[HTML][HTML] Adazd-Net: Automated adaptive and explainable Alzheimer's disease detection system using EEG signals
Background: Alzheimer's disease (AZD) is a degenerative neurological condition that
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …
causes dementia and leads the brain to atrophy. Although AZD cannot be cured, early …
Power‐Efficient Multisensory Reservoir Computing Based on Zr‐Doped HfO2 Memcapacitive Synapse Arrays
M Pei, Y Zhu, S Liu, H Cui, Y Li, Y Yan, Y Li… - Advanced …, 2023 - Wiley Online Library
Hardware implementation tailored to requirements in reservoir computing would facilitate
lightweight and powerful temporal processing. Capacitive reservoirs would boost power …
lightweight and powerful temporal processing. Capacitive reservoirs would boost power …
Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients
P Subramani, SK, P BD - Personal and ubiquitous computing, 2023 - Springer
Abstract Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients
experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps …
experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps …
Hand gesture classification using time–frequency images and transfer learning based on CNN
Hand gesture-based systems are one of the most effective technological advances and
continue to develop with improvements in the field of human–computer interaction. Surface …
continue to develop with improvements in the field of human–computer interaction. Surface …
[HTML][HTML] Machine learning-based feature extraction and classification of emg signals for intuitive prosthetic control
Signals play a fundamental role in science, technology, and communication by conveying
information through varying patterns, amplitudes, and frequencies. This paper introduces …
information through varying patterns, amplitudes, and frequencies. This paper introduces …
Machine-learning-enabled adaptive signal decomposition for a brain-computer interface using EEG
Background and objective The use of adaptive signal decomposition methods and machine
learning (ML) algorithms have gained interest in biomedical applications. Brain-computer …
learning (ML) algorithms have gained interest in biomedical applications. Brain-computer …
Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches
Surface Electromyography (sEMG) has become an essential tool in various fields, including
prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent …
prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent …
All-weather, natural silent speech recognition via machine-learning-assisted tattoo-like electronics
The internal availability of silent speech serves as a translator for people with aphasia and
keeps human–machine/human interactions working under various disturbances. This paper …
keeps human–machine/human interactions working under various disturbances. This paper …
Hand movement recognition from sEMG signals using Fourier decomposition method
Surface electromyogram (sEMG) provides a non-invasive way to collect EMG signals. The
sEMG signals acquired from the muscles of the forearm can be used to recognize the hand …
sEMG signals acquired from the muscles of the forearm can be used to recognize the hand …
Deep-learning-based BCI for automatic imagined speech recognition using SPWVD
The electroencephalogram (EEG)-based brain–computer interface (BCI) has potential
applications in neuroscience and rehabilitation. It benefits a person with neurological …
applications in neuroscience and rehabilitation. It benefits a person with neurological …