Smart tactile gloves for haptic interaction, communication, and rehabilitation
Wearable human machine interfaces (HMI) such as smart gloves have attracted
considerable interest in recent years. The quality of the interactive experience with the real …
considerable interest in recent years. The quality of the interactive experience with the real …
EEG channel selection techniques in motor imagery applications: a review and new perspectives
Communication, neuro-prosthetics, and environmental control are just a few applications for
disabled persons who use robots and manipulators that use brain-computer interface (BCI) …
disabled persons who use robots and manipulators that use brain-computer interface (BCI) …
Graph convolution neural network based end-to-end channel selection and classification for motor imagery brain–computer interfaces
B Sun, Z Liu, Z Wu, C Mu, T Li - IEEE transactions on industrial …, 2022 - ieeexplore.ieee.org
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in
brain–computer interface (BCI). EEG signals require a large number of channels in the …
brain–computer interface (BCI). EEG signals require a large number of channels in the …
A model combining multi branch spectral-temporal CNN, Efficient Channel attention, and LightGBM for MI-BCI classification
Accurately decoding motor imagery (MI) brain-computer interface (BCI) tasks has remained
a challenge for both neuroscience research and clinical diagnosis. Unfortunately, less …
a challenge for both neuroscience research and clinical diagnosis. Unfortunately, less …
Golden subject is everyone: A subject transfer neural network for motor imagery-based brain computer interfaces
Electroencephalographic measurement of cortical activity subserving motor behavior varies
among different individuals, restricting the potential of brain computer interfaces (BCIs) …
among different individuals, restricting the potential of brain computer interfaces (BCIs) …
SincNet-based hybrid neural network for motor imagery EEG decoding
It is difficult to identify optimal cut-off frequencies for filters used with the common spatial
pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most …
pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most …
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 …
An efficient EEG signal classification technique for Brain–Computer Interface using hybrid Deep Learning
Differently-abled individuals always need support from others for their day-to-day activities.
Brain Computer Interface (BCI) has the potential to help those people in carrying out the …
Brain Computer Interface (BCI) has the potential to help those people in carrying out the …
Adaptive spatiotemporal graph convolutional networks for motor imagery classification
B Sun, H Zhang, Z Wu, Y Zhang… - IEEE Signal Processing …, 2021 - ieeexplore.ieee.org
Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in
brain computer interfaces (BCI). In view of the characteristics of non-stationarity, time …
brain computer interfaces (BCI). In view of the characteristics of non-stationarity, time …
An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task
Objective. Motor-imagery (MI) classification base on electroencephalography (EEG) has
been long studied in neuroscience and more recently widely used in healthcare applications …
been long studied in neuroscience and more recently widely used in healthcare applications …