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Online detection of error-related potentials boosts the performance of mental typewriters

DOI: 10.1186/1471-2202-13-19

Keywords: Brain-computer interface, Electroencephalography, ERP-Speller, Error-related potentials, Information transfer rate

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Abstract:

In our study with eleven participants, an ErrP detection mechanism was implemented in an electroencephalography (EEG) based gaze-independent visual speller.Single-trial ErrPs were detected with a mean accuracy of 89.1% (AUC 0.90). The spelling speed was increased on average by 49.0% using ErrP detection. The improvement in spelling speed due to error detection was largest for participants with low spelling accuracy.The performance of BCIs can be increased by using an automatic error detection mechanism. The benefit for patients with motor disorders is potentially high since they often have rather low spelling accuracies compared to healthy people.Brain-computer interfaces (BCIs) establish a direct communication link between the human brain and an electronic device [1,2]. The intent of the user is 'decoded' from her/his brain signals, e.g. from electroencephalography (EEG) or magnetoencephalography (MEG), and transformed into control commands for an external device. A great amount of research focuses on restoring sensory-motor functionality or communication ability in people who suffer from motor disorders, such as amyotrophic lateral sclerosis (ALS) [3]. For ALS patients, BCI is a promising technology [4], because it can restore their ability to communicate wishes and needs and to interact with their environment, e.g. by controlling a spelling application [5,6], a PC-cursor [7], or a wheelchair [8].In EEG-based BCIs, many approaches capitalize on event-related potentials (ERPs) that arise as a response to sensory stimulation. An often targeted ERP component is the P300, a positive deflection at central and parietal electrode sites about 300 ms after the presentation of a stimulus that the user is attending to. The P300 and other ERP components have been successfully used as features in BCI spelling applications in order to identify the characters the user intends to write. The classic spelling application is the so-called P300-speller introduced by Farwell and Donch

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