EMG Based Control of Transhumeral Prosthesis Using Machine Learning Algorithms

Sattar, Neelum Yousaf; Kausar, Zareena; Usama, Syed Ali; Khan, Umar Shahbaz

doi:10.1007/s12555-019-1058-5

Cited by 20 publications

(8 citation statements)

References 37 publications

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“…The qualitative aspects of these RPs can be used for classification. Moreover, DNNs are highly efficient training classifiers, resulting in better classification accuracy than ML classifiers (Sattar et al, 2021 ). However, only a few studies that applied these algorithms in BCI are available (Dehghani et al, 2021 ; Singh et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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The constantly evolving human–machine interaction and advancement in sociotechnical systems have made it essential to analyze vital human factors such as mental workload, vigilance, fatigue, and stress by monitoring brain states for optimum performance and human safety. Similarly, brain signals have become paramount for rehabilitation and assistive purposes in fields such as brain–computer interface (BCI) and closed-loop neuromodulation for neurological disorders and motor disabilities. The complexity, non-stationary nature, and low signal-to-noise ratio of brain signals pose significant challenges for researchers to design robust and reliable BCI systems to accurately detect meaningful changes in brain states outside the laboratory environment. Different neuroimaging modalities are used in hybrid settings to enhance accuracy, increase control commands, and decrease the time required for brain activity detection. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) measure the hemodynamic and electrical activity of the brain with a good spatial and temporal resolution, respectively. However, in hybrid settings, where both modalities enhance the output performance of BCI, their data compatibility due to the huge discrepancy between their sampling rate and the number of channels remains a challenge for real-time BCI applications. Traditional methods, such as downsampling and channel selection, result in important information loss while making both modalities compatible. In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS EEG for hybrid BCI applications. The acquired brain signals are first projected into a non-linear dimension with RPs and fed into the CNN to extract essential features without performing any downsampling. Then, LSTM is used to learn the chronological features and time-dependence relation to detect brain activity. The average accuracies achieved with the proposed model were 78.44% for fNIRS, 86.24% for EEG, and 88.41% for hybrid EEG-fNIRS BCI. Moreover, the maximum accuracies achieved were 85.9, 88.1, and 92.4%, respectively. The results confirm the viability of the RP-based deep-learning algorithm for successful BCI systems.

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Section: Introductionmentioning

confidence: 99%

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

et al. 2022

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“…Deep neural networks have more layers than traditional ones (Luo et al, 2021 ; Sattar et al, 2021a ; Zhang et al, 2021 ). The layers in a deep neural network are composed of multiple artificial neurons, each connected to several other neurons in the next layer.…”

Section: Methodsmentioning

confidence: 99%

“…There are various methods for tracking muscle activity that occurs during physical movement (Visconti et al, 2018 ). For instance, to monitor muscular contraction, techniques applied include sonomyography (SMG) (Xie et al, 2014 ), mechanomyography (MMG) (Sattar et al, 2021a ), miokinemetric (MK) (Sattar et al, 2021a ), and electric impedance estimation (Xie et al, 2014 ). Muscle contraction for intention is often determined using surface electromyography (sEMG) and near-infrared spectroscopy (NIRS) (Zhou et al, 2007 ; Herold et al, 2018 ), as it allows continuous muscle motion monitoring during motor actions and activities for rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based framework for real-time upper limb motion intention classification using combined bio-signals

Syed,

Sattar,

Ganiyu

et al. 2023

Front. Neurorobot.

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This research study proposes a unique framework that takes input from a surface electromyogram (sEMG) and functional near-infrared spectroscopy (fNIRS) bio-signals. These signals are trained using convolutional neural networks (CNN). The framework entails a real-time neuro-machine interface to decode the human intention of upper limb motions. The bio-signals from the two modalities are recorded for eight movements simultaneously for prosthetic arm functions focusing on trans-humeral amputees. The fNIRS signals are acquired from the human motor cortex, while sEMG is recorded from the human bicep muscles. The selected classification and command generation features are the peak, minimum, and mean ΔHbO and ΔHbR values within a 2-s moving window. In the case of sEMG, wavelength, peak, and mean were extracted with a 150-ms moving window. It was found that this scheme generates eight motions with an enhanced average accuracy of 94.5%. The obtained results validate the adopted research methodology and potential for future real-time neural-machine interfaces to control prosthetic arms.

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“…Machine learning algorithms are increasingly finding application in transhumeral and transradial prostheses. In [22] , it is described how machine learning algorithms are used to classify EEG signals for the control of an arm prosthesis, and in [23] , EMG signals, classified by machine learning algorithms, are used to generate command controls for arm movements. In this paper, an innovative approach is proposed to enhance the functionality of robotic arm prostheses.…”

Section: Hardware In Contextmentioning

confidence: 99%

A low-cost robotic hand prosthesis with apparent haptic sense controlled by electroencephalographic signals

Cutipa-Puma,

Coaguila-Quispe,

Yanyachi

2023

HardwareX

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EMG Based Control of Transhumeral Prosthesis Using Machine Learning Algorithms

Cited by 20 publications

References 37 publications

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

EEG-fNIRS-based hybrid image construction and classification using CNN-LSTM

Deep learning-based framework for real-time upper limb motion intention classification using combined bio-signals

A low-cost robotic hand prosthesis with apparent haptic sense controlled by electroencephalographic signals

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