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Motor imagery classification using sparse nonnegative matrix factorization and convolutional neural networks
The motor movement performed by different body parts affects the synaptic potential at different brain cortices, which can be observed by the...
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A conditional input-based GAN for generating spatio-temporal motor imagery electroencephalograph data
Brain Computer Interface is an emerging technology for assisting patients having long term disability. Electroencephalography is the best technique...
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Efficient space learning based on kernel trick and dimension reduction technique for multichannel motor imagery EEG signals classification
Electroencephalogram (EEG) signals show the electrical activity of the brain, which are one of the inputs of the brain–computer interface (BCI). The...
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TD-LSTM: a time distributed and deep-learning-based architecture for classification of motor imagery and execution in EEG signals
One of the critical challenges in brain-computer interfaces is the classification of brain activities through the analysis of EEG signals. This paper...
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A Comprehensive Approach for Enhancing Motor Imagery EEG Classification in BCI’s
Electroencephalography (EEG) based on motor imagery has become a potential modality for brain-computer interface (BCI) systems, allowing users to...
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Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network
In recent years, there has been a renewal of interest in brain–computer interface (BCI). One of the BCI tasks is to classify the EEG motor imagery...
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Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review
The brain–computer interface (BCI) is an emerging technology that has the potential to revolutionize the world, with numerous applications ranging...
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Virtual Drone Control Using Brain-Computer Interface Based on Motor Imagery Brain Magnetic Fields
Brain-computer interfaces (BCIs) based on brain magnetic fields is a novel trend in the field of rehabilitation robotic that could be leveraged for...
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Domain-independent short-term calibration based hybrid approach for motor imagery electroencephalograph classification: a comprehensive review
Small availability of electroencephalograph (EEG) data makes the training of Brain-Computer Interface (BCI) significantly a difficult task. Recently,...
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EEG temporal information-based 1-D convolutional neural network for motor imagery classification
Brain-Computer Interface (BCI) enables human beings to interact with the outside world through brain intention. Human-computer interaction (HCI)...
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An optimized artificial intelligence based technique for identifying motor imagery from EEGs for advanced brain computer interface technology
Motor disability affects a person's ability to move and maintain balance. To remove this pain from the society, brain computer interface (BCI) system...
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Improving Motor Imagery Brain-Computer Interface Performance Through Data Screening
Brain-computer interface (BCI) technology enables the direct transmission of human control intentions to external devices, allowing direct control of...
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Improving Motor Imagery Intention Recognition via Local Relation Networks
Brain-computer interface (BCI) is a new communication and control technology established between human or animal brains and computer or other...
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Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network
Human limb movement imagery, which can be used in limb neural disorders rehabilitation and brain-controlled external devices, has become a...
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Data Augmentation for EEG Motor Imagery Classification Using Diffusion Model
Motor imagery classification using electroencephalogram (EEG) signals is an important research topic that has been extensively studied in the field...
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CNN models for EEG motor imagery signal classification
Motor imagery (MI) electroencephalography (EEG) signal classification plays an important role in brain–computer interface (BCI), which gives hope to...
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A Spiking Neural Network for Brain-Computer Interface of Four Classes Motor Imagery
Spiking neural networks (SNN) has the advantages of low power consumption and high efficiency in processing temporal information. However, due to the...
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Information Acquisition and Feature Extraction of Motor Imagery EEG
Brain-computer interface (BCI) is a new interaction model that directly connects the human brain or animal brain with external devices, which has a...
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T3SFNet: A Tuned Topological Temporal-Spatial Fusion Network for Motor Imagery with Rehabilitation Exoskeleton
In recent years, motor imagery-based brain computer interfaces (MI-BCI) combined with exoskeleton robot has proved to be a promising method for...
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A systematic rank of smart training environment applications with motor imagery brain-computer interface
Brain-Computer Interface (BCI) research is considered one of the significant interdisciplinary fields. It assists people with severe motor...
