Removal of artifacts from EEG signals: a review
Electroencephalogram (EEG) plays an important role in identifying brain activity and
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
behavior. However, the recorded electrical activity always be contaminated with artifacts and …
Separated channel convolutional neural network to realize the training free motor imagery BCI systems
In the recent context of Brain-computer interface (BCI), it has been widely known that
transferring the knowledge of existing subjects to a new subject can effectively alleviate the …
transferring the knowledge of existing subjects to a new subject can effectively alleviate the …
Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review
M Schmoigl-Tonis, C Schranz… - Frontiers in Human …, 2023 - frontiersin.org
Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing
communication between the human brain and external devices. Electroencephalography …
communication between the human brain and external devices. Electroencephalography …
The dynamic brain networks of motor imagery: time-varying causality analysis of scalp EEG
Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which
involves a large-scale network that spans multiple brain areas. The corresponding cortical …
involves a large-scale network that spans multiple brain areas. The corresponding cortical …
Motor imagery classification using mu and beta rhythms of EEG with strong uncorrelating transform based complex common spatial patterns
Recent studies have demonstrated the disassociation between the mu and beta rhythms of
electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this …
electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this …
Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network
Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for
rehabilitation of motor abilities and prosthesis control for patients with motor impairments …
rehabilitation of motor abilities and prosthesis control for patients with motor impairments …
A cross-space CNN with customized characteristics for motor imagery EEG classification
Y Hu, Y Liu, S Zhang, T Zhang, B Dai… - … on Neural Systems …, 2023 - ieeexplore.ieee.org
The classification of motor imagery-electroencephalogram (MI-EEG) based brain-computer
interface (BCI) can be used to decode neurological activities, which has been widely applied …
interface (BCI) can be used to decode neurological activities, which has been widely applied …
Robust support matrix machine for single trial EEG classification
Electroencephalogram (EEG) signals are of complex structure and can be naturally
represented as matrices. Classification is one of the most important steps for EEG signal …
represented as matrices. Classification is one of the most important steps for EEG signal …
Diagnosis of attention deficit hyperactivity disorder using non‐linear analysis of the EEG signal
YK Boroujeni, AA Rastegari… - IET systems biology, 2019 - Wiley Online Library
Attention deficit hyperactivity disorder (ADHD) is a common behavioural disorder that may
be found in 5%–8% of the children. Early diagnosis of ADHD is crucial for treating the …
be found in 5%–8% of the children. Early diagnosis of ADHD is crucial for treating the …
Comparing different classifiers in sensory motor brain computer interfaces
H Bashashati, RK Ward, GE Birch, A Bashashati - PloS one, 2015 - journals.plos.org
A problem that impedes the progress in Brain-Computer Interface (BCI) research is the
difficulty in reproducing the results of different papers. Comparing different algorithms at …
difficulty in reproducing the results of different papers. Comparing different algorithms at …