Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its
predictive power. Such evaluation is achieved via cross-validation, a method also used to …

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard
practice in analyzing fMRI data. Although decoding methods have been extensively applied …

Deep learning algorithms trained to predict masked words from large amount of text have
recently been shown to generate activations similar to those of the human brain. However …

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic
oscillations have been linked to numerous physiological, cognitive, behavioral and disease …

Understanding spoken language requires transforming ambiguous acoustic streams into a
hierarchy of representations, from phonemes to meaning. It has been suggested that the …

The clinical and societal measurement of human sleep has increased exponentially in
recent years. However, unlike other fields of medical analysis that have become highly …

Objective. Supervised learning paradigms are often limited by the amount of labeled data
that is available. This phenomenon is particularly problematic in clinically-relevant data …

Sleep stage classification constitutes an important preliminary exam in the diagnosis of
sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of …

Speech consists of a continuously-varying acoustic signal. Yet human listeners experience it
as sequences of discrete speech sounds, which are used to recognise discrete words. To …

The Brain Imaging Data Structure (BIDS) project is a rapidly evolving effort in the human
brain imaging research community to create standards allowing researchers to readily …