Ours and others' work have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range.
Adaptive Frequency Tracking
Oscillatory phenomena have gained increasing importance in the field of neuroscience, particularly because improvements in analysis methods have revealed how oscillatory activity is both a highly efficient and also information-rich signal. One paradigmatic shift in the conceptualization of oscillatory activity has been to consider not only changes within a particular frequency band, but also the interactions and synchronizations between frequencies of brain activity that are in turn thought to coordinate responses between widespread brain areas and may represent a key “binding” mechanism necessary for perception, consciousness and actions. This project focuses on the development of methods to non-invasively and quantitatively assess such oscillatory activity and applies these methods to identify the spatio-temporal mechanisms underlying healthy and impaired sensation and perception.
In EEG research, peri-stimulus averages across trials are typically used to derive event-related potentials (ERPs) at each recorded electrode and to study evoked neural responses to external stimuli. Averaging across trials improves the signal-to-noise ratio and reduces the influence of physiological and instrumental noise because it preserves only those EEG signals that are time-locked to stimulus onset.
Efforts of the CIBM's EEG Brain Mapping Core focus on the developments and application of machine learning techniques for single-trial EEG analysis based on neurophysiologically interpretable features (i.e. voltage topographies at the scalp). The distribution of the voltage measurement across the scalp is informative of the underlying neural sources configuration in a way that a change in this distribution forcibly reflects a change at the level of the sources (reviewed in Michel, Murray et al., 2004; Murray et al., 2008; Michel & Murray 2012). The changes over time and across trials and conditions of these distributions can be captured at the level of the single ERP responses by a single-trial classification method based on topographic analysis.
These methods intend detailing interdependencies between single-trial responses and performance. In clinical research, it gives the possibility to statistically evaluate single subject data, an essential tool for analyzing patients with specific deficits and that cannot be considered part of a group.