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Imagination

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FDCN-C: A deep learning model based on frequency enhancement, deformable convolution network, and crop module for electroencephalography motor imagery classification.

PloS one
Motor imagery (MI)-electroencephalography (EEG) decoding plays an important role in brain-computer interface (BCI), which enables motor-disabled patients to communicate with external world via manipulating smart equipment. Currently, deep learning (D...

Boosted Harris Hawks Shuffled Shepherd Optimization Augmented Deep Learning based motor imagery classification for brain computer interface.

PloS one
Motor imagery (MI) classification has been commonly employed in making brain-computer interfaces (BCI) to manage the outside tools as a substitute neural muscular path. Effectual MI classification in BCI improves communication and mobility for people...

MACNet: A Multidimensional Attention-Based Convolutional Neural Network for Lower-Limb Motor Imagery Classification.

Sensors (Basel, Switzerland)
Decoding lower-limb motor imagery (MI) is highly important in brain-computer interfaces (BCIs) and rehabilitation engineering. However, it is challenging to classify lower-limb MI from electroencephalogram (EEG) signals, because lower-limb motions (L...

An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.

Medical & biological engineering & computing
Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with the recovery progress. It is required to endow the recognition model with continuous learning and sel...

Enhancing Motor Imagery Classification with Residual Graph Convolutional Networks and Multi-Feature Fusion.

International journal of neural systems
Stroke, an abrupt cerebrovascular ailment resulting in brain tissue damage, has prompted the adoption of motor imagery (MI)-based brain-computer interface (BCI) systems in stroke rehabilitation. However, analyzing electroencephalogram (EEG) signals f...

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)
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
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...

Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.

PloS one
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...

Exploring cultural imaginaries of robots with children with brittle bone disease: a participatory design study.

Medical humanities
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, ...

A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.

Journal of neural engineering
. 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...