AIMC Topic: Signal Processing, Computer-Assisted
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Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.
Computers in biology and medicine
Jun 10, 2025
The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy efficiency and ability to process spatiotemporal information, closely imitating the behavioral mechanisms of biological neurons in the brain. SNN exhibit ...
Automated classification of seizure onset pattern using intracranial electroencephalogram signal of non-human primates.
Physiological measurement
Jun 10, 2025
To develop and validate a machine learning framework for the classification of distinct seizure onset patterns using intracranial EEG (iEEG) recordings in a non-human primate (NHP) model of penicillin-induced seizures.iEEG data were collected from si...
A machine-learning-based approach to predict early hallmarks of progressive hearing loss.
Hearing research
Jun 6, 2025
Machine learning (ML) techniques are increasingly being used to improve disease diagnosis and treatment. However, the application of these computational approaches to the early diagnosis of age-related hearing loss (ARHL), the most common sensory def...
Age Factors
Disease Models, Animal
Mice, Inbred C57BL
Predictive Value of Tests
Male
Disease Progression
Mice
Hearing
Auditory Threshold
Evoked Potentials, Auditory, Brain Stem
Presbycusis
Signal Processing, Computer-Assisted
Cadherins
Early Diagnosis
Female
Humans
Machine Learning
Animals
Phenotype
Angular correlation-based feature selection for machine learning classification of manual automatisms using body sensor network data.
Computers in biology and medicine
Jun 4, 2025
Automatisms are repetitive, semi-ordered movements often observed in focal impaired awareness seizures and, less frequently, in generalized seizures with brief loss of consciousness. This study aims to improve the detection of these automatisms by op...
Assessing the impact of artifact correction and artifact rejection on the performance of SVM- and LDA-based decoding of EEG signals.
NeuroImage
Jun 3, 2025
Numerous studies have demonstrated that eyeblinks and other large artifacts can decrease the signal-to-noise ratio of EEG data, resulting in decreased statistical power for conventional univariate analyses. However, it is not clear whether eliminatin...
PhyTransformer: A unified framework for learning spatial-temporal representation from physiological signals.
Neural networks : the official journal of the International Neural Network Society
May 19, 2025
As a modal of physiological information, electroencephalogram (EEG), surface electromyography (sEMG), and eye tracking (ET) signals are widely used to decode human intention, promoting the development of human-computer interaction systems. Extensive ...
Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods.
Sensors (Basel, Switzerland)
May 18, 2025
Driven by the remarkable capabilities of machine learning, brain-computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to promin...
Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.
Nature cardiovascular research
May 16, 2025
Large-cohort imaging and diagnostic studies often assess cardiac function but overlook underlying biological mechanisms. Cardiac digital twins (CDTs) are personalized physics-constrained and physiology-constrained in silico representations, uncoverin...
Continuous Joint Kinematics Prediction Using GAT-LSTM Framework Based on Muscle Synergy and Sparse sEMG.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 8, 2025
sEMG signals hold significant potential for motion prediction, with promising applications in areas such as rehabilitation, sports training, and human-computer interaction. However, achieving robust prediction accuracy remains a critical challenge, a...
Event driven neural network on a mixed signal neuromorphic processor for EEG based epileptic seizure detection.
Scientific reports
May 7, 2025
Long-term monitoring of biomedical signals is essential for the modern clinical management of neurological conditions such as epilepsy. However, developing wearable systems that are able to monitor, analyze, and detect epileptic seizures with long-la...
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