Understanding how the brain works in healthy and pathological conditions is considered as one of the major challenges for the 21st century. After the first electroencephalography (EEG) measurements in 1929, the 90's was the birth of modern functional brain imaging with the first functional MRI (fMRI) and full head magnetoencephalography (MEG) system. By offering noninvasively unique insights into the living brain, imaging has revolutionized in the last thirty years both clinical and cognitive neuroscience.

After pioneering breakthroughs in physics and engineering, the field of neuroscience has to face new major computational and statistical challenges. The size of the datasets produced by publicly funded populations studies (Human Connectome Project in the USA, UK Biobank or Cam-CAN in the UK etc.) keeps increasing with now hundreds of terabytes of data made available for basic and translational research.

While machine learning can offer great opportunities in this context, these datasets rarely come with strong annotations which are necessary to employ the most powerful supervised predictive models. In this talk I will present three statistical machine learning strategies applied to electrophysiological data where models are learnt without human supervision.

References:

Uncovering the structure of clinical EEG signals with self-supervised learning Banville H., Chehab O., Hyvärinen A., Engemann D., Gramfort A. (2021) Journal of Neural Engineering 18: (046020).

Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers Engemann D., Kozynets O., Sabbagh D., Lemaître G., Varoquaux G., Liem F., Gramfort A. (2020)

eLife 9: (e54055).