A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of
communication through the utilization of neural activity generated due to kinesthetic …
communication through the utilization of neural activity generated due to kinesthetic …
An end-to-end deep learning approach to MI-EEG signal classification for BCIs
H Dose, JS Møller, HK Iversen… - Expert Systems with …, 2018 - Elsevier
Goal: To develop and implement a Deep Learning (DL) approach for an
electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) …
electroencephalogram (EEG) based Motor Imagery (MI) Brain-Computer Interface (BCI) …
Review of Riemannian distances and divergences, applied to SSVEP-based BCI
The firstgeneration of brain-computer interfaces (BCI) classifies multi-channel
electroencephalographic (EEG) signals, enhanced by optimized spatial filters. The second …
electroencephalographic (EEG) signals, enhanced by optimized spatial filters. The second …
Bispectrum-based channel selection for motor imagery based brain-computer interfacing
The performance of motor imagery (MI) based Brain-computer interfacing (BCI) is easily
affected by noise and redundant information that exists in the multi-channel …
affected by noise and redundant information that exists in the multi-channel …
Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels
The essential task of a motor imagery brain–computer interface (BCI) is to extract the motor
imagery-related features from electroencephalogram (EEG) signals for classifying motor …
imagery-related features from electroencephalogram (EEG) signals for classifying motor …
Multi-view multi-scale optimization of feature representation for EEG classification improvement
Effectively extracting common space pattern (CSP) features from motor imagery (MI) EEG
signals is often highly dependent on the filter band selection. At the same time, optimizing …
signals is often highly dependent on the filter band selection. At the same time, optimizing …
Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition
D Gurve, D Delisle-Rodriguez… - Journal of Neural …, 2020 - iopscience.iop.org
Objective. This study aims to propose and validate a subject-specific approach to recognize
two different cognitive neural states (relax and pedaling motor imagery (MI)) by selecting the …
two different cognitive neural states (relax and pedaling motor imagery (MI)) by selecting the …
Improvement motor imagery EEG classification based on sparse common spatial pattern and regularized discriminant analysis
R Fu, M Han, Y Tian, P Shi - Journal of Neuroscience Methods, 2020 - Elsevier
Background The classification of psychological tasks such as motor imagery based on
electroencephalography (EEG) signals is an essential issue in the brain computer interface …
electroencephalography (EEG) signals is an essential issue in the brain computer interface …
Automatic detection of epileptic seizures in EEG using sparse CSP and fisher linear discrimination analysis algorithm
R Fu, Y Tian, P Shi, T Bao - Journal of medical systems, 2020 - Springer
In order to realize the automatic epileptic seizure detection, feature extraction and
classification of electroencephalogram (EEG) signals are performed on the interictal, the pre …
classification of electroencephalogram (EEG) signals are performed on the interictal, the pre …
Effects of an integrated neurofeedback system with dry electrodes: EEG acquisition and cognition assessment
G Pei, J Wu, D Chen, G Guo, S Liu, M Hong, T Yan - Sensors, 2018 - mdpi.com
Electroencephalogram (EEG) neurofeedback improves cognitive capacity and behaviors by
regulating brain activity, which can lead to cognitive enhancement in healthy people and …
regulating brain activity, which can lead to cognitive enhancement in healthy people and …