Oluwarotimi Samuel | Chinese Academy of Sciences (original) (raw)

Papers by Oluwarotimi Samuel

Research paper thumbnail of A Self-Learning Control Scheme for Upper-Limb Prosthesis Control Using Combined Neuromuscular and Brain Wave Signals

Proceedings of 7th International Electronic Conference on Sensors and Applications, 2020

The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task ... more The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task is to decode an input signal, produce a respective actuation signal and drive the motors in the prosthesis limb towards the completion of the user's intended gesture motion. The pattern recognition architecture works with a classifier which is typically trained and calibrated offline with a supervised learning framework. This method involves the training of classifiers which form part of the pattern recognition scheme, but also induces additional and often undesired lead time in the prosthesis design phase. In this study, a three-phase identification framework is formulated to design a control architecture capable of self-learning patterns from bio-signal inputs from electromyography (neuromuscular) and electroencephalography (brain wave) biosensors, for a transhumeral amputee case study. The results show that the designed self-learning framework can help reduce lead time in prosthesis control interface customisation, and can also be extended as an adaptive control scheme to minimise the performance degradation of the prosthesis controller.

Research paper thumbnail of Pregnancy Labor Prediction Using Magnetomyography Sensing and a Self-Sorting Cybernetic Model

Engineering Proceedings, 2021

To date, effective means of predicting pregnancy labor continues to lack. Magnetic field signals ... more To date, effective means of predicting pregnancy labor continues to lack. Magnetic field signals during uterine contraction have shown, in recent studies, to be a good source of information for predicting labor state with a greater accuracy compared with existing methods. The means of labor prediction methods from such signals appear to rely on a supervised learning post-processing framework whose calibration relies on an effective labelling of the training sample set. As a potential solution to this, using a reduced electrode channel from magnetomyography instrumentation, we propose a multi-stage self-sorting cybernetic model that is comprised of an ensemble of various post-processing methods, and is underpinned by an unsupervised learning framework that allows for an automated method towards learning from the trend in the data to infer labor state and imminency. Experimental results showed a comparable accuracy with those from a supervised learning method adopted in a prior study....

Research paper thumbnail of A Low-rank Spatiotemporal based EEG Multi-Artifacts Cancellation Method for Enhanced ConvNet-DL’s Motor Imagery Characterization

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021

Multi-channel Electroencephalograph (EEG) signal is an important source of neural information for... more Multi-channel Electroencephalograph (EEG) signal is an important source of neural information for motor imagery (MI) limb movement intent decoding. The decoded MI movement intent often serve as potential control input for braincomputer interface (BCI) based rehabilitation robots. However, the presence of multiple dynamic artifacts in EEG signal leads to serious processing challenge that affects the BCI system in practical settings. Hence, this study propose a hybrid approach based on Low-rank spatiotemporal filtering technique for concurrent elimination of multiple EEG artifacts. Afterwards, a convolutional neural network based deep learning model (ConvNet-DL) that extracts neural information from the cleaned EEG signal for MI tasks decoding was built. The proposed method was studied in comparison with existing artifact removal methods using EEG signals of transhumeral amputees who performed five different MI tasks. Remarkably, the proposed method led to significant improvements in MI task decoding accuracy for the ConvNet-DL model in the range of 8.00~13.98%, while up to 14.38% increment was recorded in terms of the MCC: Mathew correlation coefficients at p<0.05. Also, a signal to error ratio of more than 11 dB was recorded by the proposed method. Clinical Relevance-This study showed that a combination of the proposed hybrid EEG artifact removal method and ConvNet-DL can significantly improve the decoding accuracy of MI upper limb movement tasks. Our findings may provide potential control input for BCI rehabilitation robotic systems.

Research paper thumbnail of HD-sEMG Signal Denoising Method for Improved Classification Performance in Transhumeral Amputees Pros thesis Control

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021

Surface myoelectric pattern recognition (sMPR) based control strategy is a popularly adopted sche... more Surface myoelectric pattern recognition (sMPR) based control strategy is a popularly adopted scheme for multifunctional upper limb prostheses. Meanwhile, above-elbow amputees (transhumeral: TH) usually have limited residual arm muscles, that mostly hinder the provision of requisite signals necessary for physiologically appropriate sMPR control. Hence, the need to maximally explore the limited signals to realize adequate sMPR control scheme in practical settings. This study proposes an effective signal denoising method driven by Multiscale Local Polynomial Transform (MLPT) concept that can improve the signal quality, thus allowing adequate decoding of TH amputees' motion intent from high-density electromyogram (HD-sEMG) signals. The proposed method's performance was systematically investigated with HD-sEMG signals obtained from TH amputees that performed multiple classes of targeted upper limb movement tasks, and compared with two common signal denoising methods based on wavelet transform. The obtained results show that the proposed MLPT method outperformed both existing methods for motion tasks decoding with over 13.0% increment in accuracy across subjects. The possibility of generating distinct and repeatable myoelectric contraction patterns using the MLPT based denoised HDs-EMG recordings was investigated. The obtained results proved that the MLPT method can better denoise and aid the reconstruction of myoelectric signal patterns of the amputees. Therefore, this suggest the potential of the MLPT method in characterizing high-level upper limb amputees' muscle activation patterns in the context of sMPR prostheses control scheme. Sciences (CAS),

Research paper thumbnail of Application of noninvasive magnetomyography in labour imminency prediction for term and preterm pregnancies and ethnicity specific labour prediction

Machine Learning with Applications, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Research paper thumbnail of GBRAMP: A generalized backtracking regularized adaptive matching pursuit algorithm for signal reconstruction

Computers & Electrical Engineering, 2021

：In order to resolve the problem of excessive processing time and inadequate accuracy caused by e... more ：In order to resolve the problem of excessive processing time and inadequate accuracy caused by existing algorithms in robot vision image reconstruction, a block variable step size adaptive compression sensor reconstruction algorithm is proposed. The algorithm integrates the regularized orthogonal matching pursuit technique in a seamlessly efficient manner to obtain consistent and accurate signal reconstruction outcomes. To apply this technique, a set of selected atoms is initialized by setting fuzzy threshold. Subsequently, inappropriate atoms are excluded, and an iterative procedure is initiated to update the set so as to approximate the signal sparsity in a stepwise fashion. In comparison with commonly used algorithms, the proposed algorithm achieved the lowest signal recovery and reconstruction error. Findings from this study indicate that our proposed hybrid paradigm may lead to positive advancement towards the development of intelligent robotic vision systems for industrial applications.

Research paper thumbnail of Efficient Channel Selection Approach for Motor Imaginary Classification based on Convolutional Neural Network

2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), 2018

Research paper thumbnail of Flexible noncontact electrodes for comfortable monitoring of physiological signals

International Journal of Adaptive Control and Signal Processing, 2019

Research paper thumbnail of A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever

Expert Systems with Applications, 2013

Research has identified Typhoid Fever (TF) as the major cause of morbidity and mortality in most ... more Research has identified Typhoid Fever (TF) as the major cause of morbidity and mortality in most developing countries. The diagnosis of TF involves several variables which usually makes it difficult to arrive at accurate and timely diagnosis. This research proposes a Web-Based Decision Support System (WBDSS) driven by Fuzzy Logic (FL) for the diagnosis of TF. The system comprises of a Knowledge Base (KB) and a Fuzzy Inference System (FIS).The FIS is composed of a Fuzzifier, Fuzzy Inference Engine (FIE), and a Defuzzifier. The FIE is the core of the FIS and it adopts the Root Sum Square (RSS) technique in drawing valid conclusion. The Fuzzifier uses a triangular membership function to determine the degree of contribution of each decision variable while the Defuzzifier adopts the Centroid of Area (CoA) defuzzification technique to generate a crisp output for a given diagnosis. An experimental study of the proposed system was conducted using medical records of TF patients obtained from the Federal Medical Center, Owo, Ondo State-Nigeria over a period of six months and the results of the study were found to be within the range of predefined limit as examined by medical experts. Standard statistical metrics were used to measure the efficiency of the proposed system and the results obtained show that the proposed system is 94% efficient in providing accurate diagnosis.

Research paper thumbnail of An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees

Computer Methods and Programs in Biomedicine, 2021

BACKGROUND AND OBJECTIVE Recognition of motor intention based on electroencephalogram (EEG) signa... more BACKGROUND AND OBJECTIVE Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and control for those with severe motor disabilities. In analysis of EEG data, achieving a higher classification performance is dependent on the appropriate representation of EEG features which is mostly characterized by one unique frequency before applying a learning model. Neglecting other frequencies of EEG signals could deteriorate the recognition performance of the model because each frequency has its unique advantages. Motivated by this idea, we propose to obtain distinguishable features with different frequencies by introducing an integrated deep learning model to accurately classify multiple classes of upper limb movement intentions. METHODS The proposed model is a combination of long short-term memory (LSTM) and stacked autoencoder (SAE). To validate the method, four high-level amputees were recruited to perform five motor intention tasks. The acquired EEG signals were first preprocessed before exploring the consequence of input representation on the performance of LSTM-SAE by feeding four frequency bands related to the tasks into the model. The learning model was further improved by t-distributed stochastic neighbor embedding (t-SNE) to eliminate feature redundancy, and to enhance the motor intention recognition. RESULTS The experimental results of the classification performance showed that the proposed model achieves an average performance of 99.01% for accuracy, 99.10% for precision, 99.09% for recall, 99.09% for f1_score, 99.77% for specificity, and 99.0% for Cohen's kappa, across multi-subject and multi-class scenarios. Further evaluation with 2-dimensional t-SNE revealed that the signal decomposition has a distinct multi-class separability in the feature space. CONCLUSION This study demonstrated the predominance of the proposed model in its ability to accurately classify upper limb movements from multiple classes of EEG signals, and its potential application in the development of a more intuitive and naturalistic prosthetic control.

Research paper thumbnail of A Hybrid Brain-Computer Interface using Extreme Learning Machines for Motor Intention Detection

Progress in Artificial Intelligence and Pattern Recognition

Research paper thumbnail of Enhancing care strategies for preterm pregnancies by using a prediction machine to aid clinical care decisions

Machine Learning with Applications

Research paper thumbnail of Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals

IET Cyber-Systems and Robotics

Research paper thumbnail of A Comparative Analysis on the Impact of Linear and Non-Linear Filtering Techniques on EMG Signal Quality of Transhumeral Amputees

2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)

Research paper thumbnail of A Deep Learning based Model for Decoding Motion Intent of Traumatic Brain Injured Patients' using HD-sEMG Recordings

2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)

Research paper thumbnail of A Low Channel Number Sensing Approach for an Ethnic Specific Labour Immanency Prediction using Bio-Electromagnetism

2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)

Research paper thumbnail of Mbl-2 gene polymorphisms in pediatric Burkitt lymphoma: an approach based on machine learning techniques

Research, Society and Development

Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in... more Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in 80% of less advanced stages, it presents in advanced stages in about 75% of cases in Brazil’s Northeast region, requiring urgent and intensive care in the early stages of treatment. Objectives: therefore, this study aimed to verify the participation of MBL-2 gene polymorphisms in the development of Burkitt lymphoma. Methods: In this article, computational approaches based on the Machine Learning technique were used, where we implemented the Random Forest and KMeans algorithms to classify patterns of individuals diagnosed with the disease and, therefore, differentiate them from healthy individuals. A group of 56 patients aged 0 to 18 years, with Burkitt lymphoma, from a reference hospital in the treatment of childhood cancer, was evaluated, together with a control group consisting of 150 samples, all of which were tested for exon 1 polymorphisms and the MBL2 gene -221 and -550 regions. R...

Research paper thumbnail of A study on preterm birth predictions using physiological signals, medical health record information and low‐dimensional embedding methods

IET Cyber-Systems and Robotics

Research paper thumbnail of Contrast of multi‐resolution analysis approach to transhumeral phantom motion decoding

CAAI Transactions on Intelligence Technology

Research paper thumbnail of A Hybrid Approach for Cardiac Blood Flow Vortex Ring Identification Based on Optical Flow and Lagrangian Averaged Vorticity Deviation

Frontiers in Physiology

Objective: The measurement of cardiac blood flow vortex characteristics can help to facilitate th... more Objective: The measurement of cardiac blood flow vortex characteristics can help to facilitate the analysis of blood flow dynamics that regulates heart function. However, the complexity of cardiac flow along with other physical limitations makes it difficult to adequately identify the dominant vortices in a heart chamber, which play a significant role in regulating the heart function. Although the existing vortex quantification methods can achieve this goal, there are still some shortcomings: such as low precision, and ignoring the center of the vortex without the description of vortex deformation processes. To address these problems, an optical flow Lagrangian averaged vorticity deviation (Optical flow-LAVD) method is proposed.Methodology: We examined the flow within the right atrium (RA) of the participants’ hearts, by using a single set of scans pertaining to a slice at two-chamber short-axis orientation. Toward adequate extraction of the vortex ring characteristics, a novel appr...

Research paper thumbnail of A Self-Learning Control Scheme for Upper-Limb Prosthesis Control Using Combined Neuromuscular and Brain Wave Signals

Proceedings of 7th International Electronic Conference on Sensors and Applications, 2020

The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task ... more The control scheme in a myoelectric prosthesis includes a pattern recognition section whose task is to decode an input signal, produce a respective actuation signal and drive the motors in the prosthesis limb towards the completion of the user's intended gesture motion. The pattern recognition architecture works with a classifier which is typically trained and calibrated offline with a supervised learning framework. This method involves the training of classifiers which form part of the pattern recognition scheme, but also induces additional and often undesired lead time in the prosthesis design phase. In this study, a three-phase identification framework is formulated to design a control architecture capable of self-learning patterns from bio-signal inputs from electromyography (neuromuscular) and electroencephalography (brain wave) biosensors, for a transhumeral amputee case study. The results show that the designed self-learning framework can help reduce lead time in prosthesis control interface customisation, and can also be extended as an adaptive control scheme to minimise the performance degradation of the prosthesis controller.

Research paper thumbnail of Pregnancy Labor Prediction Using Magnetomyography Sensing and a Self-Sorting Cybernetic Model

Engineering Proceedings, 2021

To date, effective means of predicting pregnancy labor continues to lack. Magnetic field signals ... more To date, effective means of predicting pregnancy labor continues to lack. Magnetic field signals during uterine contraction have shown, in recent studies, to be a good source of information for predicting labor state with a greater accuracy compared with existing methods. The means of labor prediction methods from such signals appear to rely on a supervised learning post-processing framework whose calibration relies on an effective labelling of the training sample set. As a potential solution to this, using a reduced electrode channel from magnetomyography instrumentation, we propose a multi-stage self-sorting cybernetic model that is comprised of an ensemble of various post-processing methods, and is underpinned by an unsupervised learning framework that allows for an automated method towards learning from the trend in the data to infer labor state and imminency. Experimental results showed a comparable accuracy with those from a supervised learning method adopted in a prior study....

Research paper thumbnail of A Low-rank Spatiotemporal based EEG Multi-Artifacts Cancellation Method for Enhanced ConvNet-DL’s Motor Imagery Characterization

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021

Multi-channel Electroencephalograph (EEG) signal is an important source of neural information for... more Multi-channel Electroencephalograph (EEG) signal is an important source of neural information for motor imagery (MI) limb movement intent decoding. The decoded MI movement intent often serve as potential control input for braincomputer interface (BCI) based rehabilitation robots. However, the presence of multiple dynamic artifacts in EEG signal leads to serious processing challenge that affects the BCI system in practical settings. Hence, this study propose a hybrid approach based on Low-rank spatiotemporal filtering technique for concurrent elimination of multiple EEG artifacts. Afterwards, a convolutional neural network based deep learning model (ConvNet-DL) that extracts neural information from the cleaned EEG signal for MI tasks decoding was built. The proposed method was studied in comparison with existing artifact removal methods using EEG signals of transhumeral amputees who performed five different MI tasks. Remarkably, the proposed method led to significant improvements in MI task decoding accuracy for the ConvNet-DL model in the range of 8.00~13.98%, while up to 14.38% increment was recorded in terms of the MCC: Mathew correlation coefficients at p<0.05. Also, a signal to error ratio of more than 11 dB was recorded by the proposed method. Clinical Relevance-This study showed that a combination of the proposed hybrid EEG artifact removal method and ConvNet-DL can significantly improve the decoding accuracy of MI upper limb movement tasks. Our findings may provide potential control input for BCI rehabilitation robotic systems.

Research paper thumbnail of HD-sEMG Signal Denoising Method for Improved Classification Performance in Transhumeral Amputees Pros thesis Control

2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021

Surface myoelectric pattern recognition (sMPR) based control strategy is a popularly adopted sche... more Surface myoelectric pattern recognition (sMPR) based control strategy is a popularly adopted scheme for multifunctional upper limb prostheses. Meanwhile, above-elbow amputees (transhumeral: TH) usually have limited residual arm muscles, that mostly hinder the provision of requisite signals necessary for physiologically appropriate sMPR control. Hence, the need to maximally explore the limited signals to realize adequate sMPR control scheme in practical settings. This study proposes an effective signal denoising method driven by Multiscale Local Polynomial Transform (MLPT) concept that can improve the signal quality, thus allowing adequate decoding of TH amputees' motion intent from high-density electromyogram (HD-sEMG) signals. The proposed method's performance was systematically investigated with HD-sEMG signals obtained from TH amputees that performed multiple classes of targeted upper limb movement tasks, and compared with two common signal denoising methods based on wavelet transform. The obtained results show that the proposed MLPT method outperformed both existing methods for motion tasks decoding with over 13.0% increment in accuracy across subjects. The possibility of generating distinct and repeatable myoelectric contraction patterns using the MLPT based denoised HDs-EMG recordings was investigated. The obtained results proved that the MLPT method can better denoise and aid the reconstruction of myoelectric signal patterns of the amputees. Therefore, this suggest the potential of the MLPT method in characterizing high-level upper limb amputees' muscle activation patterns in the context of sMPR prostheses control scheme. Sciences (CAS),

Research paper thumbnail of Application of noninvasive magnetomyography in labour imminency prediction for term and preterm pregnancies and ethnicity specific labour prediction

Machine Learning with Applications, 2021

This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Research paper thumbnail of GBRAMP: A generalized backtracking regularized adaptive matching pursuit algorithm for signal reconstruction

Computers & Electrical Engineering, 2021

：In order to resolve the problem of excessive processing time and inadequate accuracy caused by e... more ：In order to resolve the problem of excessive processing time and inadequate accuracy caused by existing algorithms in robot vision image reconstruction, a block variable step size adaptive compression sensor reconstruction algorithm is proposed. The algorithm integrates the regularized orthogonal matching pursuit technique in a seamlessly efficient manner to obtain consistent and accurate signal reconstruction outcomes. To apply this technique, a set of selected atoms is initialized by setting fuzzy threshold. Subsequently, inappropriate atoms are excluded, and an iterative procedure is initiated to update the set so as to approximate the signal sparsity in a stepwise fashion. In comparison with commonly used algorithms, the proposed algorithm achieved the lowest signal recovery and reconstruction error. Findings from this study indicate that our proposed hybrid paradigm may lead to positive advancement towards the development of intelligent robotic vision systems for industrial applications.

Research paper thumbnail of Efficient Channel Selection Approach for Motor Imaginary Classification based on Convolutional Neural Network

2018 IEEE International Conference on Cyborg and Bionic Systems (CBS), 2018

Research paper thumbnail of Flexible noncontact electrodes for comfortable monitoring of physiological signals

International Journal of Adaptive Control and Signal Processing, 2019

Research paper thumbnail of A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever

Expert Systems with Applications, 2013

Research has identified Typhoid Fever (TF) as the major cause of morbidity and mortality in most ... more Research has identified Typhoid Fever (TF) as the major cause of morbidity and mortality in most developing countries. The diagnosis of TF involves several variables which usually makes it difficult to arrive at accurate and timely diagnosis. This research proposes a Web-Based Decision Support System (WBDSS) driven by Fuzzy Logic (FL) for the diagnosis of TF. The system comprises of a Knowledge Base (KB) and a Fuzzy Inference System (FIS).The FIS is composed of a Fuzzifier, Fuzzy Inference Engine (FIE), and a Defuzzifier. The FIE is the core of the FIS and it adopts the Root Sum Square (RSS) technique in drawing valid conclusion. The Fuzzifier uses a triangular membership function to determine the degree of contribution of each decision variable while the Defuzzifier adopts the Centroid of Area (CoA) defuzzification technique to generate a crisp output for a given diagnosis. An experimental study of the proposed system was conducted using medical records of TF patients obtained from the Federal Medical Center, Owo, Ondo State-Nigeria over a period of six months and the results of the study were found to be within the range of predefined limit as examined by medical experts. Standard statistical metrics were used to measure the efficiency of the proposed system and the results obtained show that the proposed system is 94% efficient in providing accurate diagnosis.

Research paper thumbnail of An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees

Computer Methods and Programs in Biomedicine, 2021

BACKGROUND AND OBJECTIVE Recognition of motor intention based on electroencephalogram (EEG) signa... more BACKGROUND AND OBJECTIVE Recognition of motor intention based on electroencephalogram (EEG) signals has attracted considerable research interest in the field of pattern recognition due to its notable application of non-muscular communication and control for those with severe motor disabilities. In analysis of EEG data, achieving a higher classification performance is dependent on the appropriate representation of EEG features which is mostly characterized by one unique frequency before applying a learning model. Neglecting other frequencies of EEG signals could deteriorate the recognition performance of the model because each frequency has its unique advantages. Motivated by this idea, we propose to obtain distinguishable features with different frequencies by introducing an integrated deep learning model to accurately classify multiple classes of upper limb movement intentions. METHODS The proposed model is a combination of long short-term memory (LSTM) and stacked autoencoder (SAE). To validate the method, four high-level amputees were recruited to perform five motor intention tasks. The acquired EEG signals were first preprocessed before exploring the consequence of input representation on the performance of LSTM-SAE by feeding four frequency bands related to the tasks into the model. The learning model was further improved by t-distributed stochastic neighbor embedding (t-SNE) to eliminate feature redundancy, and to enhance the motor intention recognition. RESULTS The experimental results of the classification performance showed that the proposed model achieves an average performance of 99.01% for accuracy, 99.10% for precision, 99.09% for recall, 99.09% for f1_score, 99.77% for specificity, and 99.0% for Cohen's kappa, across multi-subject and multi-class scenarios. Further evaluation with 2-dimensional t-SNE revealed that the signal decomposition has a distinct multi-class separability in the feature space. CONCLUSION This study demonstrated the predominance of the proposed model in its ability to accurately classify upper limb movements from multiple classes of EEG signals, and its potential application in the development of a more intuitive and naturalistic prosthetic control.

Research paper thumbnail of A Hybrid Brain-Computer Interface using Extreme Learning Machines for Motor Intention Detection

Progress in Artificial Intelligence and Pattern Recognition

Research paper thumbnail of Enhancing care strategies for preterm pregnancies by using a prediction machine to aid clinical care decisions

Machine Learning with Applications

Research paper thumbnail of Phantom motion intent decoding for transhumeral prosthesis control with fused neuromuscular and brain wave signals

IET Cyber-Systems and Robotics

Research paper thumbnail of A Comparative Analysis on the Impact of Linear and Non-Linear Filtering Techniques on EMG Signal Quality of Transhumeral Amputees

2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)

Research paper thumbnail of A Deep Learning based Model for Decoding Motion Intent of Traumatic Brain Injured Patients' using HD-sEMG Recordings

2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)

Research paper thumbnail of A Low Channel Number Sensing Approach for an Ethnic Specific Labour Immanency Prediction using Bio-Electromagnetism

2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)

Research paper thumbnail of Mbl-2 gene polymorphisms in pediatric Burkitt lymphoma: an approach based on machine learning techniques

Research, Society and Development

Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in... more Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in 80% of less advanced stages, it presents in advanced stages in about 75% of cases in Brazil’s Northeast region, requiring urgent and intensive care in the early stages of treatment. Objectives: therefore, this study aimed to verify the participation of MBL-2 gene polymorphisms in the development of Burkitt lymphoma. Methods: In this article, computational approaches based on the Machine Learning technique were used, where we implemented the Random Forest and KMeans algorithms to classify patterns of individuals diagnosed with the disease and, therefore, differentiate them from healthy individuals. A group of 56 patients aged 0 to 18 years, with Burkitt lymphoma, from a reference hospital in the treatment of childhood cancer, was evaluated, together with a control group consisting of 150 samples, all of which were tested for exon 1 polymorphisms and the MBL2 gene -221 and -550 regions. R...

Research paper thumbnail of A study on preterm birth predictions using physiological signals, medical health record information and low‐dimensional embedding methods

IET Cyber-Systems and Robotics

Research paper thumbnail of Contrast of multi‐resolution analysis approach to transhumeral phantom motion decoding

CAAI Transactions on Intelligence Technology

Research paper thumbnail of A Hybrid Approach for Cardiac Blood Flow Vortex Ring Identification Based on Optical Flow and Lagrangian Averaged Vorticity Deviation

Frontiers in Physiology

Objective: The measurement of cardiac blood flow vortex characteristics can help to facilitate th... more Objective: The measurement of cardiac blood flow vortex characteristics can help to facilitate the analysis of blood flow dynamics that regulates heart function. However, the complexity of cardiac flow along with other physical limitations makes it difficult to adequately identify the dominant vortices in a heart chamber, which play a significant role in regulating the heart function. Although the existing vortex quantification methods can achieve this goal, there are still some shortcomings: such as low precision, and ignoring the center of the vortex without the description of vortex deformation processes. To address these problems, an optical flow Lagrangian averaged vorticity deviation (Optical flow-LAVD) method is proposed.Methodology: We examined the flow within the right atrium (RA) of the participants’ hearts, by using a single set of scans pertaining to a slice at two-chamber short-axis orientation. Toward adequate extraction of the vortex ring characteristics, a novel appr...