151
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Vu PP, Chestek CA, Nason SR, Kung TA, Kemp SW, Cederna PS. The future of upper extremity rehabilitation robotics: research and practice. Muscle Nerve 2020; 61:708-718. [PMID: 32413247 PMCID: PMC7868083 DOI: 10.1002/mus.26860] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 03/03/2020] [Accepted: 03/03/2020] [Indexed: 01/14/2023]
Abstract
The loss of upper limb motor function can have a devastating effect on people's lives. To restore upper limb control and functionality, researchers and clinicians have developed interfaces to interact directly with the human body's motor system. In this invited review, we aim to provide details on the peripheral nerve interfaces and brain-machine interfaces that have been developed in the past 30 years for upper extremity control, and we highlight the challenges that still remain to transition the technology into the clinical market. The findings show that peripheral nerve interfaces and brain-machine interfaces have many similar characteristics that enable them to be concurrently developed. Decoding neural information from both interfaces may lead to novel physiological models that may one day fully restore upper limb motor function for a growing patient population.
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Affiliation(s)
- Philip P. Vu
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
- Section of Plastic Surgery, University of Michigan, Ann Arbor, Michigan
| | - Cynthia A. Chestek
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
- Robotics Institute, University of Michigan, Ann Arbor, Michigan
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, Michigan
| | - Samuel R. Nason
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
| | - Theodore A. Kung
- Section of Plastic Surgery, University of Michigan, Ann Arbor, Michigan
| | - Stephen W.P. Kemp
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
- Section of Plastic Surgery, University of Michigan, Ann Arbor, Michigan
| | - Paul S. Cederna
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan
- Section of Plastic Surgery, University of Michigan, Ann Arbor, Michigan
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152
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Focus on better care and ethics: Are medical ethics lagging behind the development of new medical technologies? Intensive Care Med 2020; 46:1611-1613. [PMID: 32462323 PMCID: PMC7251219 DOI: 10.1007/s00134-020-06112-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 05/11/2020] [Indexed: 01/29/2023]
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153
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Jeong JH, Shim KH, Kim DJ, Lee SW. Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1226-1238. [DOI: 10.1109/tnsre.2020.2981659] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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154
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Sardi S, Vardi R, Meir Y, Tugendhaft Y, Hodassman S, Goldental A, Kanter I. Brain experiments imply adaptation mechanisms which outperform common AI learning algorithms. Sci Rep 2020; 10:6923. [PMID: 32327697 PMCID: PMC7181840 DOI: 10.1038/s41598-020-63755-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 03/31/2020] [Indexed: 11/09/2022] Open
Abstract
Attempting to imitate the brain's functionalities, researchers have bridged between neuroscience and artificial intelligence for decades; however, experimental neuroscience has not directly advanced the field of machine learning (ML). Here, using neuronal cultures, we demonstrate that increased training frequency accelerates the neuronal adaptation processes. This mechanism was implemented on artificial neural networks, where a local learning step-size increases for coherent consecutive learning steps, and tested on a simple dataset of handwritten digits, MNIST. Based on our on-line learning results with a few handwriting examples, success rates for brain-inspired algorithms substantially outperform the commonly used ML algorithms. We speculate this emerging bridge from slow brain function to ML will promote ultrafast decision making under limited examples, which is the reality in many aspects of human activity, robotic control, and network optimization.
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Affiliation(s)
- Shira Sardi
- Department of Physics, Bar-Ilan University, Ramat-Gan, 52900, Israel
| | - Roni Vardi
- Gonda Interdisciplinary Brain Research Center and the Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel
| | - Yuval Meir
- Department of Physics, Bar-Ilan University, Ramat-Gan, 52900, Israel
| | - Yael Tugendhaft
- Department of Physics, Bar-Ilan University, Ramat-Gan, 52900, Israel
| | - Shiri Hodassman
- Department of Physics, Bar-Ilan University, Ramat-Gan, 52900, Israel
| | - Amir Goldental
- Department of Physics, Bar-Ilan University, Ramat-Gan, 52900, Israel
| | - Ido Kanter
- Department of Physics, Bar-Ilan University, Ramat-Gan, 52900, Israel.
- Gonda Interdisciplinary Brain Research Center and the Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel.
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155
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Tang T, Goh WL, Yao L, Cheong JH, Gao Y. An Integrated Multi-Channel Biopotential Recording Analog Front-End IC With Area-Efficient Driven-Right-Leg Circuit. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:297-304. [PMID: 31831435 DOI: 10.1109/tbcas.2019.2959412] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A multi-channel biopotential recording analog front-end (AFE) with a fully integrated area-efficient driven-right-leg (DRL) circuit is presented in this paper. The proposed AFE includes 10 channels of low-noise capacitive coupled instrumentation amplifier (CCIA), one shared 10-bit SAR ADC and a fully integrated DRL to enhance the system-level common-mode rejection ratio (CMRR). The proposed DRL circuit senses the common-mode at the CCIA output so that the AFE gain is reused as the DRL loop gain. Therefore, area efficient unit-gain buffer with small averaging capacitors can be used in DRL circuit to reduce the circuit area significantly. The proposed AFE has been implemented in a standard 0.18-μm CMOS process. The DRL circuit achieved more than 85% chip area reduction compared to the state-of-art on-chip DRL circuits and maximum 60 dB enhancement to system-level CMRR. Measurement results show high/low AFE gain of 60 dB/54 dB respectively with 1 μA/channel current consumption under 1.0 V power supply. The measured AFE input-referred noise in 1 Hz - 10k Hz range is 4.2 μVrms and the maximum system-level CMRR is 110 dB.
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156
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Amo Usanos C, Boquete L, de Santiago L, Barea Navarro R, Cavaliere C. Induced Gamma-Band Activity during Actual and Imaginary Movements: EEG Analysis. SENSORS 2020; 20:s20061545. [PMID: 32168747 PMCID: PMC7146111 DOI: 10.3390/s20061545] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 03/03/2020] [Accepted: 03/10/2020] [Indexed: 11/16/2022]
Abstract
The purpose of this paper is to record and analyze induced gamma-band activity (GBA) (30-60 Hz) in cerebral motor areas during imaginary movement and to compare it quantitatively with activity recorded in the same areas during actual movement using a simplified electroencephalogram (EEG). Brain activity (basal activity, imaginary motor task and actual motor task) is obtained from 12 healthy volunteer subjects using an EEG (Cz channel). GBA is analyzed using the mean power spectral density (PSD) value. Event-related synchronization (ERS) is calculated from the PSD values of the basal GBA (GBAb), the GBA of the imaginary movement (GBAim) and the GBA of the actual movement (GBAac). The mean GBAim and GBAac values for the right and left hands are significantly higher than the GBAb value (p = 0.007). No significant difference is detected between mean GBA values during the imaginary and actual movement (p = 0.242). The mean ERS values for the imaginary movement (ERSimM (%) = 23.52) and for the actual movement (ERSacM = 27.47) do not present any significant difference (p = 0.117). We demonstrated that ERS could provide a useful way of indirectly checking the function of neuronal motor circuits activated by voluntary movement, both imaginary and actual. These results, as a proof of concept, could be applied to physiology studies, brain-computer interfaces, and diagnosis of cognitive or motor pathologies.
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157
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Saha S, Baumert M. Intra- and Inter-subject Variability in EEG-Based Sensorimotor Brain Computer Interface: A Review. Front Comput Neurosci 2020; 13:87. [PMID: 32038208 PMCID: PMC6985367 DOI: 10.3389/fncom.2019.00087] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/16/2019] [Indexed: 12/05/2022] Open
Abstract
Brain computer interfaces (BCI) for the rehabilitation of motor impairments exploit sensorimotor rhythms (SMR) in the electroencephalogram (EEG). However, the neurophysiological processes underpinning the SMR often vary over time and across subjects. Inherent intra- and inter-subject variability causes covariate shift in data distributions that impede the transferability of model parameters amongst sessions/subjects. Transfer learning includes machine learning-based methods to compensate for inter-subject and inter-session (intra-subject) variability manifested in EEG-derived feature distributions as a covariate shift for BCI. Besides transfer learning approaches, recent studies have explored psychological and neurophysiological predictors as well as inter-subject associativity assessment, which may augment transfer learning in EEG-based BCI. Here, we highlight the importance of measuring inter-session/subject performance predictors for generalized BCI frameworks for both normal and motor-impaired people, reducing the necessity for tedious and annoying calibration sessions and BCI training.
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Affiliation(s)
- Simanto Saha
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
| | - Mathias Baumert
- School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia
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158
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Volkova K, Lebedev MA, Kaplan A, Ossadtchi A. Decoding Movement From Electrocorticographic Activity: A Review. Front Neuroinform 2019; 13:74. [PMID: 31849632 PMCID: PMC6901702 DOI: 10.3389/fninf.2019.00074] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/14/2019] [Indexed: 01/08/2023] Open
Abstract
Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for people suffering from neurological disabilities. This recording technique combines adequate temporal and spatial resolution with the lower risks of medical complications compared to the other invasive methods. ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients. During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of different complexity. Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings. Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.
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Affiliation(s)
- Ksenia Volkova
- Center for Bioelectric Interfaces, Higher School of Economics, National Research University, Moscow, Russia
| | - Mikhail A. Lebedev
- Center for Bioelectric Interfaces, Higher School of Economics, National Research University, Moscow, Russia
| | - Alexander Kaplan
- Center for Bioelectric Interfaces, Higher School of Economics, National Research University, Moscow, Russia
- Center for Biotechnology Development, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Laboratory for Neurophysiology and Neuro-Computer Interfaces, Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Alexei Ossadtchi
- Center for Bioelectric Interfaces, Higher School of Economics, National Research University, Moscow, Russia
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