AIMC Topic: Signal Processing, Computer-Assisted

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EEG Signals Classification Related to Visual Objects Using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression.

Brain topography Jan 6, 2025
By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of brain function. This study introduces a method for classifying EEG signals related to visual objects, employing a combination of an LSTM network and n...
Humans Signal Processing, Computer-Assisted Neural Networks, Computer Nonlinear Dynamics Brain Female Young Adult Male Adult Fuzzy Logic Electroencephalography
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Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.

Scientific reports Jan 6, 2025
We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardiographic (BCG) signals and explored their use in R-R interval (RRI) estimation. Preprocessed BCG and reference ECG signals were inputted into the bidi...
Ballistocardiography Electrocardiography Signal Processing, Computer-Assisted Heart Rate Male Deep Learning Humans Female Young Adult Adult
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A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.

Journal of neural engineering Jan 6, 2025
. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient...
Humans Neural Networks, Computer Linear Models Algorithms Brain-Computer Interfaces Electroencephalography Imagination Signal Processing, Computer-Assisted
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Real-time isolation of physiological tremor using recursive singular spectrum analysis and random vector functional link for surgical robotics.

ISA transactions Jan 4, 2025
Hand-held robotic instruments enhance precision in microsurgery by mitigating physiological tremor in real time. Current tremor filtering algorithms in these instruments often employ nonlinear phase prefilters to isolate the tremor signal. However, t...
Signal Processing, Computer-Assisted Tremor Humans Algorithms Robotics Robotic Surgical Procedures
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Cognitive load detection through EEG lead wise feature optimization and ensemble classification.

Scientific reports Jan 4, 2025
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying featur...
Male Cognition Electroencephalography Young Adult Adult Signal Processing, Computer-Assisted Humans Machine Learning Algorithms Brain Female
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ECGEFNet: A two-branch deep learning model for calculating left ventricular ejection fraction using electrocardiogram.

Artificial intelligence in medicine Jan 3, 2025
Left ventricular systolic dysfunction (LVSD) and its severity are correlated with the prognosis of cardiovascular diseases. Early detection and monitoring of LVSD are of utmost importance. Left ventricular ejection fraction (LVEF) is an essential ind...
Humans Ventricular Function, Left Stroke Volume Ventricular Dysfunction, Left Deep Learning Electrocardiography Signal Processing, Computer-Assisted
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DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN-Transformer Network.

Sensors (Basel, Switzerland) Jan 3, 2025
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by variou...
Artifacts Signal-To-Noise Ratio Deep Learning Electroencephalography Signal Processing, Computer-Assisted Humans Algorithms Neural Networks, Computer Brain
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Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML.

Scientific reports Jan 2, 2025
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-in...
Aged Young Adult Adult Humans Female Signal Processing, Computer-Assisted Blood Glucose Self-Monitoring Deep Learning Blood Glucose Photoplethysmography Male Middle Aged
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Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.

Scientific reports Jan 2, 2025
Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer int...
Evoked Potentials, Visual Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Neural Networks, Computer Algorithms Deep Learning
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A Novel State Space Model with Dynamic Graphic Neural Network for EEG Event Detection.

International journal of neural systems Dec 31, 2024
Electroencephalography (EEG) is a widely used physiological signal to obtain information of brain activity, and its automatic detection holds significant research importance, which saves doctors' time, improves detection efficiency and accuracy. Howe...
Signal Processing, Computer-Assisted Sleep Stages Brain Humans Electroencephalography Neural Networks, Computer Seizures
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