[HTML][HTML] Tensor decomposition of EEG signals: a brief review
Electroencephalography (EEG) is one fundamental tool for functional brain imaging. EEG
signals tend to be represented by a vector or a matrix to facilitate data processing and …
signals tend to be represented by a vector or a matrix to facilitate data processing and …
Autism: cause factors, early diagnosis and therapies
Autism spectrum disorder (ASD) is a complex neurobiological disorder characterized by
neuropsychological and behavioral deficits. Cognitive impairment, lack of social skills, and …
neuropsychological and behavioral deficits. Cognitive impairment, lack of social skills, and …
Temporally constrained sparse group spatial patterns for motor imagery BCI
Common spatial pattern (CSP)-based spatial filtering has been most popularly applied to
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …
electroencephalogram (EEG) feature extraction for motor imagery (MI) classification in brain …
[BUKU][B] The illustrated wavelet transform handbook: introductory theory and applications in science, engineering, medicine and finance
PS Addison - 2017 - taylorfrancis.com
This second edition of The Illustrated Wavelet Transform Handbook: Introductory Theory and
Applications in Science, Engineering, Medicine and Finance has been fully updated and …
Applications in Science, Engineering, Medicine and Finance has been fully updated and …
Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis
Canonical correlation analysis (CCA) has been one of the most popular methods for
frequency recognition in steady-state visual evoked potential (SSVEP)-based brain …
frequency recognition in steady-state visual evoked potential (SSVEP)-based brain …
Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface
Background Common spatial pattern (CSP) has been most popularly applied to motor-
imagery (MI) feature extraction for classification in brain–computer interface (BCI) …
imagery (MI) feature extraction for classification in brain–computer interface (BCI) …
Sparse Bayesian learning for obtaining sparsity of EEG frequency bands based feature vectors in motor imagery classification
Effective common spatial pattern (CSP) feature extraction for motor imagery (MI)
electroencephalogram (EEG) recordings usually depends on the filter band selection to a …
electroencephalogram (EEG) recordings usually depends on the filter band selection to a …
L1-regularized multiway canonical correlation analysis for SSVEP-based BCI
Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and
designed reference signals of sine-cosine waves usually works well for steady-state visual …
designed reference signals of sine-cosine waves usually works well for steady-state visual …
Computer-aided diagnosis of depression using EEG signals
The complex, nonlinear and non-stationary electroencephalogram (EEG) signals are very
tedious to interpret visually and highly difficult to extract the significant features from them …
tedious to interpret visually and highly difficult to extract the significant features from them …
Tensor-based anomaly detection: An interdisciplinary survey
Traditional spectral-based methods such as PCA are popular for anomaly detection in a
variety of problems and domains. However, if data includes tensor (multiway) structure (eg …
variety of problems and domains. However, if data includes tensor (multiway) structure (eg …