A major shift in scientific perspective on the nature and use of electrophysiological brain data is now ongoing a shift from measurement and visualization of individual channel signals (in the 'recording channel space') to visualizing and interpreting the data directly within a suitable inverse model representing activity reaching the electrodes by volume conduction from a set of effective data sources in native 'brain source space'. An equivalent shift, via the development and exploitation of an appropriate inverse imaging model, made possible the phenomenon of structural and functional magnetic resonance imaging (fMRI). While the electrophysiological inverse problem is still difficult, dramatic progress has been and is being made through combined use of multimodal imaging and modern statistical signal processing methods. Recovering the considerable degree of spatial source resolution available in high-density scalp electroencephalographic (EEG) and other electrophysiological data, while retaining its natural advantage over other functional imaging methods in temporal resolution, has begun to yield a steady stream of new information about patterns of distributed brain processing supporting human behavior and experience. Relative to other brain imaging modalities, EEG has substantial and increasing cost and mobility advantages, making promotion of new EEG methods for source space analysis of increasing interest and importance for brain and health research. However, applying new source signal and signal processing models to electrophysiological data is complex and increasingly involves application of modern mathematical methods whose details are not within the training of most health research professionals. The EEGLAB signal processing environment, an open source software project of the Swartz Center for Computational Neuroscience (SCCN) at the University of California San Diego, began as a set of EEG data analysis running on MATLAB (The Mathworks, Inc.) released on the World Wide Web in 1997. EEGLAB was first released from SCCN in 2001. Now, more than ten years later, the EEGLAB reference paper (Delorme &Makeig, 2004) has over 2,350 Google scholar citations (increasing at above 1 per day), the opt-in EEGLAB discussion email list links over 5,000 researchers, the news list over 9,000, and a recent survey of 687 researcher respondents reports EEGLAB to be the software environment most widely used for electrophysiological data analysis worldwide. EEGLAB is thus now a de facto standard supporting a wide range of EEG and other electrophysiological research studies and teaching labs. At least 35 EEGLAB plug-in toolsets have now been released by researchers from many laboratories. Under NIH PAR 11-028 we propose renewal funding to further develop and maintain the EEGLAB software framework. We propose new and better tools for brain source and source network modeling and localization, an expanded online EEGLAB course and workshop, better statistical inference modeling of group data, and new support for automated source decomposition, measure computation, data duration, and data sharing.
A major shift in scientific perspective on the nature and use of human electrophysiological data is now accelerating from measuring and visualizing individual scalp channel signals to directly visualizing and interpreting their brain sources. The EEGLAB signal processing environment, an open source software project of the Swartz Center for Computational Neuroscience (SCCN) at the University Of California San Diego (UCSD), supports a large number of EEG and related electrophysiological research studies and teaching labs. We propose continued funding to further develop and maintain the EEGLAB environment.
|Ojeda, Alejandro; Bigdely-Shamlo, Nima; Makeig, Scott (2014) MoBILAB: an open source toolbox for analysis and visualization of mobile brain/body imaging data. Front Hum Neurosci 8:121|
|Lenartowicz, Agatha; Delorme, Arnaud; Walshaw, Patricia D et al. (2014) Electroencephalography correlates of spatial working memory deficits in attention-deficit/hyperactivity disorder: vigilance, encoding, and maintenance. J Neurosci 34:1171-82|
|Gramann, Klaus; Ferris, Daniel P; Gwin, Joseph et al. (2014) Imaging natural cognition in action. Int J Psychophysiol 91:22-9|
|Broccard, Frédéric D; Mullen, Tim; Chi, Yu Mike et al. (2014) Closed-loop brain-machine-body interfaces for noninvasive rehabilitation of movement disorders. Ann Biomed Eng 42:1573-93|
|McLoughlin, Grainne; Palmer, Jason A; Rijsdijk, Fruhling et al. (2014) Genetic overlap between evoked frontocentral theta-band phase variability, reaction time variability, and attention-deficit/hyperactivity disorder symptoms in a twin study. Biol Psychiatry 75:238-47|
|McLoughlin, Grainne; Makeig, Scott; Tsuang, Ming T (2014) In search of biomarkers in psychiatry: EEG-based measures of brain function. Am J Med Genet B Neuropsychiatr Genet 165B:111-21|
|Bigdely-Shamlo, Nima; Mullen, Tim; Kreutz-Delgado, Kenneth et al. (2013) Measure projection analysis: a probabilistic approach to EEG source comparison and multi-subject inference. Neuroimage 72:287-303|
|Delorme, Arnaud; Palmer, Jason; Onton, Julie et al. (2012) Independent EEG sources are dipolar. PLoS One 7:e30135|
|Delorme, Arnaud; Mullen, Tim; Kothe, Christian et al. (2011) EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing. Comput Intell Neurosci 2011:130714|
|Acar, Zeynep Akalin; Makeig, Scott (2010) Neuroelectromagnetic forward head modeling toolbox. J Neurosci Methods 190:258-70|
Showing the most recent 10 out of 16 publications