ArticleJournal of electrical bioimpedance2023
Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis.
Article in Journal of electrical bioimpedance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Decoding pre-movement neural activity from thalamic LFPs for adaptive neurostimulation in tremor patients.Neurocomputing · 2026Article
- Neural Efficiency and Attentional Instability in Gaming Disorder: A Task-Based Occipital EEG and Machine Learning Study.Bioengineering (Basel, Switzerland) · 2026Article
- Child behavior recognition in social robot interaction using stacked deep neural networks and biomechanical signals.Scientific reports · 2025Article
- RS-STGCN: Regional-Synergy Spatio-Temporal Graph Convolutional Network for emotion recognition.Frontiers in neuroscience · 2025Article
- UAV propeller fault diagnosis using deep learning of non-traditional χScientific reports · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomedical engineering stands at the forefront of medical innovation, with electroencephalography (EEG) signal analysis providing critical insights into neural functions. This paper delves into the utilization of EEG signals within the MILimbEEG dataset to explore their potential for machine learning-based task recognition and diagnosis. Capturing the brain's electrical activity through electrodes 1 to 16, the signals are recorded in the time-domain in microvolts. An advanced feature extraction methodology harnessing Hjorth Parameters-namely Activity, Mobility, and Complexity-is employed to analyze the acquired signals. Through correlation analysis and examination of clustering behaviors, the study presents a comprehensive discussion on the emergent patterns within the data. The findings underscore the potential of integrating these features into machine learning algorithms for enhanced diagnostic precision and task recognition in biomedical applications. This exploration paves the way for future research where such signal processing techniques could revolutionize the efficiency and accuracy of biomedical engineering diagnostics.
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Registered trials
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