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2020 IEEE East-West Design & Test Symposium (EWDTS), 2020
With the recent development of technology and acquisition devices, the research of detection and classification utilizing EEG signals is rapidly increasing. One of the critical research in the field of the brain-computer interface includes an accurate detection of motor neuron behavior called motor imagery (MI) events.
Muhammad Yeamim Hossain +1 more
openaire +1 more source
With the recent development of technology and acquisition devices, the research of detection and classification utilizing EEG signals is rapidly increasing. One of the critical research in the field of the brain-computer interface includes an accurate detection of motor neuron behavior called motor imagery (MI) events.
Muhammad Yeamim Hossain +1 more
openaire +1 more source
2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), 2021
With the advancement of machine learning algorithms, the electroencephalogram (EEG) signal can be highly useful to identify the motor neuron activity (also known as motor imagery, MI event). Depending on the problem and experimental dataset/protocol, researchers use different classification algorithms to classify motor imagery events.
Muhammad Yeamim Hossain, Abul Sayeed
openaire +1 more source
With the advancement of machine learning algorithms, the electroencephalogram (EEG) signal can be highly useful to identify the motor neuron activity (also known as motor imagery, MI event). Depending on the problem and experimental dataset/protocol, researchers use different classification algorithms to classify motor imagery events.
Muhammad Yeamim Hossain, Abul Sayeed
openaire +1 more source
Annals of the New York Academy of Sciences
Abstract Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long‐term dependencies in sequential EEG data. Models like long short‐term memory and transformers improve performance but still face challenges of ...
Minghan Guo +6 more
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Abstract Deep learning has revolutionized electroencephalograph (EEG) decoding, with convolutional neural networks (CNNs) being a predominant tool. However, CNNs struggle with long‐term dependencies in sequential EEG data. Models like long short‐term memory and transformers improve performance but still face challenges of ...
Minghan Guo +6 more
openaire +2 more sources
2021
Electroencephalography (EEG) classification is an important part in brain-computer interface system. Motor imagery is a novel experimental paradigm that has been proved effective clinically in recognizing EEG from different limb motions. Our object is to finish motor imagery based EEG classification.
Yonghao Ren +3 more
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Electroencephalography (EEG) classification is an important part in brain-computer interface system. Motor imagery is a novel experimental paradigm that has been proved effective clinically in recognizing EEG from different limb motions. Our object is to finish motor imagery based EEG classification.
Yonghao Ren +3 more
openaire +1 more source
2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), 2017
Motor imagery based brain-computer interface (MI-BCI) has been proven to be effective in post-stroke rehabilitation. However, the low specificity of MI-BCI is usually associated with a mis-triggering problem, which means the irrelevant task would induce a false positive output.
Lichao Xu +7 more
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Motor imagery based brain-computer interface (MI-BCI) has been proven to be effective in post-stroke rehabilitation. However, the low specificity of MI-BCI is usually associated with a mis-triggering problem, which means the irrelevant task would induce a false positive output.
Lichao Xu +7 more
openaire +1 more source
IEEE Journal of Biomedical and Health Informatics
Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, it remains uncertain whether neural patterns different from the traditional inherent limbs motor imagery (MI) can be extracted, which is essential for high-dimensional control of external devices.
Zhuang Wang +7 more
openaire +2 more sources
Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, it remains uncertain whether neural patterns different from the traditional inherent limbs motor imagery (MI) can be extracted, which is essential for high-dimensional control of external devices.
Zhuang Wang +7 more
openaire +2 more sources
Physics-Informed Attention Temporal Convolutional Network for EEG-Based Motor Imagery Classification
IEEE Transactions on Industrial Informatics, 2023Hamdi Altaheri +2 more
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A shallow mirror transformer for subject-independent motor imagery BCI
Computers in Biology and Medicine, 2023Jing Luo, Zhenghao Shi
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