Dendrite morphological neural networks for motor task recognition from electroencephalographic signals

2018 ◽  
Vol 44 ◽  
pp. 12-24 ◽  
Author(s):  
Javier M. Antelis ◽  
Berenice Gudiño-Mendoza ◽  
Luis Eduardo Falcón ◽  
Gildardo Sanchez-Ante ◽  
Humberto Sossa
Keyword(s):  
Neural Networks ◽  
Motor Task ◽  
Task Recognition ◽  
Electroencephalographic Signals ◽  
Morphological Neural Networks

scholarly journals Bipolar Morphological Neural Networks: Gate-Efficient Architecture for Computer Vision

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Elena E. Limonova ◽  
Daniil M. Alfonso ◽  
Dmitry P. Nikolaev ◽  
Vladimir V. Arlazarov
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Morphological Neural Networks

scholarly journals A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Jinlong Hu ◽  
Yuezhen Kuang ◽  
Bin Liao ◽  
Lijie Cao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Imaging ◽  
Motor Task ◽  
Data Classification ◽  
Fmri Data ◽  
Imaging Data ◽  
Whole Brain ◽  
Human Connectome Project ◽  
Brain Imaging Data ◽  
3D Cnn

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.

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