11. The method of claim 1 , wherein the EEG signals are captured in a series of overlapping global windows each in sync with a known presentation of a stimulus and each global window is subdivided into a plurality of overlapping time windows up to a threshold time window, for each said global window the extracted features for a first said time window being presented to a first said spatial classifier and so forth to span the threshold time window and no further to generate said plurality of first level outputs.
1. A method of coupling human neural response with computer pattern analysis for enhanced single-event detection of significant non-stationary brain responses triggered upon occurrence of a task-relevant stimulus, comprising: measuring EEG signals associated with a person's brain activity from a plurality of electrodes placed on the person's scalp; subdividing the EEG signals into a plurality of different time windows; extracting features from the EEG signals for each said different time window; presenting the extracted features to a respective plurality of computer-implemented spatial classifiers trained to detect spatial patterns of said extracted features during different time windows from the occurrence of the task-relevant stimulus and to generate first level outputs indicative of the occurrence or absence of a significant brain response; and presenting the spatial classifiers' first level outputs to a temporal classifier, said temporal classifier configured to implement feature-level fusion or decision-level fusion to detect temporal patterns across the different time windows relating to the evolution of the non-stationary brain response to task-relevant stimulus and to generate a second level output indicative of the occurrence or absence of the significant non-stationary brain response.