AIMC Topic: Brain-Computer Interfaces

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Speech decoding from stereo-electroencephalography (sEEG) signals using advanced deep learning methods.

Journal of neural engineering
Brain-computer interfaces (BCIs) are technologies that bypass damaged or disrupted neural pathways and directly decode brain signals to perform intended actions. BCIs for speech have the potential to restore communication by decoding the intended spe...

STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding.

Neural networks : the official journal of the International Neural Network Society
Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end...

A novel virtual robotic platform for controlling six degrees of freedom assistive devices with body-machine interfaces.

Computers in biology and medicine
Body-machine interfaces (BoMIs)-systems that control assistive devices (e.g., a robotic manipulator) with a person's movements-offer a robust and non-invasive alternative to brain-machine interfaces for individuals with neurological injuries. However...

Band power feature part-based convolutional neural network with African vulture optimization fostered channel selection for EEG classification.

Computer methods in biomechanics and biomedical engineering
The electroencephalogram-based motor imagery (MI-EEG) classification task is significant for brain-computer interface (BCI). EEG signals need a lot of channels to be acquired, which makes it difficult to use in real-world applications. Choosing the o...

Use of Artificial Intelligence Techniques to Assist Individuals with Physical Disabilities.

Annual review of biomedical engineering
Assistive technologies (AT) enable people with disabilities to perform activities of daily living more independently, have greater access to community and healthcare services, and be more productive performing educational and/or employment tasks. Int...

Multiclass Classification of Visual Electroencephalogram Based on Channel Selection, Minimum Norm Estimation Algorithm, and Deep Network Architectures.

Sensors (Basel, Switzerland)
This work addresses the challenge of classifying multiclass visual EEG signals into 40 classes for brain-computer interface applications using deep learning architectures. The visual multiclass classification approach offers BCI applications a signif...

Large-scale foundation models and generative AI for BigData neuroscience.

Neuroscience research
Recent advances in machine learning have led to revolutionary breakthroughs in computer games, image and natural language understanding, and scientific discovery. Foundation models and large-scale language models (LLMs) have recently achieved human-l...

Leveraging temporal dependency for cross-subject-MI BCIs by contrastive learning and self-attention.

Neural networks : the official journal of the International Neural Network Society
Brain-computer interfaces (BCIs) built based on motor imagery paradigm have found extensive utilization in motor rehabilitation and the control of assistive applications. However, traditional MI-BCI systems often exhibit suboptimal classification per...

EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion.

Journal of affective disorders
Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lac...

Brain-computer interfaces inspired spiking neural network model for depression stage identification.

Journal of neuroscience methods
BACKGROUND: Depression is a global mental disorder, and traditional diagnostic methods mainly rely on scales and subjective evaluations by doctors, which cannot effectively identify symptoms and even carry the risk of misdiagnosis. Brain-Computer Int...