EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification
Objective. Classification of electroencephalography (EEG)-based motor imagery (MI) is a
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
crucial non-invasive application in brain–computer interface (BCI) research. This paper …
Data augmentation for self-paced motor imagery classification with C-LSTM
D Freer, GZ Yang - Journal of neural engineering, 2020 - iopscience.iop.org
Objective. Brain–computer interfaces (BCI) are becoming important tools for assistive
technology, particularly through the use of motor imagery (MI) for aiding task completion …
technology, particularly through the use of motor imagery (MI) for aiding task completion …
Improving multi-class motor imagery EEG classification using overlapping sliding window and deep learning model
Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems.
MI tasks are performed by imagining doing a specific task and classifying MI through EEG …
MI tasks are performed by imagining doing a specific task and classifying MI through EEG …
Multi-class motor imagery EEG classification method with high accuracy and low individual differences based on hybrid neural network
J Liu, F Ye, H Xiong - Journal of Neural Engineering, 2021 - iopscience.iop.org
Objective. Most current methods of classifying different patterns for motor imagery EEG
signals require complex pre-processing and feature extraction steps, which consume time …
signals require complex pre-processing and feature extraction steps, which consume time …
EEG-based detection of mental workload level and stress: the effect of variation in each state on classification of the other
M Bagheri, SD Power - Journal of Neural Engineering, 2020 - iopscience.iop.org
Objective. A passive brain-computer interface (pBCI) is a system that continuously adapts
human-computer interaction to the user's state. Key to the efficacy of such a system is the …
human-computer interaction to the user's state. Key to the efficacy of such a system is the …
The neurophysiological basis of leadership: a machine learning approach
E Parra Vargas, J Philip, LA Carrasco-Ribelles… - Management …, 2023 - emerald.com
Purpose This research employed two neurophysiological techniques (
electroencephalograms (EEG) and galvanic skin response (GSR)) and machine learning …
electroencephalograms (EEG) and galvanic skin response (GSR)) and machine learning …
Classification of raw spinal cord injury EEG data based on the temporal-spatial inception deep convolutional neural network
H Mirzabagherian, MA Sardari… - 2021 9th RSI …, 2021 - ieeexplore.ieee.org
Today, decoding electroencephalography (EEG) data is an important and efficient
achievement in neuroscience and Brain-Computer Interface (BCI) systems. So that …
achievement in neuroscience and Brain-Computer Interface (BCI) systems. So that …
Subject wise motor imagery classification from eeg data using transfer learning
AA Khan, A Hassan, MT Jahangir - 2022 24th International …, 2022 - ieeexplore.ieee.org
Machine learning (ML) has no doubt virtually helped in nearly all fields of life, including
medical sciences. ML models are now being trained, tested and developed with the help of …
medical sciences. ML models are now being trained, tested and developed with the help of …
A convolutional neural network and stacked autoencoders approach for motor imagery based brain-computer interface
R Arabshahi, M Rouhani - 2020 10th International Conference …, 2020 - ieeexplore.ieee.org
In this research, we are investigating Convolutional Neural Networks (CNN) and Stacked
Auto Encoders (SAE) to classify EEG Motor Imagery signals. Also, we use Cohen Class …
Auto Encoders (SAE) to classify EEG Motor Imagery signals. Also, we use Cohen Class …
Brain-Computer Interface of Emotion and Motor Imagery Using 2D Convolutional Neural Network—Recurrent Neural Network
Brain-Computer Interface (BCI) allows human communication without gestures or muscle
movements with external devices. BCI works by translating commands from the brain into …
movements with external devices. BCI works by translating commands from the brain into …