErrP classifier transferability across datasets with varying cognitive and task-related factors, highlighting challenges and insights.

Findings

ErrP variability impacts classifier transferability

Transferability differs between observational and interactive ErrPs

Cognitive factors influence ErrP classification performance

Abstract

Brain-Computer Interfaces (BCI) have allowed for direct communication from the brain to external applications for the automatic detection of cognitive processes such as error recognition. Error-related potentials (ErrPs) are a particular brain signal elicited when one commits or observes an erroneous event. However, due to the noisy properties of the brain and recording devices, ErrPs vary from instance to instance as they are combined with an assortment of other brain signals, biological noise, and external noise, making the classification of ErrPs a non-trivial problem. Recent works have revealed particular cognitive processes such as awareness, embodiment, and predictability that contribute to ErrP variations. In this paper, we explore the performance of classifier transferability when trained on different ErrP variation datasets generated by varying the levels of awareness and embodiment for a given task. In particular, we look at transference between observational and interactive ErrP categories when elicited by similar and differing tasks. Our empirical results provide an exploratory analysis into the ErrP transferability problem from a data perspective.