Real-time detection of event-related brain activity.
The complexity and inter-individual variation of brain signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex brain signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of brain function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize brain signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.
Schalk
Gerwin
G
Brain-Computer Interface R&D Program, Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA. schalk@wadsworth.org
Leuthardt
Eric C
EC
Brunner
Peter
P
Ojemann
Jeffrey G
JG
Gerhardt
Lester A
LA
Wolpaw
Jonathan R
JR
D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D003199:Computer Systems; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D005071:Evoked Potentials; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity