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DOSED: a deep learning approach to detect multiple sleep micro-events in EEG signal
Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal +2
Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so…
Beyond Pham's algorithm for joint diagonalization
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
The approximate joint diagonalization of a set of matrices consists in finding a basis in which these matrices are as diagonal as possible. This problem naturally appears in severa…
A Quasi-Newton algorithm on the orthogonal manifold for NMF with transform learning
Pierre Ablin, Dylan Fagot, Herwig Wendt +2
Nonnegative matrix factorization (NMF) is a popular method for audio spectral unmixing. While NMF is traditionally applied to off-the-shelf time-frequency representations based on…
A deep learning architecture to detect events in EEG signals during sleep
Stanislas Chambon, Valentin Thorey, Pierrick J. Arnal +2
Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (> 10s) such as…
Accelerating likelihood optimization for ICA on real signals
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort
We study optimization methods for solving the maximum likelihood formulation of independent component analysis (ICA). We consider both the the problem constrained to white signals…
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
Tom Dupré La Tour, Thomas Moreau, Mainak Jas +1
Frequency-specific patterns of neural activity are traditionally interpreted as sustained rhythmic oscillations, and related to cognitive mechanisms such as attention, high level v…