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 …
Feature subset and time segment selection for the classification of EEG data based motor imagery
The selection of feature subset and time segment is of great significance to the benefit of
motor imagery classification. Hence, applying it in classification as well as itself alone has …
motor imagery classification. Hence, applying it in classification as well as itself alone has …
Correlation-based channel selection and regularized feature optimization for MI-based BCI
Multi-channel EEG data are usually necessary for spatial pattern identification in motor
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …
imagery (MI)-based brain computer interfaces (BCIs). To some extent, signals from some …
Investigating feature selection techniques to enhance the performance of EEG-based motor imagery tasks classification
Analyzing electroencephalography (EEG) signals with machine learning approaches has
become an attractive research domain for linking the brain to the outside world to establish …
become an attractive research domain for linking the brain to the outside world to establish …
Subject and class specific frequency bands selection for multiclass motor imagery classification
HI Suk, SW Lee - International Journal of Imaging Systems and …, 2011 - Wiley Online Library
EEG‐based discrimination among motor imagery states has been widely studied for brain‐
computer interfaces (BCIs) due to the great potential for real‐life applications. However, in …
computer interfaces (BCIs) due to the great potential for real‐life applications. However, in …
Motor imagery BCI classification based on novel two‐dimensional modelling in empirical wavelet transform
Brain complexity and non‐stationary nature of electroencephalography (EEG) signal make
considerable challenges for the accurate identification of different motor‐imagery (MI) tasks …
considerable challenges for the accurate identification of different motor‐imagery (MI) tasks …
A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces
H Choi, J Park, YM Yang - Sensors, 2022 - mdpi.com
The brain–computer interface (BCI) is used to understand brain activities and external
bodies with the help of the motor imagery (MI). As of today, the classification results for EEG …
bodies with the help of the motor imagery (MI). As of today, the classification results for EEG …
Multi-task EEG signal classification using correlation-based IMF selection and multi-class CSP
N Alizadeh, S Afrakhteh, MR Mosavi - IEEE Access, 2023 - ieeexplore.ieee.org
In the context of motor imagery (MI)-based brain-computer interface (BCI) systems, a great
amount of research has been studied for attaining higher classification performance by …
amount of research has been studied for attaining higher classification performance by …
Uncorrelated multiway discriminant analysis for motor imagery EEG classification
Motor imagery-based brain–computer interfaces (BCIs) training has been proved to be an
effective communication system between human brain and external devices. A practical …
effective communication system between human brain and external devices. A practical …
A novel method for classification of multi-class motor imagery tasks based on feature fusion
Y Hou, T Chen, X Lun, F Wang - Neuroscience Research, 2022 - Elsevier
Motor imagery based brain computer interface (MI-BCI) has the advantage of strong
independence that can rely on the spontaneous brain activity of the user to operate external …
independence that can rely on the spontaneous brain activity of the user to operate external …