Feedback Related Potentials for EEG-Based Typing Systems.

Paula Gonzalez-Navarro,Deniz Erdoğmuş,Basak Celik,Mohammad Moghadamfalahi,Murat Akcakaya,Melanie Fried-Oken

doi:10.3389/fnhum.2021.788258

Abstract

Error related potentials (ErrP), which are elicited in the EEG in response to a perceived error, have been used for error correction and adaption in the event related potential (ERP)-based brain computer interfaces designed for typing. In these typing interfaces, ERP evidence is collected in response to a sequence of stimuli presented usually in the visual form and the intended user stimulus is probabilistically inferred (stimulus with highest probability) and presented to the user as the decision. If the inferred stimulus is incorrect, ErrP is expected to be elicited in the EEG. Early approaches to use ErrP in the design of typing interfaces attempt to make hard decisions on the perceived error such that the perceived error is corrected and either the sequence of stimuli are repeated to obtain further ERP evidence, or without further repetition the stimulus with the second highest probability is presented to the user as the decision of the system. Moreover, none of the existing approaches use a language model to increase the performance of typing. In this work, unlike the existing approaches, we study the potential benefits of fusing feedback related potentials (FRP), a form of ErrP, with ERP and context information (language model, LM) in a Bayesian fashion to detect the user intent. We present experimental results based on data from 12 healthy participants using RSVP Keyboard™ to complete a copy-phrase-task. Three paradigms are compared: [P1] uses only ERP/LM Bayesian fusion; [P2] each RSVP sequence is appended with the top candidate in the alphabet according to posterior after ERP evidence fusion; corresponding FRP is then incorporated; and [P3] the top candidate is shown as a prospect to generate FRP evidence only if its posterior exceeds a threshold. Analyses indicate that ERP/LM/FRP evidence fusion during decision making yields significant speed-accuracy benefits for the user.

Highlights

Event related potentials (ERPs) are commonly employed in the design of non-invasive electroencephalography (EEG)-based brain computer interfaces (BCIs) to detect the user intent (Farwell and Donchin, 1988; Acqualagna et al, 2010; Orhan et al, 2012; Akcakaya et al, 2014; Moghadamfalahi et al, 2015)
We study the potential benefits of fusing feedback related potentials (FRP) with event related potential (ERP) and context information (LM) in a Bayesian fashion to detect the user intent
We study the potential benefits of fusing FRP, ERP, and language evidence using probabilistic generative models for a speller BCI

Summary

Introduction

Event related potentials (ERPs) are commonly employed in the design of non-invasive electroencephalography (EEG)-based brain computer interfaces (BCIs) to detect the user intent (Farwell and Donchin, 1988; Acqualagna et al, 2010; Orhan et al, 2012; Akcakaya et al, 2014; Moghadamfalahi et al, 2015). When the user realizes that the interface failed to properly recognize user’s intention, an ErrP signal is induced, which can characterized by two fronto-central positive peaks appearing 200 and 320 ms after the feedback; a fronto-central negativity near 250 ms and at last, broader frontocentral negative deflection about 450 ms after the feedback. These latencies can change depending on the experimental paradigm (Iturrate et al, 2013). It has been studied that the positive components of the ErrP reflects conscious error processing or post-error adjustment of response strategies (Falkenstein et al, 2000)

Methods

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Conclusion