The N400 for Brain Computer Interfacing: complexities and opportunities

TL;DR

This paper reviews the potential of the N400 event-related potential for brain-computer interfaces, highlighting its applications, challenges like signal variability, and opportunities for future research in semantic processing.

Contribution

It provides a comprehensive overview of N400-based BCI applications, discusses current limitations, and identifies open questions and future research directions.

Findings

N400 can be exploited for BCI in face recognition, language detection, and mental state probing.

Signal-to-noise ratio is a major limiting factor in N400-based BCI performance.

Significant variability in N400 signals across subjects affects BCI reliability.

Abstract

The N400 is an Event Related Potential that is evoked in response to conceptually meaningful stimuli. It is for instance more negative in response to incongruent than congruent words in a sentence, and more negative for unrelated than related words following a prime word. This sensitivity to semantic content of a stimulus in relation to the mental context of an individual makes it a signal of interest for Brain Computer Interfaces. Given this potential it is notable that the BCI literature exploiting the N400 is limited. We identify three existing application areas: (1) exploiting the semantic processing of faces to enhance matrix speller performance, (2) detecting language processing in patients with Disorders of Consciousness, and (3) using semantic stimuli to probe what is on a user's mind. Drawing on studies from these application areas, we illustrate that the N400 can successfully be exploited for BCI purposes, but that the signal-to-noise ratio is a limiting factor, with signal strength also varying strongly across subjects. Furthermore, we put findings in context of the general N400 literature, noting open questions and identifying opportunities for further research.