A novel 9-class auditory ERP paradigm driving a predictive text entry system

Brain-Computer Interfaces (BCIs) based on Event Related Potentials (ERPs) strive for offering communication pathways which are independent of muscle activity. While most visual ERP-based BCI paradigms require good control of the user's gaze direction, auditory BCI paradigms overcome this restriction. The present work proposes a novel approach using Auditory Evoked Potentials (AEP) for the example of a multiclass text spelling application. To control the ERP speller, BCI users focus their attention to two-dimensional auditory stimuli that vary in both, pitch (high/medium/low) and direction (left/middle/right) and that are presented via headphones. The resulting nine different control signals are exploited to drive a predictive text entry system. It enables the user to spell a letter by a single 9-class decision plus two additional decisions to confirm a spelled word.This paradigm - called PASS2D - was investigated in an online study with twelve healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits per minute) which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like late-stage ALS patients