AIMC Topic: Brain-Computer Interfaces

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Latent alignment in deep learning models for EEG decoding.

Journal of neural engineering Feb 17, 2025
. Brain-computer interfaces (BCIs) face a significant challenge due to variability in electroencephalography (EEG) signals across individuals. While recent approaches have focused on standardizing input signal distributions, we propose that aligning ...
Event-Related Potentials, P300 Sleep Stages Imagination Humans Deep Learning Female Electroencephalography Adult Brain-Computer Interfaces Male
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Enhancing detection of SSVEPs using discriminant compacted network.

Journal of neural engineering Feb 14, 2025
. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio and high information transfer rates (ITRs). Currently, accurate detection is a c...
Neural Networks, Computer Humans Male Female Photic Stimulation Electroencephalography Deep Learning Adult Brain-Computer Interfaces Discriminant Analysis Evoked Potentials, Visual
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Depth-sensor-based shared control assistance for mobility and object manipulation: toward long-term home-use of BCI-controlled assistive robotic devices.

Journal of neural engineering Feb 14, 2025
Assistive robots can be developed to restore or provide more autonomy for individuals with motor impairments. In particular, power wheelchairs can compensate lower-limb impairments, while robotic manipulators can compensate upper-limbs impairments. R...
Equipment Design Brain-Computer Interfaces Wheelchairs Self-Help Devices Quadriplegia Male Robotics Electroencephalography Humans Female Adult
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A combination of deep learning models and type-2 fuzzy for EEG motor imagery classification through spatiotemporal-frequency features.

Journal of medical engineering & technology Feb 14, 2025
Developing a robust and effective technique is crucial for interpreting a user's brainwave signals accurately in the realm of biomedical signal processing. The variability and uncertainty present in EEG patterns over time, compounded by noise, pose n...
Signal Processing, Computer-Assisted Humans Brain-Computer Interfaces Algorithms Deep Learning Fuzzy Logic Bayes Theorem Electroencephalography
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BrainForest: Neuromorphic Multiplier-Less Bit-Serial Weight-Memory-Optimized 1024-Tree Brain-State Classification Processor.

IEEE transactions on biomedical circuits and systems Feb 11, 2025
Personalized brain implants have the potential to revolutionize the treatment of neurological disorders and augment cognition. Medical implants that deliver therapeutic stimulation in response to detected seizures have already been deployed for the t...
Brain-Computer Interfaces Electroencephalography Seizures Signal Processing, Computer-Assisted Humans Neural Networks, Computer Machine Learning Algorithms Brain Models, Neurological
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Multiclass Classification Framework of Motor Imagery EEG by Riemannian Geometry Networks.

IEEE journal of biomedical and health informatics Feb 10, 2025
In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM) of electroencephalogram (EEG) signals plays a critical role in accurate classification. Given that SCMs are symmetric positive definite (SPD), Riema...
Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Neural Networks, Computer Algorithms Brain Imagination Humans
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Incremental Classification for High-Dimensional EEG Manifold Representation Using Bidirectional Dimensionality Reduction and Prototype Learning.

IEEE journal of biomedical and health informatics Feb 10, 2025
In brain-computer interface (BCI) systems, symmetric positive definite (SPD) manifold within Riemannian space has been frequently utilized to extract spatial features from electroencephalogram (EEG) signals. However, the intrinsic high dimensionality...
Machine Learning Algorithms Brain Humans Neural Networks, Computer Signal Processing, Computer-Assisted Adult Brain-Computer Interfaces Principal Component Analysis Dimensionality Reduction Electroencephalography
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ECA-FusionNet: a hybrid EEG-fNIRS signals network for MI classification.

Journal of neural engineering Feb 7, 2025
. Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical a...
Brain Humans Spectroscopy, Near-Infrared Brain-Computer Interfaces Neural Networks, Computer Electroencephalography Movement Imagination
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Robust Decoding of Rich Dynamical Visual Scenes With Retinal Spikes.

IEEE transactions on neural networks and learning systems Feb 6, 2025
Sensory information transmitted to the brain activates neurons to create a series of coping behaviors. Understanding the mechanisms of neural computation and reverse engineering the brain to build intelligent machines requires establishing a robust r...
Humans Brain-Computer Interfaces Neural Networks, Computer Photic Stimulation Visual Perception Algorithms Action Potentials Retina
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Enhanced electroencephalogram signal classification: A hybrid convolutional neural network with attention-based feature selection.

Brain research Feb 2, 2025
Accurate recognition and classification of motor imagery electroencephalogram (MI-EEG) signals are crucial for the successful implementation of brain-computer interfaces (BCI). However, inherent characteristics in original MI-EEG signals, such as non...
Brain-Computer Interfaces Imagination Electroencephalography Signal Processing, Computer-Assisted Male Attention Deep Learning Convolutional Neural Networks Humans Neural Networks, Computer Brain
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