Title |
Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG)
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Published in |
Journal of NeuroEngineering and Rehabilitation, December 2014
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DOI | 10.1186/1743-0003-11-165 |
Pubmed ID | |
Authors |
Matthias Witkowski, Mario Cortese, Marco Cempini, Jürgen Mellinger, Nicola Vitiello, Surjo R Soekadar |
Abstract |
Brain-machine interfaces (BMIs) allow direct translation of electric, magnetic or metabolic brain signals into control commands of external devices such as robots, prostheses or exoskeletons. However, non-stationarity of brain signals and susceptibility to biological or environmental artifacts impede reliable control and safety of BMIs, particularly in daily life environments. Here we introduce and tested a novel hybrid brain-neural computer interaction (BNCI) system fusing electroencephalography (EEG) and electrooculography (EOG) to enhance reliability and safety of continuous hand exoskeleton-driven grasping motions. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Austria | 1 | 25% |
Russia | 1 | 25% |
United States | 1 | 25% |
Italy | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 2% |
Hungary | 1 | <1% |
Germany | 1 | <1% |
Egypt | 1 | <1% |
Unknown | 143 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 29 | 19% |
Researcher | 21 | 14% |
Student > Ph. D. Student | 21 | 14% |
Student > Bachelor | 10 | 7% |
Student > Doctoral Student | 8 | 5% |
Other | 18 | 12% |
Unknown | 42 | 28% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 50 | 34% |
Neuroscience | 12 | 8% |
Computer Science | 11 | 7% |
Agricultural and Biological Sciences | 5 | 3% |
Psychology | 4 | 3% |
Other | 15 | 10% |
Unknown | 52 | 35% |