An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand

Biomedizinische Technik - Tập 60 Số 3 - 2015
Surjo R. Soekadar1, Matthias Witkowski1,2,3,4,5,6,7, Nicola Vitiello3,6, Niels Birbaumer1,2,3,4,5,6,7
1Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital of Tübingen, Calwerstr. 14, 72076 Tübingen, Germany
2Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital of Tübingen, Calwerstr. 14, 72076 Tübingen, Germany; and Institute of Medical Psychology and Behavioral Neurobiology,
3Fondazione Don Carlo Gnocchi, Center of Florence, via di Scandicci 256, 50143 Firenze, Italy
4Niels Birbaumer: Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Silcherstr. 5, 72076 Tübingen, Germany; and
5Ospedale San Camillo -IRCCS, Istituto di Ricovero e Cura a Carattere Scientifico, Venezia-Lido, Italy
6The BioRobotics Institute, Scuola Superiore Sant’Anna, viale Rinaldo Piaggio, 34, 56025 Pontedera (Pisa), Italy
7University of Tübingen, Silcherstr. 5, 72076 Tübingen, Germany

Tóm tắt

Abstract

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.

Từ khóa


Tài liệu tham khảo

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London, 1943, Medical Research Council to the investigation of peripheral nerve injuries nd ed Her Majesty s Stationery Office, Aids

Pfurtscheller, 1979, Evaluation of event - related desynchronization preceding and following self - paced movement Electroencephalogr, Clin Neurophysiol, 138, 10.1016/0013-4694(79)90063-4

Soekadar, machine interfaces in neurorehabilitation of stroke in press, Brain Neurobiol Dis, 2015

Matsumoto, 2012, of EMG signals of patients with essential tremor focusing on the change of tremor frequency, Analysis Conf Proc IEEE Eng Med Biol Soc, 2244

Millan, 2009, Del Asynchronous non - invasive brain - activated control of an intelligent wheelchair, Conf Proc IEEE Eng Med Biol Soc, 3361

Collinger, 2013, Functional priorities assistive technology computer interfaces after spinal cord injury, brain J Rehabil Res Dev, 145, 10.1682/JRRD.2011.11.0213

Pfurtscheller, 2003, - control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia, Thought Neurosci Lett, 351

Pfurtscheller, 2000, oscillations control hand orthosis in a tetraplegic, Brain Neurosci Lett, 292

BNCI, Future a roadmap for future directions in brain / neuronal computer interaction research Future BNCI Program under the European Union Seventh Framework Programme grant http www brainable org Documents Future BNCI Roadmap pdf, 2007

Müller, 2005, - based neuroprosthesis control a step towards clinical practice, Neurosci Lett, 382

Rupp, 2013, Schneiders Think grasp controlled neuroprosthesis for the upper extremity in press, Biomed Tech, 10.1515/bmt-2013-4440

Soekadar, 2011, based online brain - machine interfaces BMI in the context of optimizing BMI learning and performance, neurorehabilitation IEEE, 19, 542

Müller, 2014, Motor imagery - induced EEG patterns in individuals with spinal cord injury and their impact on brain - computer interface accuracy, J Neural Eng, 11

Hargrove, 2013, Robotic leg control with EMG decoding in an amputee with nerve transfers, AM Young Engl J Med, 369

Rohm, 2013, Schneiders Müller Hybrid brain - computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high - level spinal cord injury, Artif Intell Med, 59

Millán, 2010, Combining brain computer interfaces and assistive technologies state of the art and challenges Front, Neurosci, 161