Motor imagery EEG signals decoding by multivariate empirical wavelet transform-based framework for robust brain–computer interfaces
The robustness and computational load are the key challenges in motor imagery (MI) based
on electroencephalography (EEG) signals to decode for the development of practical brain …
on electroencephalography (EEG) signals to decode for the development of practical brain …
[HTML][HTML] Evaluation of machine learning algorithms for classification of EEG signals
In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the
accuracy of the classification of motor movements. Machine learning (ML) algorithms such …
accuracy of the classification of motor movements. Machine learning (ML) algorithms such …
Emotion recognition and dynamic functional connectivity analysis based on EEG
X Liu, T Li, C Tang, T Xu, P Chen, A Bezerianos… - IEEE …, 2019 - ieeexplore.ieee.org
Although emotion recognition techniques have been well developed, the understanding of
the neural mechanism remains rudimentary. The traditional static network approach cannot …
the neural mechanism remains rudimentary. The traditional static network approach cannot …
Towards efficient decoding of multiple classes of motor imagery limb movements based on EEG spectral and time domain descriptors
To control multiple degrees of freedom (MDoF) upper limb prostheses, pattern recognition
(PR) of electromyogram (EMG) signals has been successfully applied. This technique …
(PR) of electromyogram (EMG) signals has been successfully applied. This technique …
Bio-signal based motion control system using deep learning models: A deep learning approach for motion classification using EEG and EMG signal fusion
H Aly, SM Youssef - Journal of Ambient Intelligence and Humanized …, 2023 - Springer
Bioelectrical time signals are the signals that can be measured through the electrical
potential difference across an organ over the time. Electroencephalography (EEG) signals …
potential difference across an organ over the time. Electroencephalography (EEG) signals …
An approach for brain-controlled prostheses based on a facial expression paradigm
R Li, X Zhang, Z Lu, C Liu, H Li, W Sheng… - Frontiers in …, 2018 - frontiersin.org
One of the most exciting areas of rehabilitation research is brain-controlled prostheses,
which translate electroencephalography (EEG) signals into control commands that operate …
which translate electroencephalography (EEG) signals into control commands that operate …
A channel selection approach based on convolutional neural network for multi-channel EEG motor imagery decoding
D Mzurikwao, OW Samuel, MG Asogbon… - 2019 IEEE Second …, 2019 - ieeexplore.ieee.org
For many disabled people, brain computer interface (BCI) may be the only way to
communicate with others and to control things around them. Using motor imagery paradigm …
communicate with others and to control things around them. Using motor imagery paradigm …
Hybrid brain computer interface for movement control of upper limb prostheses
Electroencephalography (EEG) and Electromyography (EMG) signals are playing significant
role in controlling bio-robotics applications, such as prostheses. Brain Computer Interfaces …
role in controlling bio-robotics applications, such as prostheses. Brain Computer Interfaces …
The effects of random stimulation rate on measurements of auditory brainstem response
Electroencephalography (EEG) signal is an electrophysiological recording from electrodes
placed on the scalp to reflect the electrical activities of the brain. Auditory brainstem …
placed on the scalp to reflect the electrical activities of the brain. Auditory brainstem …
Efficient channel selection approach for motor imaginary classification based on convolutional neural network
Brain Computer Interface (BCI) may be the only way to communicate and control for
disabled people. Someone's intention can be decoded from their brainwaves during motor …
disabled people. Someone's intention can be decoded from their brainwaves during motor …