This letter reports on the effectiveness of applying K-singular value decomposition (SVD) dictionary learning to electroencephalogram (EEG) compressed sensing framework with outlier detection and independent component analysis. Using K-SVD matrix our design parameter optimization, for example, at compression ratio four, we improved normalized mean square error by 31.4% compared that discrete co...

Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more "interesting" sources than with time-domain ICA, and to more meaningful...

A seizure prediction algorithm is proposed that combines novel multivariate EEG features with patient-specific machine learning. The algorithm computes the eigenspectra of space-delay correlation and covariance matrices from 15-s blocks of EEG data at multiple delay scales. The principal components of these features are used to classify the patient's preictal or interictal state. This is done u...

Electroencephalography (EEG) signals’ interpretation is based on waveform analysis, where meaningful information should emerge from a plethora of data. Nonetheless, the continuous increase in computational power and the development of new data processing algorithms in the recent years have put into reach the possibility of analyzing raw EEG signals. Bearing that motivation, the authors propose ...

The aim of this study was to ascertain the acceptability of sleep-deprived EEGs to parents and their young child. Fifty unselected children having a sleep-deprived EEG were recruited. Data were collected from a sleep diary, a parent questionnaire and the request form of the EEG. Data collected covered developmental, learning and behavioural problems and the acceptability of the sleep-deprived E...

Reliable diagnosis of depressive disorder is essential for both optimal treatment and prevention of fatal outcomes. In this study, we aimed to elucidate the effectiveness of two non-linear measures, Higuchi’s Fractal Dimension (HFD) and Sample Entropy (SampEn), in detecting depressive disorders when applied on EEG. HFD and SampEn of EEG signals were used as features for seven machine learning a...

Variational Learning theory allows the estimation of posterior probability distributions of model parameters, rather than the parameters themselves. We demonstrate the use of variational learning methods on HiddenMarkov models with different observation models and apply the HMM to a range of biomedical signals, such as EEG, periodic breathing and RR-interval series.

Using machine-learning methodologies to analyze EEG signals becomes increasingly attractive for recognizing human emotions because of the objectivity of physiological data and the capability of the learning principles on modeling emotion classifiers from heterogeneous features. However, the conventional subject-specific classifiers may induce additional burdens to each subject for preparing mul...