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## Error-aware BCIs

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Signal Processing to Drive Human-Computer Interaction: EEG and eye-controlled interfaces — Recommend this title to your library

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The ability of recognizing and correcting the erroneous actions is an integral part of human nature. Plenty of neuroscientific studies have been investigating the ability of human brain to recognize errors. The distinct neuronal responses that are produced by the human brain during the perception of an erroneous action are referred to as error-related potentials (ErrPs). Although research in brain-computer interfaces (BCIs) has managed to achieve significant improvement in terms of detecting the users'intentions over the last years, in a real-world setting, the interpretation of brain commands still remains an error-prone procedure leading to inaccurate interactions. Even for multimodal interaction schemes, the attained performance is far from optimal. As a means to overcome these debilities, and apart from developing more sophisticated machine-learning techniques or adding further modalities, scientists have also exploited the users' ability to perceive errors. During the rapid growth of the BCI/Human-Machine Interaction (HMI) technology over the last years, ErrPs have been used widely in order to enhance several existing BCI applications serving as a passive correction mechanism towards a more user-friendly environment. The principal idea is that a BCI system may incorporate, as feedback, the user's judgement about its function and use this feedback to correct its current output. In this chapter, we discuss the potentials and applications of ErrPs into developing hybrid BCI systems that emphasize in reliability and user experience by introducing the so-called error awareness.

Chapter Contents:

• 11.1 Introduction to error-related potentials
• 11.2 Spatial filtering
• 11.2.1 Subspace learning
• 11.2.1.1 Unsupervised collaborative representation projection
• 11.2.1.2 Supervised collaborative representation
• 11.2.2 Increasing signal-to-noise ratio
• 11.2.2.1 Preliminaries
• 11.2.2.2 Discriminant spatial filters
• 11.3 Measuring the efficiency–ICRT
• 11.4 An error-aware SSVEP-based BCI
• 11.4.1 Experimental protocol
• 11.4.2 Dataset
• 11.4.3 Implementation details–preprocessing
• 11.4.4 Results
• 11.4.4.1 Visual representation
• 11.4.4.2 Spatial filtering evaluation
• 11.5 An error-aware gaze-based keyboard
• 11.5.1 Methodology
• 11.5.2 Typing task and physiological recordings
• 11.5.3 Pragmatic typing protocol
• 11.5.4 Data analysis
• 11.5.5 System adjustment and evaluation
• 11.5.6 Results
• 11.5.6.1 Physiological findings
• 11.5.6.2 SVM classification for predicting typesetting errors from physiological activity
• 11.5.6.3 Incorporating the SVM classifier(s) in the gaze-based keyboard
• 11.5.6.4 Simulated implementation of the EDS
• 11.6 Summary
• References

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