Real-time detection of event-related brain activity Export

@article{citeulike:3368106, abstract = {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.}, author = {Schalk, G. and Leuthardt, E. and Brunner, P. and Ojemann, J. and Gerhardt, L. and Wolpaw, J.}, citeulike-article-id = {3368106}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.neuroimage.2008.07.037}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S1053811908008525}, day = {01}, doi = {10.1016/j.neuroimage.2008.07.037}, issn = {10538119}, journal = {NeuroImage}, keywords = {functional\_imaging, imaging, methods, mri, real-time}, month = {November}, number = {2}, pages = {245--249}, posted-at = {2009-05-05 18:07:56}, priority = {2}, title = {Real-time detection of event-related brain activity}, url = {http://dx.doi.org/10.1016/j.neuroimage.2008.07.037}, volume = {43}, year = {2008} }

TY - JOUR ID - citeulike:3368106 L3 - citeulike-article-id:3368106 N2 - 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. IS - 2 JF - NeuroImage SN - 10538119 EP - 249 TI - Real-time detection of event-related brain activity VL - 43 SP - 245 KW - functional_imaging KW - imaging KW - methods KW - mri KW - real-time AU - Schalk, G AU - Leuthardt, E AU - Brunner, P AU - Ojemann, J AU - Gerhardt, L AU - Wolpaw, J PY - 2008/11/01/ UR - http://dx.doi.org/10.1016/j.neuroimage.2008.07.037 ER -