title: EEG-based single-trial detection of errors from multiple error-related brain activity.
abstract: A key ability of the human brain is to monitor erroneous events and adjust behaviors accordingly. Electrophysiological and neuroimaging studies have demonstrated different brain activities related to errors. Meanwhile, the recognition of error-related brain activity as one aspect of performance monitoring has been reported for potential applications in clinical neuroscience and brain-machine interface, where single-trial analysis and classification would provide novel insights on dynamic brain responses to errors. However, procedures of selecting features, as well as procedures of single-trial classification, are not fully investigated for optimal performance. In the present study, we investigated the performance of different configurations of feature extractions in both temporal and frequency domains, for discriminating response errors in a color-word matching Stroop task. Motivated by our previous investigations, we evaluated both temporal and frequency features with component signals, which were obtained from EEG signals via an independent component analysis (ICA). Five component signals (independent components, ICs), originated from the frontal, motor, parietal, and occipital areas, were included in detecting error-related brain activity from single-trial EEG data. The results showed that better performance can be achieved by optimizing time window and frequency range of selected features, sampling scheme of feature-related data, and training of classifiers. However, a simple combination of features from multiple component signals can only slightly improve the detection performance of errors in single-trial data as compared to the frontal IC only. More importantly, it is indicated that four ICs other than the frontal one also carry similar discriminative information about errors in both temporal and frequency domains. The fact suggests flexible means in detecting errors from EEG beyond the frontal brain areas, which might be very valuable in practical applications such that the frontal area is not accessible.
authors.lastname: Guofa Shou
authors.lastname: Lei Ding
mesh_terms: D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D006801:Humans; D015203:Reproducibility of Results