A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG-Reference-Cited by-同舟云学术

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A fingertip force prediction model for grasp patterns characterised from the chaotic behaviour of EEG

Published:2018-05-19 Issue:11 Volume:56 Page:2095-2107
ISSN:0140-0118
Container-title:Medical & Biological Engineering & Computing
language:en
Short-container-title:Med Biol Eng Comput

Author:

Roy Rinku,Sikdar Debdeep,Mahadevappa Manjunatha,Kumar C. S.

Funder

Department of Science and Technology, Ministry of Science and Technology

SRIC, IIT KHARAGPUR,Sponsored Research & Industrial Consultancy, IIT Kharagpur

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering

Link

http://link.springer.com/article/10.1007/s11517-018-1833-0/fulltext.html

Reference42 articles.

1. Acharya S, Fifer MS, Benz HL, Crone NE, Thakor NV (2010) Electrocorticographic amplitude predicts finger positions during slow grasping motions of the hand. J Neural Eng 7(4):046,002

2. Agashe HA, Contreras-Vidal JL (2013) Decoding the evolving grasping gesture from electroencephalographic (EEG) activity. In: 2013 35th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 5590–5593

3. Agashe HA, Paek AY, Zhang Y, Contreras-Vidal JL (2015) Global cortical activity predicts shape of hand during grasping. Front Neurosci 9:121

4. Benmouiza K, Cheknane A (2013) Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models. Energy Convers Manag 75:561–569

5. Bennett KP, Campbell C (2000) Support vector machines: hype or hallelujah? Acm Sigkdd Explor Newslett 2(2):1–13

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