scholarly journals A Novel Deep Learning Scheme for Motor Imagery EEG Decoding Based on Spatial Representation Fusion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 202100-202110
Author(s):  
Jun Yang ◽  
Zhengmin Ma ◽  
Jin Wang ◽  
Yunfa Fu
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Spatial Representation ◽  
Learning Scheme

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

2019 ◽  
Vol 36 (6) ◽  
Author(s):  
Ahmad Hassanpour ◽  
Majid Moradikia ◽  
Hojjat Adeli ◽  
Seyed Raouf Khayami ◽  
Pirooz Shamsinejadbabaki
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Learning Scheme ◽  
End To End

A novel hybrid deep learning scheme for four-class motor imagery classification

2019 ◽  
Vol 16 (6) ◽  
pp. 066004 ◽  
Author(s):  
Ruilong Zhang ◽  
Qun Zong ◽  
Liqian Dou ◽  
Xinyi Zhao
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Learning Scheme

Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: a review

2021 ◽  
Author(s):  
Hamdi Altaheri ◽  
Ghulam Muhammad ◽  
Mansour Alsulaiman ◽  
Syed Umar Amin ◽  
Ghadir Ali Altuwaijri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Learning Techniques ◽  
Electroencephalogram Eeg

scholarly journals Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 210 ◽  
Author(s):  
Zied Tayeb ◽  
Juri Fedjaev ◽  
Nejla Ghaboosi ◽  
Christoph Richter ◽  
Lukas Everding ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Motor Imagery ◽  
Classification Performance ◽  
Feature Engineering ◽  
Learning Models ◽  
Eeg Signals ◽  
Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

scholarly journals A Clonal Selection Optimization System for Multiparty Secure Computing

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Minyu Shi ◽  
Yongting Zhang ◽  
Huanhuan Wang ◽  
Junfeng Hu ◽  
Xiang Wu
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Differential Privacy ◽  
Clonal Selection ◽  
Smart Cities ◽  
Model Performance ◽  
Processing System ◽  
Optimization System ◽  
Learning Scheme ◽  
Training Efficiency

The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.

Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

2018 ◽  
Vol 15 (3) ◽  
pp. 036028 ◽  
Author(s):  
Antonio Maria Chiarelli ◽  
Pierpaolo Croce ◽  
Arcangelo Merla ◽  
Filippo Zappasodi
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Brain Computer Interface ◽  
Computer Interface
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