AIMC Topic: Imagination
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Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.
PloS one
Jan 16, 2025
In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (EEG) has shown promising applications in controlling various devices such as wheelchairs, assistive technologies, and driverless vehicles. However, de...
A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.
Sensors (Basel, Switzerland)
Jan 13, 2025
Enhancing motor disability assessment and its imagery classification is a significant concern in contemporary medical practice, necessitating reliable solutions to improve patient outcomes. One promising avenue is the use of brain-computer interfaces...
Multiscale Spatial-Temporal Feature Fusion Neural Network for Motor Imagery Brain-Computer Interfaces.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Motor imagery, one of the main brain-computer interface (BCI) paradigms, has been extensively utilized in numerous BCI applications, such as the interaction between disabled people and external devices. Precise decoding, one of the most significant a...
MFRC-Net: Multi-Scale Feature Residual Convolutional Neural Network for Motor Imagery Decoding.
IEEE journal of biomedical and health informatics
Jan 7, 2025
Motor imagery (MI) decoding is the basis of external device control via electroencephalogram (EEG). However, the majority of studies prioritize enhancing the accuracy of decoding methods, often overlooking the magnitude and computational resource dem...
A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
Journal of neural engineering
Jan 6, 2025
. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient...
Exploring cultural imaginaries of robots with children with brittle bone disease: a participatory design study.
Medical humanities
Jan 2, 2025
A symbiotic relationship exists between narrative imaginaries of and real-life advancements in technology. Such cultural imaginings have a powerful influence on our understanding of the potential that technology has to affect our lives; as a result, ...
Imagining alternative futures with augmentative and alternative communication: a manifesto.
Medical humanities
Jan 2, 2025
This manifesto seeks to challenge dominant narratives about the future of augmentative and alternative communication (AAC). Current predictions are mainly driven by technological developments-technologies usually being developed for different markets...
A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.
Sensors (Basel, Switzerland)
Dec 30, 2024
In this paper, a bibliometric review is conducted on brain-computer interfaces (BCI) in non-invasive paradigms like motor imagery (MI) and steady-state visually evoked potentials (SSVEP) for applications in rehabilitation and robotics. An exploratory...
Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.
Computers in biology and medicine
Dec 12, 2024
This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currentl...
MSVTNet: Multi-Scale Vision Transformer Neural Network for EEG-Based Motor Imagery Decoding.
IEEE journal of biomedical and health informatics
Dec 5, 2024
OBJECT: Transformer-based neural networks have been applied to the electroencephalography (EEG) decoding for motor imagery (MI). However, most networks focus on applying the self-attention mechanism to extract global temporal information, while the c...
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