Friday, May 2, 2025
5/2/2025
User-friendly open-source pipeline for anatomically precise analysis of single-trial M/EEG - PROJECT SUMMARY Magneto- and electroencephalography (M/EEG) measure human brain activity non-invasively, using sensors placed on or above the head, with millisecond accuracy, and have long been used to study neural and cognitive processes. This project is to develop a user-friendly, open-source analysis pipeline to make recent innovations in analysis techniques available to a broad audience. First, M/EEG has traditionally been analyzed primarily as event-related activity (an experiment is conceptualized as a series of trials, each constituting a unique event; for example, the response to a syllable da). Such experiments typically require many repetitions of identical trials to separate signal from noise in the M/EEG response. More recent techniques allow conceptualizing experimental time as continuous, with different aspects of stimuli that are distributed in time evoking different overlapping brain responses (for example, the auditory cortex response continuously “tracks” the acoustic envelope of speech, or the visual cortex tracks the amount of visual motion in a movie, etc.). In this time-continuous analysis, no repeated trials are necessary, and the signal is instead estimated using detailed models of the stimuli. Second, while brain activity is measured outside of the head, it has long been possible to estimate anatomically localized sources of this activity in the brain, and recent advances have greatly improved this source estimation. These two advances (single-trial continuous analysis and improved source localization) have only recently been combined successfully, and no off-the-shelf software package allows researchers to readily apply this method to existing or new datasets. This project will develop such a software tool with an easy-to-use pipeline for group level analysis. The technique that this tool will make available is essential for analyzing data from experimental designs with naturalistic stimuli (for example, participants watching a movie or listening to an audiobook). Such experiments offer enhanced validity, and are rich and versatile: researchers can test many different hypotheses about what neural processes are evoked by the stimuli. However, the traditional averaging techniques that are widely available are not remotely suitable. The software tool developed here is ideally suited for analyzing such datasets. It will be made widely accessible and user-friendly, to lower the threshold for non-neuroscientists to test hypotheses about neural representations. For example, existing datasets of audiobook listening could be used by psychologists, computer scientists and linguists to test hypotheses about neural representations of speech.
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