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Single-trial EEG Analysis Methods for Understanding Individual Mechanisms of Learning and Plasticity

Understanding how the human brain processes incoming sensory stimuli has greatly benefited from the development of high-resolution functional brain imaging techniques. Within the others, electromagnetic brain imaging techniques such as electroencephalography (EEG) represent a unique access on this temporal dynamic at the same speed as the brain processes these stimuli.

Classical analysis and interpretation of electrical responses to external stimuli relies on the assumption that stimulus related activities, the so called event related potential, are stable in time (stationary) across several trials. Based on this assumption, the standard approach consists in analysing an average of brain responses to similar stimuli. However, the stationary hypothesis does not take into account many factors influencing the neural activity context, including effects of learning, plasticity and fatigue. These limitations have driven an increasing interest in developing new tools of EEG analysis at the level of single brain responses.

These kinds of methods open the way to address a fundamental issue in cognitive neuroscience, which consists in discovering relations between behavioural output and brain activity measures during the course of an experiment. Our plan is to further develop a single-trial method that we recently proposed and to exploit this approach for uncovering mechanisms underlying learning and plasticity phenomena. Our novel proposition for tackling single-trial multichannel EEG analysis -based on topographic map analysis and with minimal use of a priori constraints- has shown promising results on auditory stimulation dataset. Establishing robust criteria of application in general experimental conditions represent the next step to be taken.

The present project plan is expected to have an impact in several domains. In clinical research, this novel approach gives the possibility to statistically evaluate single subject data, an essential tool for analysing patients with specific deficits and impairments and that cannot be considered part of a group. In cognitive neuroscience, it provides a novel tool for understanding behaviour and brain activity interdependencies at both single subject and at group levels. In basic neurophysiology, it provides a new representation of event related potentials and promises to cast light on the mechanisms of its generation.