AIMC Topic: Electroencephalography

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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 ...
Female Adult Male Humans Event-Related Potentials, P300 Sleep Stages Imagination Deep Learning Electroencephalography Brain-Computer Interfaces
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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...
Humans Neural Networks, Computer Female Adult Male Deep Learning Evoked Potentials, Visual Photic Stimulation Discriminant Analysis Electroencephalography Brain-Computer Interfaces
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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 Male Robotics Electroencephalography Self-Help Devices Quadriplegia 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 Brain-Computer Interfaces Humans Algorithms Deep Learning Fuzzy Logic Bayes Theorem Electroencephalography
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A Multimodal Deep Learning Approach to Intraoperative Nociception Monitoring: Integrating Electroencephalogram, Photoplethysmography, and Electrocardiogram.

Sensors (Basel, Switzerland) Feb 13, 2025
Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), ...
Humans Photoplethysmography Middle Aged Electrocardiography Female Anesthesia, General Deep Learning Adult Monitoring, Intraoperative Male Electroencephalography Nociception
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Few-shot transfer learning for individualized braking intent detection on neuromorphic hardware.

Journal of neural engineering Feb 13, 2025
This work explores use of a few-shot transfer learning method to train and implement a convolutional spiking neural network (CSNN) on a BrainChip Akida AKD1000 neuromorphic system-on-chip for developing individual-level, instead of traditionally used...
Intention Automobile Driving Transfer, Psychology Neural Networks, Computer Machine Learning Female Adult Male Electroencephalography Humans
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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...
Neural Networks, Computer Machine Learning Algorithms Humans Seizures Signal Processing, Computer-Assisted Brain-Computer Interfaces Brain Models, Neurological Electroencephalography
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Low-Power and Low-Cost AI Processor With Distributed-Aggregated Classification Architecture for Wearable Epilepsy Seizure Detection.

IEEE transactions on biomedical circuits and systems Feb 11, 2025
Wearable devices with continuous monitoring capabilities are critical for the daily detection of epileptic seizures, as they provide users with accurate and comprehensible analytical results. However, current AI classifiers rely on a two-stage recogn...
Epilepsy Humans Seizures Signal Processing, Computer-Assisted Neural Networks, Computer Artificial Intelligence Algorithms Wearable Electronic Devices Electroencephalography
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RVDLAHA: An RISC-V DLA Hardware Architecture for On-Device Real-Time Seizure Detection and Personalization in Wearable Applications.

IEEE transactions on biomedical circuits and systems Feb 11, 2025
Epilepsy is a globally distributed chronic neurological disorder that may pose a threat to life without warning. Therefore, the use of wearable devices for real-time detection and treatment of epilepsy is crucial. Additionally, personalizing disease ...
Humans Neural Networks, Computer Algorithms Animals Seizures Rats Deep Learning Signal Processing, Computer-Assisted Electroencephalography Wearable Electronic Devices
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PhysioEx: a new Python library for explainable sleep staging through deep learning.

Physiological measurement Feb 10, 2025
Sleep staging is a crucial task in clinical and research contexts for diagnosing and understanding sleep disorders. This work introduces PhysioEx (Physiological Signal Explainer), a Python library designed to support the analysis of sleep stages usin...
Electroencephalography Signal Processing, Computer-Assisted Sleep Stages Humans Software Deep Learning
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