Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
High-Density Surface Electromyography (HD-sEMG) enriches motion intention pattern recognition systems by providing more spatial information. Multichannel linear descriptors (MLD) could provide a comprehensive description of the overall state characteristics within the muscle regions. In this study, an MLD-based spatial feature extraction method was proposed to capture differences and correlations in various muscle regions during movement, ultimately enhancing the system's classification accuracy. The performance of the feature extraction method was compared with traditional time domain feature extraction method under various classifiers and different movement types. The results show that employing the proposed method with the spatial features improves the classification error rates of combined movements from 11.14% to 7.28%, and better adaptability for all classifiers utilized in this study, which shows the effect of utilization of spatial information in different muscle regions.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/EMBC53108.2024.10782222 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A systematic review of EEG-based machine learning classifications for obsessive-compulsive disorder: current status and future directions.
BMC Psychiatry
September 2025
Department of Cognitive Neuroscience, Faculty of Biology, Bielefeld University, Bielefeld, Germany.
Obsessive-compulsive disorder (OCD) is a chronic and disabling condition affecting approximately 3.5% of the global population, with diagnosis on average delayed by 7.1 years or often confounded with other psychiatric disorders.
View Article and Find Full Text PDFTPC-GCN: Deep learning for pulse pattern classification in traditional Chinese medicine.
Med Eng Phys
October 2025
College of Basic Medical Science, Shanxi University of Chinese Medicine, Jinzhong, 030619, Shanxi, China.
Pulse diagnosis holds a pivotal role in traditional Chinese medicine (TCM) diagnostics, with pulse characteristics serving as one of the critical bases for its assessment. Accurate classification of these pulse pattern is paramount for the objectification of TCM. This study proposes an enhanced SMOTE approach to achieve data augmentation, followed by multi-domain feature extraction.
View Article and Find Full Text PDFMultiTox: A sequence-based stacked ensemble model for multiclass protein toxin classification.
Int J Biol Macromol
September 2025
Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, India; Infosys Centre for Artificial Intelligence, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), Okhla Phase III, New Delhi, 110020, In
Understanding the structural and functional diversity of toxin proteins is critical for elucidating macromolecular behavior, mechanistic variability, and structure-driven bioactivity. Traditional approaches have primarily focused on binary toxicity prediction, offering limited resolution into distinct modes of action of toxins. Here, we present MultiTox, an ensemble stacking framework for the classification of toxin proteins based on their molecular mode of action: neurotoxins, cytotoxins, hemotoxins, and enterotoxins.
View Article and Find Full Text PDFMachine learning based classification of imagined speech electroencephalogram data from the amplitude and phase spectrum of frequency domain EEG signal.
Biomed Phys Eng Express
September 2025
electrical engineering department, Indian Institute of Technology Roorkee, Research wing, electrical department, Roorkee, uttrakhand, 247664, INDIA.
Imagined speech classification involves decoding brain signals to recognize verbalized thoughts or intentions without actual speech production. This technology has significant implications for individuals with speech impairments, offering a means to communicate through neural signals. The prime objective of this work is to propose an innovative machine learning (ML) based classification methodology that combines electroencephalogram (EEG) data augmentation using a sliding window technique with statistical feature extraction from the amplitude and phase spectrum of frequency domain EEG segments.
View Article and Find Full Text PDFReliability of fingerprint experts in extracting and evaluating minutiae in individualization tests of fingerprint traces.
J Forensic Leg Med
August 2025
Laboratory of Criminalistics, Adam Mickiewicz University in Poznań, al. Niepodległości 53, Poznań 61-714, Poland; Center for Advanced Technologies, Adam Mickiewicz University in Poznań, ul. Uniwersytetu Poznańskiego 10, Poznań 61-614, Poland.
This study examines the reliability of fingerprint experts in assessing the individualization value of minutiae during the analysis of latent fingerprint traces. Despite the widespread use of fingerprint evidence in criminal investigations, growing concerns about examiner variability and the lack of verification protocols have prompted critical scrutiny of forensic practices. In this study, 30 Polish fingerprint experts were asked to identify and evaluate seven minutiae in two fingerprint traces of differing quality.
View Article and Find Full Text PDF