Automatic Classification of Auto-Correction Errors in Predictive Text Entry Based on EEG and Context Information Inproceedings

Felix Putze, Maik Schünemann, Tanja Schultz, Wolfgang Stuerzlinger


State-of-the-art auto-correction methods for predictive text entry systems work reasonably well, but can never be perfect due to the properties of human language. We present an approach for the automatic detection of erroneous auto-corrections based on brain activity and text-entry-based context features. We describe an experiment and a new system for the classification of human reactions to auto-correction errors. We show how auto-correction errors can be detected with an average accuracy of 85%.

Date of publication: Nov - 2017
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