A Simple Generative Model for Single-Trial EEG Classification
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Berlin Brain-Computer Interface-The HCI communication channel for discovery
International Journal of Human-Computer Studies
The Berlin brain-computer interface
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing
Computational Intelligence and Neuroscience - Special issue on academic software applications for electromagnetic brain mapping using MEG and EEG
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It is a well-known finding in human psychophysics that a subject's recognition of having committed a response error is accompagnied by specific EEG variations that can easily be observed in averaged event-related potentials (ERP). Here, we present a pattern recognition approach that allows for a robust single trial detection of this error potential from multichannel EEG signals. By designing classifiers that are capable of bounding false positives (FP), which would classify correct responses as errors, we achieve performance characteristics that make this method appealing for response-verification or even response-correction in EEG-based communication, i.e., brain-computer interfacing (BCI). This method provides a substantial improvement over the choice of a simple amplitude threshold criterion, as it had been utilized earlier for single trial detection of error potentials.