A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update
Objective. Most current electroencephalography (EEG)-based brain–computer interfaces
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …
(BCIs) are based on machine learning algorithms. There is a large diversity of classifier …
Feature extraction and classification methods for hybrid fNIRS-EEG brain-computer interfaces
In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared
spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) …
spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) …
Enhancing detection of SSVEPs for a high-speed brain speller using task-related component analysis
Objective: This study proposes and evaluates a novel data-driven spatial filtering approach
for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high …
for enhancing steady-state visual evoked potentials (SSVEPs) detection toward a high …
Improving the performance of individually calibrated SSVEP-BCI by task-discriminant component analysis
A brain-computer interface (BCI) provides a direct communication channel between a brain
and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has …
and an external device. Steady-state visual evoked potential based BCI (SSVEP-BCI) has …
Robust similarity measurement based on a novel time filter for SSVEPs detection
The steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has
received extensive attention in research for the less training time, excellent recognition …
received extensive attention in research for the less training time, excellent recognition …
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 …
[HTML][HTML] Hybrid brain–computer interface techniques for improved classification accuracy and increased number of commands: a review
In this paper, hybrid brain-computer interface (hBCI) technologies for improving
classification accuracy and increasing the number of commands are reviewed. Hybridization …
classification accuracy and increasing the number of commands are reviewed. Hybridization …
[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 …
A comparison study of canonical correlation analysis based methods for detecting steady-state visual evoked potentials
Canonical correlation analysis (CCA) has been widely used in the detection of the steady-
state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard …
state visual evoked potentials (SSVEPs) in brain-computer interfaces (BCIs). The standard …
Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system
Recognition of epileptic seizures from offline EEG signals is very important in clinical
diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine …
diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine …