AIMC Topic: Imagination

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ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data.

Computers in biology and medicine Nov 2, 2023
OBJECTIVE: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signal...
Neural Networks, Computer Machine Learning Humans Brain-Computer Interfaces Movement Imagination Algorithms Electroencephalography
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A novel method for modeling effective connections between brain regions based on EEG signals and graph neural networks for motor imagery detection.

Computer methods in biomechanics and biomedical engineering Aug 7, 2023
Classified as biomedical signal processing, cerebral signal processing plays a key role in human-computer interaction (HCI) and medical diagnosis. The motor imagery (MI) problem is an important research area in this field. Accurate solutions to this ...
Imagination Neural Networks, Computer Brain Electroencephalography Signal Processing, Computer-Assisted Humans
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EEG motor imagery classification using deep learning approaches in naïve BCI users.

Biomedical physics & engineering express Jun 23, 2023
Motor Imagery (MI)-Brain Computer-Interfaces (BCI) illiteracy defines that not all subjects can achieve a good performance in MI-BCI systems due to different factors related to the fatigue, substance consumption, concentration, and experience in the ...
Humans Reproducibility of Results Deep Learning Imagination Electroencephalography Brain-Computer Interfaces
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Portable deep-learning decoder for motor imaginary EEG signals based on a novel compact convolutional neural network incorporating spatial-attention mechanism.

Medical & biological engineering & computing Apr 25, 2023
Due to high computational requirements, deep-learning decoders for motor imaginary (MI) electroencephalography (EEG) signals are usually implemented on bulky and heavy computing devices that are inconvenient for physical actions. To date, the applica...
Deep Learning Electroencephalography Neural Networks, Computer Algorithms Brain-Computer Interfaces Imagination Humans
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Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing.

Medical & biological engineering & computing Feb 23, 2023
Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG s...
Signal Processing, Computer-Assisted Brain-Computer Interfaces Imagination Support Vector Machine Electroencephalography Algorithms
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Classification of BCI Multiclass Motor Imagery Task Based on Artificial Neural Network.

Clinical EEG and neuroscience Jan 5, 2023
Motor imagery (MI) signals recorded by electroencephalography provide the most practical basis for conceiving brain-computer interfaces (BCI). These interfaces offer a high degree of freedom. This helps people with motor disabilities communicate with...
Imagination Brain Discriminant Analysis Signal Processing, Computer-Assisted Neural Networks, Computer Brain-Computer Interfaces Algorithms Electroencephalography Humans
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Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.

Scientific reports Dec 25, 2022
Achieving an efficient and reliable method is essential to interpret a user's brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it ...
Bayes Theorem Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Artificial Intelligence Algorithms Neural Networks, Computer Imagination
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A review of critical challenges in MI-BCI: From conventional to deep learning methods.

Journal of neuroscience methods Oct 29, 2022
Brain-computer interfaces (BCIs) have achieved significant success in controlling external devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor Imagery (MI) system bridges brain and external devices as communication tools...
Imagination Humans Electroencephalography Algorithms Signal Processing, Computer-Assisted Deep Learning Brain-Computer Interfaces
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Considerate motion imagination classification method using deep learning.

PloS one Oct 20, 2022
In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the ...
Imagination Electroencephalography Algorithms Deep Learning Brain-Computer Interfaces
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On the Deep Learning Models for EEG-Based Brain-Computer Interface Using Motor Imagery.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Aug 19, 2022
Motor imagery (MI) based brain-computer interface (BCI) is an important BCI paradigm which requires powerful classifiers. Recent development of deep learning technology has prompted considerable interest in using deep learning for classification and ...
Electroencephalography Humans Imagination Brain-Computer Interfaces Algorithms Deep Learning
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