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 …
High-speed spelling with a noninvasive brain–computer interface
The past 20 years have witnessed unprecedented progress in brain–computer interfaces
(BCIs). However, low communication rates remain key obstacles to BCI-based …
(BCIs). However, low communication rates remain key obstacles to BCI-based …
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 …
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface
Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-
state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) due to its …
state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) due to its …
A benchmark dataset for SSVEP-based brain–computer interfaces
This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset
acquired with a 40-target brain-computer interface (BCI) speller. The dataset consists of 64 …
acquired with a 40-target brain-computer interface (BCI) speller. The dataset consists of 64 …
Multi-kernel extreme learning machine for EEG classification in brain-computer interfaces
One of the most important issues for the development of a motor-imagery based brain-
computer interface (BCI) is how to design a powerful classifier with strong generalization …
computer interface (BCI) is how to design a powerful classifier with strong generalization …
A convolutional neural network for steady state visual evoked potential classification under ambulatory environment
The robust analysis of neural signals is a challenging problem. Here, we contribute a
convolutional neural network (CNN) for the robust classification of a steady-state visual …
convolutional neural network (CNN) for the robust classification of a steady-state visual …