An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand
| Autor: | Niels Birbaumer, Matthias Witkowski, Surjo R. Soekadar, Nicola Vitiello |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Adult
Male medicine.medical_specialty Engineering Movement Biomedical Engineering Electroencephalography Assistive brain-machine interface (BMI) Biosignal fusion Brachial plexus injury (BPI) Electrooculography (EOG) Sensori-motor rhythms (SMR) Physical medicine and rehabilitation Computer Systems medicine Humans Paralysis Exoskeleton Device Brain–computer interface Neural computer medicine.diagnostic_test business.industry Brain Signal Processing Computer-Assisted Electrooculography Robotics Hybrid approach Hand Surgery Exoskeleton Improved performance Brain-Computer Interfaces business |
| Zdroj: | Biomedizinische Technik. Biomedical engineering. 60(3) |
| ISSN: | 1862-278X |
| Popis: | The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable control of assistive robotics is still an unsolved challenge. Here, we introduce a novel brain/neural-computer interaction (BNCI) system that integrates electroencephalography (EEG) and electrooculography (EOG) to improve control of assistive robotics in daily life environments. To evaluate the applicability and performance of this hybrid approach, five healthy volunteers (HV) (four men, average age 26.5±3.8 years) and a 34-year-old patient with complete finger paralysis due to a brachial plexus injury (BPI) used EEG (condition 1) and EEG/EOG (condition 2) to control grasping motions of a hand exoskeleton. All participants were able to control the BNCI system (BNCI control performance HV: 70.24±16.71%, BPI: 65.93±24.27%), but inclusion of EOG significantly improved performance across all participants (HV: 80.65±11.28, BPI: 76.03±18.32%). This suggests that hybrid BNCI systems can achieve substantially better control over assistive devices, e.g., a hand exoskeleton, than systems using brain signals alone and thus may increase applicability of brain-controlled assistive devices in daily life environments. |
| Databáze: | OpenAIRE |
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