Journal
BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK
Volume 60, Issue 3, Pages 199-205Publisher
WALTER DE GRUYTER GMBH
DOI: 10.1515/bmt-2014-0126
Keywords
assistive brain-machine interface (BMI); biosignal fusion; brachial plexus injury (BPI); electrooculography (EOG); sensori-motor rhythms (SMR)
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Funding
- German Federal Ministry of Education and Research (BMBF) [01GQ0831, 16SV5838K]
- European Commission [288551, 609593]
- Deutsche Forschungsgemeinschaft (DFG) [SO932-2]
- Reinhart Koselleck Project
- Volkswagenstiftung
- Baden-Wurttemberg Stiftung
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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.
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