AIMC Topic: Signal Processing, Computer-Assisted

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Applied machine learning for nociceptive pain detection using EEG spectral features.

Biomedical physics & engineering express
. This study explores a more reliable method for measuring nociceptive pain induced by laser stimuli from electroencephalography (EEG) signals, addressing the limitations of fixed pain scales by incorporating inter-individual variability in subjectiv...

Classification of cardiac electrical signals between patients with myocardial infarction and healthy controls by using time-frequency features and 3D convolutional neural networks.

Biomedical physics & engineering express
Electrocardiogram (ECG) signal classification plays an important role in myocardial infarction (MI) detection and screening. Despite that much progress has been made, the interpretation of ECG signals is still extremely time-consuming, and heavily re...

A Feature Extraction and Selection Framework for Electrocorticography-Based Neural Activity Classification.

Journal of medical systems
Electrocorticography (ECoG) signals provide a valuable window into neural activity, yet their complex structure makes reliable classification challenging. This study addresses the problem by proposing a feature-selective framework that integrates mul...

AI-based approach for heart failure readmission prediction using SCG, ECG, and GSR signals.

Physiological measurement
Heart failure (HF) is considered a global pandemic because of increasing prevalence, high mortality rate, frequent hospitalization, and associated economic burden. This study explores a noninvasive method that may help in managing HF patients by pred...

Machine learning-enabled acoustic sensing for RPW infestation detection.

Scientific reports
Red Palm Weevil(RPW) infestation is a major challenge in palm production, often remaining undetected until severe internal damage has occurred. This proposed work presents a novel non-invasive auditory detection system that combines advanced signal p...

A novel channel reduction concept to enhance the classification of motor imagery tasks in brain-computer interface systems.

PloS one
Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redunda...

PhysioFormer: Integrating multimodal physiological signals and symbolic regression for explainable affective state prediction.

PloS one
As affective computing becomes increasingly crucial in health monitoring and psychological intervention, accurately identifying affective states is a key challenge. While traditional machine learning models have achieved some success in affective com...

Identifying EEG-based neurobehavioral risk markers of gaming addiction using machine learning and iowa gambling task.

Biomedical physics & engineering express
Internet Gaming Disorder (IGD), Gaming Disorder (GD), and Internet Addiction represent behavioral patterns with significant psychological and neurological consequences. Affected individuals often disengage from routine activities and exhibit distress...

Decoding covert visual attention of electroencephalography signals using continuous wavelet transform and deep learning approach.

Scientific reports
Covert visual attention decoding from EEG signals is a key challenge in cognitive neuroscience and brain-computer interface applications. Traditional approaches often rely on manual feature extraction and handcrafted pipelines, which limit scalabilit...

Detection of cognitive load using EEG signal and lifting wavelet transform with specific lead selection.

Biomedical physics & engineering express
Solving an arithmetic task is a complex assignment that includes sequencing, memory, fact retrieval, and decision making. Observation of the human brain's response to such activities is quite essential as it helps in the diagnosis of various diseases...