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 …
[PDF][PDF] Bio-robotics research for non-invasive myoelectric neural interfaces for upper-limb prosthetic control: A 10-year perspective review
ABSTRACT A decade ago, a group of researchers from academia and industry identified a
dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis …
dichotomy between the industrial and academic state-of-the-art in upper-limb prosthesis …
Data augmentation for motor imagery signal classification based on a hybrid neural network
As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery
(MI) has been widely used in the fields of neurological rehabilitation and robot control …
(MI) has been widely used in the fields of neurological rehabilitation and robot control …
An iterative wavelet threshold for signal denoising
This paper introduces an adaptive filtering process based on shrinking wavelet coefficients
from the corresponding signal wavelet representation. The filtering procedure considers a …
from the corresponding signal wavelet representation. The filtering procedure considers a …
Discriminative canonical pattern matching for single-trial classification of ERP components
Event-related potentials (ERPs) are one of the most popular control signals for brain-
computer interfaces (BCIs). However, they are very weak and sensitive to the experimental …
computer interfaces (BCIs). However, they are very weak and sensitive to the experimental …
An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information
Background Common spatial pattern (CSP) has been an effective technique for feature
extraction in electroencephalography (EEG) based brain computer interfaces (BCIs) …
extraction in electroencephalography (EEG) based brain computer interfaces (BCIs) …
Online adaptation boosts SSVEP-based BCI performance
Objective: A user-friendly steady-state visual evoked potential (SSVEP)-based brain-
computer interface (BCI) prefers no calibration for its target recognition algorithm, however …
computer interface (BCI) prefers no calibration for its target recognition algorithm, however …
CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI
Background Classification of electroencephalography (EEG) signals for motor imagery
based brain computer interface (MI-BCI) is an exigent task and common spatial pattern …
based brain computer interface (MI-BCI) is an exigent task and common spatial pattern …
Rotational data augmentation for electroencephalographic data
Motivation: For deep learning on image data, a common approach is to augment the training
data by artificial new images, using techniques like moving windows, scaling, affine …
data by artificial new images, using techniques like moving windows, scaling, affine …
A new parameter tuning approach for enhanced motor imagery EEG signal classification
A brain-computer interface (BCI) system allows direct communication between the brain and
the external world. Common spatial pattern (CSP) has been used effectively for feature …
the external world. Common spatial pattern (CSP) has been used effectively for feature …