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MTSAN-MI: Multiscale Temporal-Spatial Convolutional Self-attention Network for Motor Imagery Classification
EEG signals are widely utilized in brain-computer interfaces, where motor imagery (MI) data plays a crucial role. The effective alignment of MI-based...
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Boosting motor imagery brain-computer interface classification using multiband and hybrid feature extraction
Brain-computer interface (BCI) is a new promising technology for control and communication, the BCI system aims to decode the measured brain activity...
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Multi-class Classification of Motor Imagery EEG Signals Using Deep Learning Models
The accurate classification of Motor Imagery (MI) electroencephalography (EEG) signals is crucial for advancing Brain-Computer Interface (BCI)...
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Merged CNNs for the classification of EEG motor imagery signals
The using of Electroencephalography (EEG) signals for motor imagery (MI) has recently gained significant attention due to their remarkable ability to...
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A novel precisely designed compact convolutional EEG classifier for motor imagery classification
Robust classification of electroencephalogram data for motor imagery recognition is of paramount importance in brain–computer interface (BCI) domain....
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Deep-learning-based motor imagery EEG classification by exploiting the functional connectivity of cortical source imaging
Motor imagery (MI) is a commonly used brain–computer interface paradigm, and decoding the MI-EEG signals has been an active research area in recent...
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Adaptive filter of frequency bands based coordinate attention network for EEG-based motor imagery classification
PurposeIn the brain-computer interface (BCI), motor imagery (MI) could be defined as the Electroencephalogram (EEG) signals through imagined...
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Multiple tangent space projection for motor imagery EEG classification
Due to its non-invasiveness and easiness to implement, EEG signals decoding are in base of most based brain computer interfaces (BCI) studies. Given...
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Incorporating hand-crafted features into deep learning models for motor imagery EEG-based classification
Motor imagery (MI) is a mental process that produces two types of event-related potentials called event-related desynchronization (ERD) and...
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Enhanced Motor Imagery Based Brain-Computer Interface via Vibration Stimulation and Robotic Glove for Post-Stroke Rehabilitation
Motor imagery based brain-computer interface (MI-BCI) has been extensively researched as a potential intervention to enhance motor function for...
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Normalized deep learning algorithms based information aggregation functions to classify motor imagery EEG signal
Recently, the discipline of Brain-Computer-Interface (BCI) has attracted attention to exploiting Electroencephalograph (EEG) mental activities such...
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Semi-supervised classifier with projection graph embedding for motor imagery electroencephalogram recognition
Brain computer interface (BCI) based on motor imagery (MI) provides a communication channel between the brain and a computer or other communication...
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Comparison of Visual and Kinesthetic Motor Imagery for Upper Limb Activity
Brain computer interfaces have different applications, according to the input stimulation of the participants and the context where they are put to...
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Influence of Delays in Functional Connectivity to Distinguish Motor Imagery Tasks
Motor imagery is a cognitive technique wherein individuals mentally rehearse bodily movements. Recognized for its alignment with various cognitive...
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Spatial Feature Regularization and Label Decoupling Based Cross-Subject Motor Imagery EEG Decoding
Motor imagery (MI) serves as a vital approach to constructing brain-computer interfaces (BCIs) based on electroencephalogram (EEG) signals. However,...
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Convolutional neural network with support vector machine for motor imagery EEG signal classification
Electroencephalography (EEG) motor imagery (MI) signals has recently attracted a great deal of attention as these signals encrypt a person's desire...
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Virtual Reality Embodiment in Motor Imagery Brain–Computer Interface Training
This study investigates how the avatar embodiment in virtual reality (VR) influences training for operation of motor imagery brain–computer...
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Dictionary reduction in sparse representation-based classification of motor imagery EEG signals
Recently, sparse representation-based classification has turned into a successful technique for motor imagery electroencephalogram signal analysis....
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Multi-class Motor Imagery Recognition of Single Joint in Upper Limb Based on Multi-domain Feature Fusion
Aiming at the difficulties in extracting effective features and low classification accuracy in the current multi-class motor imagery recognition,...
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A rehabilitation framework based on motor imagery induced wheelchair movement using fuzzy vector quantization
Stroke and many other neural disorders affecting motor functions lead to a significant degradation in the quality of life. The solution to the...
