title: A Novel 9-Class Auditory ERP Paradigm Driving a Predictive Text Entry System.
abstract: 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 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 nine-class decision plus two additional decisions to confirm a spelled word. This paradigm - called PASS2D - was investigated in an online study with 12 healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits/min) which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like people with amyotrophic lateral sclerosis disease in a late stage.
authors: Machine Learning Laboratory, Berlin Institute of Technology Berlin, Germany.
authors: Johannes
authors: J
authors: Höhne
authors: Martijn
authors: M
authors: Schreuder
authors: Benjamin
authors: B
authors: Blankertz
authors: Michael
authors: M
authors: Tangermann