Abstract

Event-related potentials (ERPs) are often used in brain-computer interfaces (BCIs) for communication or system control for enhancing or regaining control for motor-disabled persons. Especially results from single-trial EEG classification approaches for BCIs support correlations between single-trial ERP detection performance and ERP expression. Hence, BCIs can be considered as a paradigm shift contributing to new methods with strong influence on both neuroscience and clinical applications. Here, we investigate the relevance of the choice of training data and classifier transfer for the interpretability of results from single-trial ERP detection. In our experiments, subjects performed a visual-motor oddball task with motor-task relevant infrequent (targets), motor-task irrelevant infrequent (deviants), and motor-task irrelevant frequent (standards) stimuli. Under dual-task condition, a secondary senso-motor task was performed, compared to the simple-task condition. For evaluation, average ERP analysis and single-trial detection analysis with different numbers of electrodes were performed. Further, classifier transfer was investigated between simple and dual task. Parietal positive ERPs evoked by target stimuli (but not by deviants) were expressed stronger under dual-task condition, which is discussed as an increase of task emphasis and brain processes involved in task coordination and change of task set. Highest classification performance was found for targets irrespective whether all 62, 6 or 2 parietal electrodes were used. Further, higher detection performance of targets compared to standards was achieved under dual-task compared to simple-task condition in case of training on data from 2 parietal electrodes corresponding to results of ERP average analysis. Classifier transfer between tasks improves classification performance in case that training took place on more varying examples (from dual task). In summary, we showed that P300 and overlaying parietal positive ERPs can successfully be detected while subjects are performing additional ongoing motor activity. This supports single-trial detection of ERPs evoked by target events to, e.g., infer a patient's attentional state during therapeutic intervention.

Highlights

  • In many brain-computer interface (BCI) applications (Vidal, 1973; Wolpaw et al, 2002) the detection of the well-known eventrelated potential (ERP) P300 is used for communication (Farwell and Donchin, 1988; Riccio et al, 2011) or control of computer programs and machines, including complex virtual environments such as a virtual apartment (Bayliss, 2003) or robots (Kim et al, 2014), which can be used with the goal of compensating for motor actions that a patient cannot carry out

  • Results of this study are highly relevant with respect to the usability of single-trial classification performance as a tool for the investigation of brain processes

  • We showed that differences in single-trial classification performance can well be explained by the strength of expression in average ERP components and the other way around

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Summary

Introduction

In many brain-computer interface (BCI) applications (Vidal, 1973; Wolpaw et al, 2002) the detection of the well-known eventrelated potential (ERP) P300 is used for communication (Farwell and Donchin, 1988; Riccio et al, 2011) or control of computer programs and machines, including complex virtual environments such as a virtual apartment (Bayliss, 2003) or robots (Kim et al, 2014), which can be used with the goal of compensating for motor actions that a patient cannot carry out. It was not necessary to add an extra task which has usually been performed to investigate the effect of different parameters on the workload such as in previous studies (Isreal et al, 1980; Allison and Polich, 2008; Käthner et al, 2014) This indicates that single-trial ERP classification can be used for control purposes in BCIs and as a measure for the strength in expression of ERPs in single trial to infer e.g., on the workload of a user online. This is a good example that BCIs can contribute to a paradigm shift in EEG analysis

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