Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI
Classifying single-trial electroencephalogram (EEG) based motor imagery (MI) tasks is
extensively used to control brain-computer interface (BCI) applications, as a communication …
extensively used to control brain-computer interface (BCI) applications, as a communication …
Comparative analysis of features extracted from EEG spatial, spectral and temporal domains for binary and multiclass motor imagery classification
The electroencephalogram (EEG) remains the predominant source of neurophysiological
signals for motor imagery-based brain-computer interfaces (MI-BCIs). Various features can …
signals for motor imagery-based brain-computer interfaces (MI-BCIs). Various features can …
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
Electroencephalography (EEG) motor imagery (MI) signals have recently gained a lot of
attention as these signals encode a person's intent of performing an action. Researchers …
attention as these signals encode a person's intent of performing an action. Researchers …
IFNet: An interactive frequency convolutional neural network for enhancing motor imagery decoding from EEG
J Wang, L Yao, Y Wang - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
Objective: The key principle of motor imagery (MI) decoding for electroencephalogram
(EEG)-based Brain-Computer Interface (BCI) is to extract task-discriminative features from …
(EEG)-based Brain-Computer Interface (BCI) is to extract task-discriminative features from …
On the deep learning models for EEG-based brain-computer interface using motor imagery
Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm
which requires powerful classifiers. Recent development of deep learning technology has …
which requires powerful classifiers. Recent development of deep learning technology has …
A new approach for motor imagery classification based on sorted blind source separation, continuous wavelet transform, and convolutional neural network
CJ Ortiz-Echeverri, S Salazar-Colores… - Sensors, 2019 - mdpi.com
Brain-Computer Interfaces (BCI) are systems that allow the interaction of people and devices
on the grounds of brain activity. The noninvasive and most viable way to obtain such …
on the grounds of brain activity. The noninvasive and most viable way to obtain such …
Learning common time-frequency-spatial patterns for motor imagery classification
The common spatial patterns (CSP) algorithm is the most popular spatial filtering method
applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain …
applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain …
Complex common spatial patterns on time-frequency decomposed EEG for brain-computer interface
V Mishuhina, X Jiang - Pattern Recognition, 2021 - Elsevier
Motor imagery brain-computer interface (MI-BCI) has many promising applications but there
are problems such as poor classification accuracy and robustness which need to be …
are problems such as poor classification accuracy and robustness which need to be …
Short time Fourier transformation and deep neural networks for motor imagery brain computer interface recognition
Motor imagery (MI) is an important control paradigm in the field of brain‐computer interface
(BCI), which enables the recognition of personal intention. So far, numerous methods have …
(BCI), which enables the recognition of personal intention. So far, numerous methods have …
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 …