AIMC Topic: Signal Processing, Computer-Assisted

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Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles.

SLAS technology Aug 28, 2024
One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatl...
Fuzzy Logic Decision Making Electroencephalography Signal Processing, Computer-Assisted Brain-Computer Interfaces Humans Machine Learning
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ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks.

Computers in biology and medicine Aug 27, 2024
We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal analysis, addressing both waveform delineation and beat type classification tasks. For beat type classification, we integrated two novel schemes into the...
Humans Heart Rate Electrocardiography Databases, Factual Signal Processing, Computer-Assisted Deep Learning Arrhythmias, Cardiac
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A new method of rock type identification based on transformer by utilizing acoustic emission.

PloS one Aug 27, 2024
The characterization and analysis of rock types based on acoustic emission (AE) signals have long been focal points in earth science research. However, traditional analysis methods struggle to handle the influx of big data. While signal processing me...
Models, Theoretical Neural Networks, Computer Algorithms Signal Processing, Computer-Assisted Acoustics
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MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

IEEE transactions on cybernetics Aug 26, 2024
Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. De...
Humans Neural Networks, Computer Signal Processing, Computer-Assisted Brain Algorithms Brain-Computer Interfaces Deep Learning Evoked Potentials Electroencephalography
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Efficient EEG Feature Learning Model Combining Random Convolutional Kernel with Wavelet Scattering for Seizure Detection.

International journal of neural systems Aug 23, 2024
Automatic seizure detection has significant value in epilepsy diagnosis and treatment. Although a variety of deep learning models have been proposed to automatically learn electroencephalography (EEG) features for seizure detection, the generalizatio...
Wavelet Analysis Seizures Signal Processing, Computer-Assisted Sensitivity and Specificity Deep Learning Electroencephalography Humans Neural Networks, Computer Machine Learning
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Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.

IEEE journal of translational engineering in health and medicine Aug 23, 2024
The integration of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However, existing research in this domain has grappled with the challenge of the eff...
Spectroscopy, Near-Infrared Electroencephalography Female Deep Learning Signal Processing, Computer-Assisted Adult Humans Brain-Computer Interfaces Male Brain
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Independent Vector Analysis for Feature Extraction in Motor Imagery Classification.

Sensors (Basel, Switzerland) Aug 22, 2024
Independent vector analysis (IVA) can be viewed as an extension of independent component analysis (ICA) to multiple datasets. It exploits the statistical dependency between different datasets through mutual information. In the context of motor imager...
Imagination Brain-Computer Interfaces Movement Humans Algorithms Support Vector Machine Electroencephalography Signal Processing, Computer-Assisted
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Advanced Sensing System for Sleep Bruxism across Multiple Postures via EMG and Machine Learning.

Sensors (Basel, Switzerland) Aug 22, 2024
Diagnosis of bruxism is challenging because not all contractions of the masticatory muscles can be classified as bruxism. Conventional methods for sleep bruxism detection vary in effectiveness. Some provide objective data through EMG, ECG, or EEG; ot...
Sleep Bruxism Electromyography Masseter Muscle Female Young Adult Adult Male Posture Signal Processing, Computer-Assisted Humans Machine Learning
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MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice.

Biosensors Aug 22, 2024
Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and re...
Neural Networks, Computer Machine Learning Mice Electroencephalography Signal Processing, Computer-Assisted Algorithms Animals Eye Movements Electromyography Sleep Stages
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Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network.

Network (Bristol, England) Aug 21, 2024
Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, time-consuming and difficult to achieve better accuracy. A DNN is suggested to recognize human hand ...
Movement Fourier Analysis Deep Learning Gestures Signal Processing, Computer-Assisted Humans Hand Electromyography Neural Networks, Computer Pattern Recognition, Automated Algorithms
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