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Sziburis T, Nowak M, Brunelli D. Instance-based learning with prototype reduction for real-time proportional myocontrol: a randomized user study demonstrating accuracy-preserving data reduction for prosthetic embedded systems. Med Biol Eng Comput 2024; 62:275-305. [PMID: 37796400 PMCID: PMC10758379 DOI: 10.1007/s11517-023-02917-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 08/21/2023] [Indexed: 10/06/2023]
Abstract
This work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in prosthetic control. To cope with high computational demands in instance-based prediction, methods of dataset reduction are evaluated considering real-time determinism to allow for the reliable integration into battery-powered portable devices. The influence of parameterization and varying proportionality schemes is analyzed, utilizing an eight-channel-sEMG armband. Besides offline cross-validation accuracy, success rates in real-time pilot experiments (online target achievement tests) are determined. Based on the assessment of specific dataset reduction techniques' adequacy for embedded control applications regarding accuracy and timing behaviour, decision surface mapping (DSM) proves itself promising when applying kNN on the reduced set. A randomized, double-blind user study was conducted to evaluate the respective methods (kNN and kNN with DSM-reduction) against ridge regression (RR) and RR with random Fourier features (RR-RFF). The kNN-based methods performed significantly better ([Formula: see text]) than the regression techniques. Between DSM-kNN and kNN, there was no statistically significant difference (significance level 0.05). This is remarkable in consideration of only one sample per class in the reduced set, thus yielding a reduction rate of over 99% while preserving success rate. The same behaviour could be confirmed in an extended user study. With [Formula: see text], which turned out to be an excellent choice, the runtime complexity of both kNN (in every prediction step) as well as DSM-kNN (in the training phase) becomes linear concerning the number of original samples, favouring dependable wearable prosthesis applications.
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Affiliation(s)
- Tim Sziburis
- Institute for Neuroinformatics (INI), Ruhr University Bochum, Universitätsstr. 150, Bochum, 44801, Germany.
- German Aerospace Center (DLR), Robotics and Mechatronics Center (RMC), Münchener Str. 20, 82234, Weßling, Germany.
| | - Markus Nowak
- German Aerospace Center (DLR), Robotics and Mechatronics Center (RMC), Münchener Str. 20, 82234, Weßling, Germany
| | - Davide Brunelli
- Department of Industrial Engineering, DII, University of Trento, Via Sommarive, 9, 38123, Trento, Italy
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Liang H, Zhu C, Iwata Y, Maedono S, Mochita M, Liu C, Ueda N, Li P, Yu H, Yan Y, Duan F. Feature Extraction of Shoulder Joint's Voluntary Flexion-Extension Movement Based on Electroencephalography Signals for Power Assistance. Bioengineering (Basel) 2018; 6:E2. [PMID: 30586920 PMCID: PMC6466223 DOI: 10.3390/bioengineering6010002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 12/19/2018] [Accepted: 12/20/2018] [Indexed: 11/28/2022] Open
Abstract
Brain-Machine Interface (BMI) has been considered as an effective way to help and support both the disabled rehabilitation and healthy individuals' daily lives to use their brain activity information instead of their bodies. In order to reduce costs and control exoskeleton robots better, we aim to estimate the necessary torque information for a subject from his/her electroencephalography (EEG) signals when using an exoskeleton robot to perform the power assistance of the upper limb without using external torque sensors nor electromyography (EMG) sensors. In this paper, we focus on extracting the motion-relevant EEG signals' features of the shoulder joint, which is the most complex joint in the human's body, to construct a power assistance system using wearable upper limb exoskeleton robots with BMI technology. We extract the characteristic EEG signals when the shoulder joint is doing flexion and extension movement freely which are the main motions of the shoulder joint needed to be assisted. Independent component analysis (ICA) is used to extract the source information of neural components, and then the average method is used to extract the characteristic signals that are fundamental to achieve the control. The proposed approach has been experimentally verified. The results show that EEG signals begin to increase at 300⁻400 ms before the motion and then decrease at the beginning of the generation of EMG signals, and the peaks appear at about one second after the motion. At the same time, we also confirmed the relationship between the change of EMG signals and the EEG signals on the time dimension, and these results also provide a theoretical basis for the delay parameter in the linear model which will be used to estimate the necessary torque information in future. Our results suggest that the estimation of torque information based on EEG signals is feasible, and demonstrate the potential of using EEG signals via the control of brain-machine interface to support human activities continuously.
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Affiliation(s)
- Hongbo Liang
- Department of Environment and Life Engineering, Graduate School of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan.
| | - Chi Zhu
- Department of Environment and Life Engineering and Department of Systems Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan.
| | - Yu Iwata
- Department of Systems Life Engineering, Graduate School of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan.
| | - Shota Maedono
- Department of Systems Life Engineering, Graduate School of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan.
| | - Mika Mochita
- Department of Systems Life Engineering, Faculty of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan.
| | - Chang Liu
- Department of Environment and Life Engineering, Graduate School of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan.
| | - Naoya Ueda
- Department of Systems Life Engineering, Graduate School of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan.
| | - Peirang Li
- Department of Systems Life Engineering, Graduate School of Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan.
| | - Haoyong Yu
- Department of Bioengineering, Faculty of Engineering, National University of Singapore, Singapore 119077, Singapore.
| | - Yuling Yan
- Department of Bioengineering, School of Engineering, Santa Clara University, Santa Clara, CA 1304, USA.
| | - Feng Duan
- Department of Automation, College of Information Technical Science, Nankai University, Tianjin 300071, China.
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Ma X, Ma C, Huang J, Zhang P, Xu J, He J. Decoding Lower Limb Muscle Activity and Kinematics from Cortical Neural Spike Trains during Monkey Performing Stand and Squat Movements. Front Neurosci 2017; 11:44. [PMID: 28223914 PMCID: PMC5293822 DOI: 10.3389/fnins.2017.00044] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Accepted: 01/20/2017] [Indexed: 11/13/2022] Open
Abstract
Extensive literatures have shown approaches for decoding upper limb kinematics or muscle activity using multichannel cortical spike recordings toward brain machine interface (BMI) applications. However, similar topics regarding lower limb remain relatively scarce. We previously reported a system for training monkeys to perform visually guided stand and squat tasks. The current study, as a follow-up extension, investigates whether lower limb kinematics and muscle activity characterized by electromyography (EMG) signals during monkey performing stand/squat movements can be accurately decoded from neural spike trains in primary motor cortex (M1). Two monkeys were used in this study. Subdermal intramuscular EMG electrodes were implanted to 8 right leg/thigh muscles. With ample data collected from neurons from a large brain area, we performed a spike triggered average (SpTA) analysis and got a series of density contours which revealed the spatial distributions of different muscle-innervating neurons corresponding to each given muscle. Based on the guidance of these results, we identified the locations optimal for chronic electrode implantation and subsequently carried on chronic neural data recordings. A recursive Bayesian estimation framework was proposed for decoding EMG signals together with kinematics from M1 spike trains. Two specific algorithms were implemented: a standard Kalman filter and an unscented Kalman filter. For the latter one, an artificial neural network was incorporated to deal with the nonlinearity in neural tuning. High correlation coefficient and signal to noise ratio between the predicted and the actual data were achieved for both EMG signals and kinematics on both monkeys. Higher decoding accuracy and faster convergence rate could be achieved with the unscented Kalman filter. These results demonstrate that lower limb EMG signals and kinematics during monkey stand/squat can be accurately decoded from a group of M1 neurons with the proposed algorithms. Our findings provide new insights for extending current BMI design concepts and techniques on upper limbs to lower limb circumstances. Brain controlled exoskeleton, prostheses or neuromuscular electrical stimulators for lower limbs are expected to be developed, which enables the subject to manipulate complex biomechatronic devices with mind in more harmonized manner.
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Affiliation(s)
- Xuan Ma
- Neural Interface and Rehabilitation Technology Research Center, School of Automation, Huazhong University of Science and Technology Wuhan, China
| | - Chaolin Ma
- Center for Neuropsychiatric Disorders, Institute of Life Science, Nanchang UniversityNanchang, China; Center for Neural Interface Design, School of Biological and Health Systems Engineering, Arizona State UniversityTempe, AZ, USA
| | - Jian Huang
- Neural Interface and Rehabilitation Technology Research Center, School of Automation, Huazhong University of Science and Technology Wuhan, China
| | - Peng Zhang
- Neural Interface and Rehabilitation Technology Research Center, School of Automation, Huazhong University of Science and Technology Wuhan, China
| | - Jiang Xu
- Department of Rehabilitation Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology Wuhan, China
| | - Jiping He
- Neural Interface and Rehabilitation Technology Research Center, School of Automation, Huazhong University of Science and TechnologyWuhan, China; Center for Neural Interface Design, School of Biological and Health Systems Engineering, Arizona State UniversityTempe, AZ, USA; Collaborative Innovation Center for Brain Science, Huazhong University of Science and TechnologyWuhan, China; Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of TechnologyBeijing, China
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