Open AccessDeep-Learning Assisting Cerebral Palsy Patient Handgrip Task Translation
Author(s) -
Fazrul Faiz Zakaria,
Mohd Nazri Mohd Warip,
Phaklen Ehkan,
Muslim Mustapa,
Mohd Zaizu Ilyas
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1962/1/012047
Subject(s) - computer science , electroencephalography , brain–computer interface , artificial intelligence , convolutional neural network , deep learning , task (project management) , artificial neural network , interface (matter) , pattern recognition (psychology) , neuroscience , psychology , engineering , systems engineering , bubble , maximum bubble pressure method , parallel computing
An electro-encephalography (EEG) brain-computer interface (BCI) can provide the brain and external environment with separate information sharing and control networks. EEG impulses, though, come from many electrodes, which produce different characteristics, and how the electrodes and features to enhance classification efficiency have been chosen has become an urgent concern. This paper explores the deep convolutional neural network architecture (CNN) hyper-parameters with separating temporal and spatial filters without any pre-processing or artificial extraction processes. It selects the raw EEG signal of electrode pairs over the cortical area as hybrid samples. Our proposed deep-learning model outperforms other neural network models previously applied to this dataset in training time (∼40%) and accuracy (∼6%). Besides, considerations such as optimum order for EEG channels do not limit our model, and it is patient-invariant. The impact of network architecture on decoder output and training time is further discussed.