AIMC Topic: Brain-Computer Interfaces

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Graph convolution network-based eeg signal analysis: a review.

Medical & biological engineering & computing Jan 30, 2025
With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The appl...
Humans Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Neural Networks, Computer Algorithms Brain
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Unsupervised, piecewise linear decoding enables an accurate prediction of muscle activity in a multi-task brain computer interface.

Journal of neural engineering Jan 29, 2025
Creating an intracortical brain computer interface (iBCI) capable of seamless transitions between tasks and contexts would greatly enhance user experience. However, the nonlinearity in neural activity presents challenges to computing a global iBCI de...
Brain-Computer Interfaces Motor Cortex Macaca mulatta Unsupervised Machine Learning Electromyography Animals Male Muscle, Skeletal Algorithms
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Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.

Brain topography Jan 28, 2025
EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, a...
Adult Machine Learning Humans Male Brain Electroencephalography Neural Networks, Computer Brain-Computer Interfaces Female Speech Imagination
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The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.

Journal of neural engineering Jan 23, 2025
. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Electroencephalography (EEG) brainwave decoding, as an exemplar indicating neural activity and human thoughts non-invasively, has been helpful in neural a...
Privacy Brain-Computer Interfaces Brain Waves Brain Deep Learning Electroencephalography Humans Neural Networks, Computer Machine Learning
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TFTL: A Task-Free Transfer Learning Strategy for EEG-Based Cross-Subject and Cross-Dataset Motor Imagery BCI.

IEEE transactions on bio-medical engineering Jan 21, 2025
OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the long-term task-based calibration required for enhanced model performance leads to an unfriendly use...
Young Adult Adult Male Humans Algorithms Female Signal Processing, Computer-Assisted Brain-Computer Interfaces Imagination Deep Learning Electroencephalography
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Enhancing Domain Diversity of Transfer Learning-Based SSVEP-BCIs by the Reconstruction of Channel Correlation.

IEEE transactions on bio-medical engineering Jan 21, 2025
OBJECTIVE: The application of transfer learning, specifically pre-training and fine-tuning, in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been demonstrated to effectively improve the classification perform...
Brain-Computer Interfaces Evoked Potentials, Visual Deep Learning Humans Young Adult Electroencephalography Signal Processing, Computer-Assisted Algorithms Adult Female Male
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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...
Humans Neural Networks, Computer Algorithms Convolutional Neural Networks Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Signal-To-Noise Ratio Imagination
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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...
Brain Convolutional Neural Networks Electroencephalography Humans Neural Networks, Computer Brain-Computer Interfaces Imagination
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A hybrid CNN model for classification of motor tasks obtained from hybrid BCI system.

Scientific reports Jan 8, 2025
The Hybrid-Brain Computer Interface (BCI) has shown improved performance, especially in classifying multi-class data. Two non-invasive BCI modules are combined to achieve an improved classification which are Electroencephalogram (EEG) and functional ...
Electroencephalography Brain-Computer Interfaces Movement Adult Male Deep Learning Humans Neural Networks, Computer
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Multi-Scale Pyramid Squeeze Attention Similarity Optimization Classification Neural Network for ERP Detection.

Neural networks : the official journal of the International Neural Network Society Jan 7, 2025
Event-related potentials (ERPs) can reveal brain activity elicited by external stimuli. Innovative methods to decode ERPs could enhance the accuracy of brain-computer interface (BCI) technology and promote the understanding of cognitive processes. Th...
Brain Humans Deep Learning Neural Networks, Computer Attention Electroencephalography Algorithms Brain-Computer Interfaces Evoked Potentials
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