Titlebook: Brain-Computer Interface Research; A State-of-the-Art S Christoph Guger,Brendan Allison,Junichi Ushiba Book 2017 The Author(s) 2017 BCI Pri

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Brain-Machine Interface Development for Finger Movement Control,d, for the first time, online BMI control of individual finger movements using electrocorticography recordings from the hand area of sensorimotor cortex. This study expands the possibilities for combined control of arm movements and more dexterous hand and finger movements.

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Brain-Computer Interface Controlling Cyborg: A Functional Brain-to-Brain Interface Between Human ane average success rates of both human BCI and cyborg reactions in a single decision were over 85%. The cyborg could be steered successfully via the human brain to complete walking along pre-set tracks with a 20% success rate.

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,Estimation of Intracranial P300 Speller Sites with Magnetoencephalography (MEG)—Perspectives for Nodural electrode grids—80% and 90% averaged accuracy, respectively. Our study demonstrates the feasibility of using MEG as a non-invasive tool for navigating electrode implantation required for high accuracy invasive P300 speller control.

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Book 2017 developments from many of the best labs worldwide. They present both non-invasive systems (based on the EEG) and intracortical methods (based on spikes or ECoG), and numerous innovative applications that will benefit new user groups

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