Title |
EEG-Based Brain–Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century
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Published in |
Frontiers in Human Neuroscience, January 2018
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DOI | 10.3389/fnhum.2018.00014 |
Pubmed ID | |
Authors |
Ioulietta Lazarou, Spiros Nikolopoulos, Panagiotis C. Petrantonakis, Ioannis Kompatsiaris, Magda Tsolaki |
Abstract |
People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain-computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Greece | 3 | 27% |
Australia | 2 | 18% |
United States | 1 | 9% |
Unknown | 5 | 45% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 9 | 82% |
Scientists | 2 | 18% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 449 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 64 | 14% |
Student > Master | 62 | 14% |
Student > Bachelor | 59 | 13% |
Researcher | 45 | 10% |
Student > Doctoral Student | 15 | 3% |
Other | 68 | 15% |
Unknown | 136 | 30% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 103 | 23% |
Neuroscience | 44 | 10% |
Computer Science | 43 | 10% |
Medicine and Dentistry | 25 | 6% |
Nursing and Health Professions | 17 | 4% |
Other | 54 | 12% |
Unknown | 163 | 36% |