Decoding speech perception from non-invasive brain recordings
Decoding speech from brain activity is a long-awaited goal in both healthcare and
neuroscience. Invasive devices have recently led to major milestones in this regard: deep …
neuroscience. Invasive devices have recently led to major milestones in this regard: deep …
Survey on the research direction of EEG-based signal processing
C Sun, C Mou - Frontiers in Neuroscience, 2023 - frontiersin.org
Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI)
systems due to its portability and simplicity. In this paper, we provide a comprehensive …
systems due to its portability and simplicity. In this paper, we provide a comprehensive …
Motor Imagery Signal Classification using Adversarial Learning: A systematic literature review
S Mishra, O Mahmudi, A Jalali - IEEE Access, 2024 - ieeexplore.ieee.org
This paper presents a comprehensive Systematic Literature Review (SLR) on the utilization
of adversarial learning techniques in Motor Imagery (MI) signal classification, a key …
of adversarial learning techniques in Motor Imagery (MI) signal classification, a key …
[HTML][HTML] ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data
O Ali, M Saif-ur-Rehman, T Glasmachers… - Computers in Biology …, 2024 - Elsevier
Abstract Objective Bio-Signals such as electroencephalography (EEG) and
electromyography (EMG) are widely used for the rehabilitation of physically disabled people …
electromyography (EMG) are widely used for the rehabilitation of physically disabled people …
Deep temporal networks for EEG-based motor imagery recognition
The electroencephalogram (EEG) based motor imagery (MI) signal classification, also
known as motion recognition, is a highly popular area of research due to its applications in …
known as motion recognition, is a highly popular area of research due to its applications in …
Deep transfer learning compared to subject-specific models for sEMG decoders
Objective. Accurate decoding of surface electromyography (sEMG) is pivotal for muscle-to-
machine-interfaces and their application eg rehabilitation therapy. sEMG signals have high …
machine-interfaces and their application eg rehabilitation therapy. sEMG signals have high …
Simultaneous EEG-fNIRS data classification through selective channel representation and spectrogram imaging
C Bunterngchit, J Wang, ZG Hou - IEEE Journal of Translational …, 2024 - ieeexplore.ieee.org
The integration of electroencephalography (EEG) and functional near-infrared spectroscopy
(fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However …
(fNIRS) can facilitate the advancement of brain-computer interfaces (BCIs). However …
Classification of post-covid-19 emotions with residual-based separable convolution networks and eeg signals
The COVID-19 epidemic has created highly unprocessed emotions that trigger stress,
anxiety, or panic attacks. These attacks exhibit physical symptoms that may easily lead to …
anxiety, or panic attacks. These attacks exhibit physical symptoms that may easily lead to …
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces
O Ali, M Saif-ur-Rehman, T Glasmachers… - arXiv preprint arXiv …, 2022 - arxiv.org
Objective: Electroencephalography (EEG) and electromyography (EMG) are two non-
invasive bio-signals, which are widely used in human machine interface (HMI) technologies …
invasive bio-signals, which are widely used in human machine interface (HMI) technologies …
A lightweight CNN for wind turbine blade defect detection based on spectrograms
Y Zhu, X Liu - Machines, 2023 - mdpi.com
Since wind turbines are exposed to harsh working environments and variable weather
conditions, wind turbine blade condition monitoring is critical to prevent unscheduled …
conditions, wind turbine blade condition monitoring is critical to prevent unscheduled …