Deep Learning for Grasp-and-Lift Movement Forecasting Based on Electroencephalography by Brain-Computer Interface

[1]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[2]  Ali Farhadi,et al.  AJILE Movement Prediction: Multimodal Deep Learning for Natural Human Neural Recordings and Video , 2017, AAAI.

[3]  S. Mutalib,et al.  Prediction of Mental Health Problems among Higher Education Student Using Machine Learning , 2020 .

[4]  Brent Lance,et al.  EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces , 2016, Journal of neural engineering.

[5]  Peng Gang,et al.  Parallel Statistical and Machine Learning Methods for Estimation of Physical Load , 2018, ICA3PP.

[6]  Katarzyna Stapor,et al.  Deep Learning Methods in Electroencephalography , 2020 .

[7]  Mads Jochumsen,et al.  Convolutional Neural Networks Improve the Prediction of Hand Movement Speed and Force from Single-trial EEG , 2019 .

[8]  Gilles Fedak,et al.  Synergy of volunteer measurements and volunteer computing for effective data collecting, processing, simulating and analyzing on a worldwide scale , 2015, 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[9]  Andre Esteva,et al.  A guide to deep learning in healthcare , 2019, Nature Medicine.

[10]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[11]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[12]  Yuriy Kochura,et al.  Chapter Nine - "Last mile" optimization of edge computing ecosystem with deep learning models and specialized tensor processing architectures , 2021, Adv. Comput..

[13]  Yuri Gordienko,et al.  User-driven Intelligent Interface on the Basis of Multimodal Augmented Reality and Brain-Computer Interaction for People with Functional Disabilities , 2017, Advances in Intelligent Systems and Computing.

[14]  S. Stober,et al.  Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control , 2020, Communications Biology.

[15]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[16]  Ewa Jarocka,et al.  Multi-channel EEG recordings during 3,936 grasp and lift trials with varying weight and friction , 2014, Scientific Data.

[17]  Feng Lin,et al.  A novel multi-dimensional features fusion algorithm for the EEG signal recognition of brain's sensorimotor region activated tasks , 2020, Int. J. Intell. Comput. Cybern..

[18]  Chin-Teng Lin,et al.  EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Yuri Gordienko,et al.  Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions , 2017, ArXiv.

[21]  Tiago H. Falk,et al.  Deep learning-based electroencephalography analysis: a systematic review , 2019, Journal of neural engineering.

[22]  Pablo Tamayo,et al.  Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies , 2014, Scientific Data.

[23]  Peng Gang,et al.  Prediction of Physical Load Level by Machine Learning Analysis of Heart Activity after Exercises , 2019, 2019 IEEE Symposium Series on Computational Intelligence (SSCI).

[24]  Honghao Gao,et al.  A Multi-Scale Activity Transition Network for Data Translation in EEG Signals Decoding , 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.