Non-invasive Brain-Computer Interfaces: State of the Art and Trends
Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to
widely influence research, clinical and recreational use. Non-invasive BCI approaches are …
widely influence research, clinical and recreational use. Non-invasive BCI approaches are …
A shallow mirror transformer for subject-independent motor imagery BCI
Objective: Motor imagery BCI plays an increasingly important role in motor disorders
rehabilitation. However, the position and duration of the discriminative segment in an EEG …
rehabilitation. However, the position and duration of the discriminative segment in an EEG …
A review on motor imagery with transcranial alternating current stimulation: bridging motor and cognitive welfare for patient rehabilitation
Research has shown the effectiveness of motor imagery in patient motor rehabilitation.
Transcranial electrical stimulation has also demonstrated to improve patient motor and non …
Transcranial electrical stimulation has also demonstrated to improve patient motor and non …
Interpretable and robust ai in eeg systems: A survey
The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …
substantially advanced human-computer interaction (HCI) technologies in the AI era …
Challenges of neural interfaces for stroke motor rehabilitation
C Vidaurre, N Irastorza-Landa… - Frontiers in Human …, 2023 - frontiersin.org
More than 85% of stroke survivors suffer from different degrees of disability for the rest of
their lives. They will require support that can vary from occasional to full time assistance …
their lives. They will require support that can vary from occasional to full time assistance …
SincMSNet: a Sinc filter convolutional neural network for EEG motor imagery classification
Objective. Motor imagery (MI) is widely used in brain-computer interfaces (BCIs). However,
the decode of MI-EEG using convolutional neural networks (CNNs) remains a challenge due …
the decode of MI-EEG using convolutional neural networks (CNNs) remains a challenge due …
SolareSkin: Self-powered Visible Light Sensing Through a Solar Cell E-Skin
SolareSkin is a self-powered and ubiquitous electronic skin equipped with ultraflexible
organic solar cells for visible light sensing and energy harvesting. This dual-functional …
organic solar cells for visible light sensing and energy harvesting. This dual-functional …
[HTML][HTML] A reference free non-negative adaptive learning system for health care monitoring and adaptive physiological artifact elimination in brain waves
CSL Prasanna, MZU Rahman - Healthcare Analytics, 2023 - Elsevier
Electroencephalogram (EEG), also referred to as brain wave (BW), is a physiological
phenomenon that depicts how the human brain functions. Brain wave analysis is …
phenomenon that depicts how the human brain functions. Brain wave analysis is …
Analysis two types of K complexes on the human EEG based on classical continuous wavelet transform
VB Dorokhov, A Runnova, ON Tkachenko… - … Journal of Nonlinear …, 2023 - pubs.aip.org
In our work, we compare EEG time–frequency features for two types of K-complexes
detected in volunteers performing the monotonous psychomotor test with their eyes closed …
detected in volunteers performing the monotonous psychomotor test with their eyes closed …
Automatic Diagnosis and Subtyping of Ischemic Stroke Based on a Multi-Dimensional Deep Learning System
X Mao, W Shan, J Yu - IEEE Transactions on Instrumentation …, 2024 - ieeexplore.ieee.org
The classification and subtyping of ischemic stroke play a significant role in therapeutic
decision-making, which leads to higher requirements on the infarct lesion location. This …
decision-making, which leads to higher requirements on the infarct lesion location. This …