Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
Independent component analysis: theory and applications
Independent component analysis: theory and applications
High-order contrasts for independent component analysis
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
Joint Approximate Diagonalization of Positive Definite Hermitian Matrices
SIAM Journal on Matrix Analysis and Applications
Robust adaptive techniques for minimization of EOG artefacts from EEG signals
Signal Processing - Signal processing in UWB communications
Fast approximate joint diagonalization incorporating weight matrices
IEEE Transactions on Signal Processing
Computational Intelligence and Neuroscience - Special issue on academic software applications for electromagnetic brain mapping using MEG and EEG
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
IEEE Transactions on Neural Networks
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EEG signals are often contaminated by ocular artifacts (OAs), in particular when they are recorded for a subject that is, in principle, awake, such as in a study of drowsiness. It is generally desirable to detect and/or correct these OAs before interpreting the EEG signals. We have identified 11 existing methods for dealing with OAs. Their study allowed us to create 16 new methods. We performed a comparative performance evaluation of the resulting 27 distinct methods using a common set of data and a common set of metrics. The data was recorded during a driving task of about two hours in a driving simulator. This led to a ranking of all methods, with five emerging clear winners, comprising two existing methods and three new ones.