EMG Pattern Recognition in the Era of Big Data and Deep Learning

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals

Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., & Stanley, H. E. (2000) · Circulation · doi:10.1161/01.cir.101.23.e215 · Cited by 14,809 · see citing works

Domain-Adversarial Training of Neural Networks

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318 · see citing works

DEAP: A Database for Emotion Analysis ;Using Physiological Signals

Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093 · see citing works

Beyond the hype: Big data concepts, methods, and analytics

Gandomi, A., & Haider, M. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.10.007 · Cited by 4,226 · see citing works

Large Scale Distributed Deep Networks

Dean, J. B., Corrado, G. S., Monga, R., Chen, K., Devin, M., Mao, M., Ranzato, M., Senior, A., Tucker, P. A., Yang, K., et al. (2012) · Cited by 3,189 · see citing works

Detecting Stress During Real-World Driving Tasks Using Physiological Sensors

Healey, J. A., & Picard, R. W. (2005) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2005.848368 · Cited by 2,207 · see citing works

A new strategy for multifunction myoelectric control

Hudgins, B., Parker, P., & Scott, R. N. (1993) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.204774 · Cited by 2,149 · see citing works

Feature reduction and selection for EMG signal classification

Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.102 · Cited by 1,562 · see citing works

The Sleep Heart Health Study: Design, Rationale, and Methods

(1997) · Sleep · doi:10.1093/sleep/20.12.1077 · Cited by 1,331 · see citing works

Big data deep learning: Challenges and perspectives

Chen, X.-W., & Lin, X. (2014) · IEEE Access · doi:10.1109/access.2014.2325029 · Cited by 1,255 · see citing works

A survey on deep learning for big data

Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.006 · Cited by 1,226 · see citing works

Targeted Muscle Reinnervation for Real-time Myoelectric Control of Multifunction Artificial Arms

Kuiken, T. A. (2009) · JAMA · doi:10.1001/jama.2009.116 · Cited by 1,089 · see citing works

Deep learning for healthcare applications based on physiological signals: A review

Faust, O., Hagiwara, Y., Hong, T. J., Lih, O. S., & Acharya, U. R. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.005 · Cited by 1,042 · see citing works

Electromyography data for non-invasive naturally-controlled robotic hand prostheses

Atzori, M., Gijsberts, A., Castellini, C., Barbara Caputo, B., Hager, A.-G. M., Elsig, S., Giatsidis, G., Bassetto, F., & Müller, H. (2014) · Scientific Data · doi:10.1038/sdata.2014.53 · Cited by 990 · see citing works

Electromyogram pattern recognition for control of powered upper-limb prostheses: State of the art and challenges for clinical use

Scheme, E., & Englehart, K. B. (2011) · The Journal of Rehabilitation Research and Development · doi:10.1682/jrrd.2010.09.0177 · Cited by 983 · see citing works

Support Vector Machine-Based Classification Scheme for Myoelectric Control Applied to Upper Limb

Oskoei, M. A., & Hu, H. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.919734 · Cited by 829 · see citing works

Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep

Terzano, M. G., Parrino, L., Sherieri, A., Chervin, R. D., Chokroverty, S., Guilleminault, C., Hirshkowitz, M., Mahowald, M. W., Moldofsky, H., Rosa, A., et al. (2001) · Sleep Medicine · doi:10.1016/s1389-9457(01)00149-6 · Cited by 795 · see citing works

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Côté‐Allard, U., Fall, C. L., Drouin, A., Campeau‐Lecours, A., Gosselin, C., Glette, K., Laviolette, F., & Gosselin, B. J. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2896269 · Cited by 770 · see citing works

A wavelet-based continuous classification scheme for multifunction myoelectric control

Englehart, K., Hudgin, B., & Parker, P. A. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.914793 · Cited by 697 · see citing works

Deep learning with convolutional neural networks applied to electromyography data: a resource for the classification of movements for prosthetic hands

Atzori, M., Cognolato, M., & Müller, H. (2016) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2016.00009 · Cited by 684 · see citing works

EMG feature evaluation for improving myoelectric pattern recognition robustness

Phinyomark, A., Quaine, F., Charbonnier, S., Servière, C., Tarpin-Bernard, F., & Laurillau, Y. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.02.023 · Cited by 638 · see citing works

Gesture recognition by instantaneous surface EMG images

Geng, W., Du, Y., Jin, W., Wei, W., Hu, Y., & Li, J. (2016) · Scientific Reports · doi:10.1038/srep36571 · Cited by 603 · see citing works

Study of stability of time-domain features for electromyographic pattern recognition

Tkach, D. C., Huang, H., & Kuiken, T. (2010) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-7-21 · Cited by 581 · see citing works

Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival

Nicolau, M. M., Levine, A. J., & Carlsson, G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1102826108 · Cited by 516 · see citing works

Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition

Singh, G., Memoli, F., & Carlsson, G. (2007) · The Eurographics Association · doi:10.2312/spbg/spbg07/091-100 · Cited by 514 · see citing works

Addressing big data issues in Scientific Data Infrastructure

Demchenko, Y., Grosso, P., de Laat, C. T. A. M., & Membrey, P. (2013) · International Conference on Collaboration Technologies and Systems (CTS) · doi:10.1109/cts.2013.6567203 · Cited by 506 · see citing works

EMG feature evaluation for movement control of upper extremity prostheses

Zardoshti-Kermani, M., Wheeler, B. C., Badie, K., & Hashemi, R. M. (1995) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.481972 · Cited by 477 · see citing works

Comparison of six electromyography acquisition setups on hand movement classification tasks

Pizzolato, S., Tagliapietra, L., Cognolato, M., Reggiani, M., Müller, H., & Atzori, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0186132 · Cited by 429 · see citing works

Resolving the Limb Position Effect in Myoelectric Pattern Recognition

Fougner, A. L., Scheme, E., Chan, A. D. C., Englehart, K. B., & Stavdahl, Ø. (2011) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2011.2163529 · Cited by 403 · see citing works

Evaluation of the forearm EMG signal features for the control of a prosthetic hand

Boostani, R., & Moradi, M. H. (2003) · Physiological Measurement · doi:10.1088/0967-3334/24/2/307 · Cited by 393 · see citing works

DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses

Abadi, M. K., Subramanian, R., Kia, S. M., Avesani, P., Patras, I., & Sebe, N. (2015) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2392932 · Cited by 367 · see citing works

Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation

Du, Y., Jin, W., Wei, W., Hu, Y., & Geng, W. (2017) · Sensors · doi:10.3390/s17030458 · Cited by 367 · see citing works

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors

Phinyomark, A., Khushaba, R. N., & Scheme, E. (2018) · Sensors · doi:10.3390/s18051615 · Cited by 365 · see citing works

Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network

Zhai, X., Jelfs, B., Chan, R. H. M., & Tin, C. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00379 · Cited by 361 · see citing works

A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand

Chu, J., Moon, I., & Mun, M.-S. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.883695 · Cited by 351 · see citing works

Improving the Performance Against Force Variation of EMG Controlled Multifunctional Upper-Limb Prostheses for Transradial Amputees

Al-Timemy, A. H., Khushaba, R. N., Bugmann, G., & Escudero, J. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2445634 · Cited by 335 · see citing works

Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wrist-motion directions

Kim, K. S., Choi, H.-H., Moon, C. S., & Mun, C. W. (2010) · Current Applied Physics · doi:10.1016/j.cap.2010.11.051 · Cited by 331 · see citing works

Enabling always-available input with muscle-computer interfaces

Saponas, T. S., Tan, D. S., Morris, D., Balakrishnan, R., Turner, J., & Landay, J. A. (2009) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/1622176.1622208 · Cited by 331 · see citing works

Toward improved control of prosthetic fingers using surface electromyogram (EMG) signals

Khushaba, R. N., Kodagoda, S., Takruri, M., & Dissanayake, G. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.02.192 · Cited by 328 · see citing works

Characterization of a Benchmark Database for Myoelectric Movement Classification

Atzori, M., Gijsberts, A., Kuzborskij, I., Elsig, S., Hager, A.-G. M., Dériaz, O., Castellini, C., Müller, H., & Barbara Caputo, B. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2328495 · Cited by 300 · see citing works

A Supervised Feature-Projection-Based Real-Time EMG Pattern Recognition for Multifunction Myoelectric Hand Control

Chu, J., Moon, I., Lee, Y.-J., Kim, S.-K., & Mun, M.-S. (2007) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2007.897262 · Cited by 300 · see citing works

The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system

Walter, S., Gruss, S., Ehleiter, H., Tan, J.-W., Traue, H. C., Crawcour, S. C., Werner, P., Al-Hamadi, A. K., & Andrade, A. D. O. (2013) · IEEE International Conference on Cybernetics (CYBCONF) · doi:10.1109/cybconf.2013.6617456 · Cited by 288 · see citing works

Building the Ninapro database: A resource for the biorobotics community

Atzori, M., Gijsberts, A., Heynen, S., Hager, A.-G. M., Dériaz, O., van der Smagt, P., Castellini, C., Barbara Caputo, B., & Müller, H. (2012) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2012.6290287 · Cited by 272 · see citing works

BioPatRec: A modular research platform for the control of artificial limbs based on pattern recognition algorithms

Ortiz-Catalan, M., Brånemark, R., & Håkansson, B. (2013) · Source Code for Biology and Medicine · doi:10.1186/1751-0473-8-11 · Cited by 232 · see citing works

EEG potentials predict upcoming emergency brakings during simulated driving

Haufe, S., Treder, M. S., Gugler, M. F., Sagebaum, M., Curio, G., & Blankertz, B. (2011) · Journal of Neural Engineering · doi:10.1088/1741-2560/8/5/056001 · Cited by 229 · see citing works

EMG‐Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks

Xia, P., Hu, J., & Peng, Y. (2017) · Artificial Organs · doi:10.1111/aor.13004 · Cited by 225 · see citing works

Improved prosthetic hand control with concurrent use of myoelectric and inertial measurements

Krasoulis, A., Kyranou, I., Erden, M. S., Nazarpour, K., & Vijayakumar, S. (2017) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-017-0284-4 · Cited by 214 · see citing works

Scalable stacking and learning for building deep architectures

Deng, L., Yu, D., & Platt, J. C. (2012) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2012.6288333 · Cited by 213 · see citing works

Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features

Khushaba, R. N., Takruri, M., Miró, J. V., & Kodagoda, S. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.03.010 · Cited by 203 · see citing works

Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms

Karthick, P. A., Ghosh, D. M., & Ramakrishnan, S. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.10.024 · Cited by 202 · see citing works

GPU-based parallel particle swarm optimization

Zhou, Y., & Tan, Y. (2009) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2009.4983119 · Cited by 201 · see citing works

Combined influence of forearm orientation and muscular contraction on EMG pattern recognition

Khushaba, R. N., Al-Timemy, A. H., Kodagoda, S., & Nazarpour, K. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.05.031 · Cited by 192 · see citing works

Muscle coordination and function during cutting movements

Neptune, R. R., Wright, I. C., & van den Bogert, A. J. (1999) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-199902000-00014 · Cited by 183 · see citing works

Efficient parallel genetic algorithms: theory and practice

Cantú‐Paz, E., & Goldberg, D. E. (2000) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/s0045-7825(99)00385-0 · Cited by 179 · see citing works

Surface EMG based continuous estimation of human lower limb joint angles by using deep belief networks

Chen, J., Zhang, X., Yu, C. H., & Xi, N. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.10.002 · Cited by 178 · see citing works

Transfer learning for sEMG hand gestures recognition using convolutional neural networks

Côté‐Allard, U., Fall, C. L., Campeau‐Lecours, A., Gosselin, C., Laviolette, F., & Gosselin, B. J. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122854 · Cited by 177 · see citing works

Advancing Muscle-Computer Interfaces with High-Density Electromyography

Amma, C., Krings, T., Böer, J., & Schultz, T. (2015) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2702123.2702501 · Cited by 173 · see citing works

Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines

Gruss, S., Treister, R., Werner, P., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140330 · Cited by 167 · see citing works

Characterizing EMG data using machine-learning tools

Yousefi, J., & Hamilton-Wright, A. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.04.018 · Cited by 159 · see citing works

Real-Time and Simultaneous Control of Artificial Limbs Based on Pattern Recognition Algorithms

Ortiz-Catalan, M., Håkansson, B. E. V., & Brånemark, R. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2305097 · Cited by 156 · see citing works

A Survey on Deep Learning in Big Data

Gheisari, M., Wang, G., & Bhuiyan, M. Z. A. (2017) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/cse-euc.2017.215 · Cited by 154 · see citing works

Tensor Deep Stacking Networks

Hutchinson, B., Deng, L., & Yu, D. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.268 · Cited by 153 · see citing works

Movement intention decoding based on deep learning for multiuser myoelectric interfaces

Park, K.-H., & Lee, S. (2016) · International Winter Conference on Brain-Computer Interface (BCI) · doi:10.1109/iww-bci.2016.7457459 · Cited by 149 · see citing works

A convolutional neural network for robotic arm guidance using sEMG based frequency-features

Côté‐Allard, U., Nougarou, F., Fall, C. L., Giguère, P., Gosselin, C., Laviolette, F., & Gosselin, B. J. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759384 · Cited by 142 · see citing works

A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition

Khushaba, R. N., Al-Timemy, A. H., Al-Ani, A., & Al-Jumaily, A. A. (2017) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2017.2687520 · Cited by 142 · see citing works

Improving EMG based classification of basic hand movements using EMD

Sapsanis, C., Georgoulas, G., Tzes, A. P., & Lymberopoulos, D. K. (2013) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2013.6610858 · Cited by 138 · see citing works

Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing

Georgi, M., Amma, C., & Schultz, T. (2015) · International Conference on Bio-inspired Systems and Signal Processing · doi:10.5220/0005276900990108 · Cited by 135 · see citing works

Emotion Recognition Based on High-Resolution EEG Recordings and Reconstructed Brain Sources

Becker, H., Fleureau, J., Guillotel, P., Wendling, F., Merlet, I., & Albera, L. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2768030 · Cited by 123 · see citing works

Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data

Palermo, F., Cognolato, M., Gijsberts, A., Müller, H., Barbara Caputo, B., & Atzori, M. (2017) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2017.8009405 · Cited by 119 · see citing works

Electromyogram (EMG) feature reduction using Mutual Components Analysis for multifunction prosthetic fingers control

Khushaba, R. N., & Kodagoda, S. (2012) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2012.6485374 · Cited by 104 · see citing works

Making Data Sharing Count: A Publication-Based Solution

Gorgolewski, K. J., Margulies, D., & Milham, M. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00009 · Cited by 99 · see citing works

Big Data Dimension Reduction Using PCA

Zhang, T., & Yang, B. (2016) · IEEE International Conference on Smart Cloud (SmartCloud) · doi:10.1109/smartcloud.2016.33 · Cited by 94 · see citing works

A Multichannel Surface EMG System for Hand Motion Recognition

Fang, Y., Liu, H., Li, G., & Zhu, X. (2015) · International Journal of Humanoid Robotics · doi:10.1142/s0219843615500115 · Cited by 93 · see citing works

Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition–Based Myoelectric Control

Scheme, E., & Englehart, K. B. (2013) · JPO Journal of Prosthetics and Orthotics · doi:10.1097/jpo.0b013e318289950b · Cited by 93 · see citing works

Validation of a Selective Ensemble-Based Classification Scheme for Myoelectric Control Using a Three-Dimensional Fitts' Law Test

Scheme, E., & Englehart, K. B. (2012) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2012.2226189 · Cited by 90 · see citing works

Multimodal Deep Learning Approach for Joint EEG-EMG Data Compression and Classification

Ben Said, A., Mohamed, A., Elfouly, T., Harras, K., & Wang, Z. J. (2017) · 2017 IEEE Wireless Communications and Networking Conference (WCNC) · doi:10.1109/wcnc.2017.7925709 · Cited by 86 · see citing works

Identification of isometric contractions based on High Density EMG maps

Rojas-Martínez, M., Mañanas, M. Á., Alonso, J. F., & Merletti, R. (2012) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2012.06.009 · Cited by 80 · see citing works

Navigating features: a topologically informed chart of electromyographic features space

Phinyomark, A., Khushaba, R. N., Ibáñez‐Marcelo, E., Patania, A., Scheme, E., & Petri, G. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0734 · Cited by 77 · see citing works

A Review of Control Methods for Electric Power Wheelchairs Based on Electromyography Signals with Special Emphasis on Pattern Recognition

Phinyomark, A., Limsakul, C., & Phukpattaranont, P. (2011) · IETE Technical Review · doi:10.4103/0256-4602.83552 · Cited by 71 · see citing works

Identification of a feature selection based pattern recognition scheme for finger movement recognition from multichannel EMG signals

Geethanjali, P., & Vikas, R. (2018) · Australasian Physical & Engineering Sciences in Medicine · doi:10.1007/s13246-018-0646-7 · Cited by 71 · see citing works

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

Phinyomark, A., Hu, H., Phukpattaranont, P., & Limsakul, C. (2012) · Measurement Science Review · doi:10.2478/v10048-012-0015-8 · Cited by 68 · see citing works

Muscle computer interfaces for driver distraction reduction

Khushaba, R. N., Kodagoda, S., Liu, D., & Dissanayake, G. (2013) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.11.002 · Cited by 66 · see citing works

“BioVid Emo DB”: A multimodal database for emotion analyses validated by subjective ratings

Zhang, L., Walter, S., Ma, X., Werner, P., Al-Hamadi, A. K., Traue, H. C., & Gruss, S. (2016) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci.2016.7849931 · Cited by 60 · see citing works

A feature extraction issue for myoelectric control based on wearable EMG sensors

Phinyomark, A., & Scheme, E. (2018) · IEEE Sensors Applications Symposium (SAS) · doi:10.1109/sas.2018.8336753 · Cited by 59 · see citing works

Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis

Phinyomark, A., Ibáñez‐Marcelo, E., & Petri, G. (2017) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2017.2734883 · Cited by 54 · see citing works

Fast principal component analysis of large data sets

Vogt, F., & Tacke, M. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00130-7 · Cited by 47 · see citing works

On the Suitability of Integrating Accelerometry Data with Electromyography Signals for Resolving the Effect of Changes in Limb Position during Dynamic Limb Movement

Radmand, A., Scheme, E., & Englehart, K. B. (2014) · JPO Journal of Prosthetics and Orthotics · doi:10.1097/jpo.0000000000000041 · Cited by 47 · see citing works

Multi-channel electromyography pattern classification using deep belief networks for enhanced user experience

Shim, H.-M., & Lee, S. (2015) · Journal of Central South University · doi:10.1007/s11771-015-2698-0 · Cited by 46 · see citing works

INVESTIGATING LONG-TERM EFFECTS OF FEATURE EXTRACTION METHODS FOR CONTINUOUS EMG PATTERN CLASSIFICATION

Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2012) · Fluctuation and Noise Letters · doi:10.1142/s0219477512500289 · Cited by 45 · see citing works

EMG feature extraction for tolerance of white Gaussian noise

Phinyomark, A., Limsakul, C., & Phukpattaranont, P. (2008) · Cited by 44 · see citing works

Electromyographic Patterns during Golf Swing: Activation Sequence Profiling and Prediction of Shot Effectiveness

Verikas, A., Vaičiukynas, E., Gelžinis, A., et al. (2016) · Sensors · doi:10.3390/s16040592 · Cited by 44 · see citing works

Determination of optimum threshold values for EMG time domain features; a multi-dataset investigation

Kamavuako, E. N., Scheme, E., & Englehart, K. B. (2016) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/4/046011 · Cited by 43 · see citing works

Pattern learning with deep neural networks in EMG-based speech recognition

Wand, M. D., & Schultz, T. (2014) · doi:10.1109/embc.2014.6944550 · Cited by 42 · see citing works

Deep belief network based affect recognition from physiological signals

Kawde, P., & Verma, G. K. (2017) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon.2017.8251115 · Cited by 41 · see citing works

Channel selection for simultaneous and proportional myoelectric prosthesis control of multiple degrees-of-freedom

Hwang, H.-J., Hahne, J. M., & Müller, K. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/5/056008 · Cited by 40 · see citing works

WAVELET-BASED DENOISING ALGORITHM FOR ROBUST EMG PATTERN RECOGNITION

Phinyomark, A., Phukpattaranont, P., & Limsakul, C. (2010) · Fluctuation and Noise Letters · doi:10.1142/s0219477511000466 · Cited by 38 · see citing works

On the robustness of EMG features for pattern recognition based myoelectric control; A multi-dataset comparison

Scheme, E., & Englehart, K. B. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6943675 · Cited by 37 · see citing works

Signal Processing and Machine Learning for Biomedical Big Data

Phinyomark, A., Ibáñez-Marcelo, E., & Petri, G. (2018) · CRC Press eBooks · doi:10.1201/9781351061223 · Cited by 35 · see citing works

EMG Pattern Classification by Split and Merge Deep Belief Network

Shim, H.-M., An, H., Lee, S., Lee, E., Min, H.-K., & Lee, S. (2016) · Symmetry · doi:10.3390/sym8120148 · Cited by 34 · see citing works

A Novel Spatial Feature for the Identification of Motor Tasks Using High-Density Electromyography

Jordanić, M., Rojas-Martínez, M., Mañanas, M. Á., Alonso, J. F., & Marateb, H. R. (2017) · Sensors · doi:10.3390/s17071597 · Cited by 33 · see citing works

Feature-Level Fusion of Surface Electromyography for Activity Monitoring

Xi, X., Tang, M., & Luo, Z. (2018) · Sensors · doi:10.3390/s18020614 · Cited by 33 · see citing works

A feasibility study on the use of anthropometric variables to make muscle–computer interface more practical

Phinyomark, A., Quaine, F., Charbonnier, S., Servière, C., Tarpin-Bernard, F., & Laurillau, Y. (2013) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2013.01.004 · Cited by 31 · see citing works

Nonlinear analysis of EMG signals - a chaotic approach

Padmanabhan, P., & Puthusserypady, S. K. (2005) · doi:10.1109/iembs.2004.1403231 · Cited by 31 · see citing works

Analog front-ends comparison in the way of a portable, low-power and low-cost EMG controller based on pattern recognition

Mastinu, E., Ortiz-Catalan, M., & Håkansson, B. E. V. (2015) · doi:10.1109/embc.2015.7318805 · Cited by 29 · see citing works

Deep Neural Network Frontend for Continuous EMG-Based Speech Recognition

Wand, M., & Schmidhuber, J. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-340 · Cited by 28 · see citing works

Investigating Classification Parameters for Continuous Myoelectrically Controlled Prostheses

Goge, A. R., & Chan, A. D. C. (2005) · CMBES Proceedings · Cited by 23 · see citing works

EMG AMPLITUDE ESTIMATORS BASED ON PROBABILITY DISTRIBUTION FOR MUSCLE–COMPUTER INTERFACE

Phinyomark, A., Quaine, F., Laurillau, Y., Thongpanja, S., Limsakul, C., & Phukpattaranont, P. (2013) · Fluctuation and Noise Letters · doi:10.1142/s0219477513500168 · Cited by 21 · see citing works

Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals

Hartwell, A., Kadirkamanathan, V., & Anderson, S. (2018) · IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2018.8487853 · Cited by 21 · see citing works

Features extraction and multi-classification of sEMG using a GPU-Accelerated GA/MLP hybrid algorithm

Luo, W., Zhang, Z., Wen, T., et al. (2017) · Journal of X-Ray Science and Technology · doi:10.3233/xst-17259 · Cited by 19 · see citing works

Evaluation of classifier topologies for the real-time classification of simultaneous limb motions

Ortiz-Catalan, M., Brånemark, R., & Håkansson, B. E. V. (2013) · doi:10.1109/embc.2013.6611081 · Cited by 14 · see citing works

The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface

Phinyomark, A., Quaine, F., & Laurillau, Y. (2014) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-4666-6090-8.ch015 · Cited by 13 · see citing works

Real-time classification of simultaneous hand and wrist motions using Artificial Neural Networks with variable threshold outputs

Ortiz-Catalan, M., Håkansson, B. E. V., & Brånemark, R. (2013) · Chalmers Research (Chalmers University of Technology) · Cited by 10 · see citing works

Deep neural networks for myoelectric pattern recognition - An implementation for multifunctional control

Laezza, R. (2018) · Cited by 10 · see citing works

Novel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models

Vögele, A., Zsoldos, R. R., Krüger, B., & Licka, T. F. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157239 · Cited by 9 · see citing works

Convolutional Neural Networks: A Survey

Krichen, M. (2023) · Computers · doi:10.3390/computers12080151 · Cited by 623 · see citing works

Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625 · see citing works

An improved performance of deep learning based on convolution neural network to classify the hand motion by evaluating hyper parameter

Triwiyanto, T., Pawana, I. P. A., & Purnomo, M. H. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2999505 · Cited by 67 · see citing works

Imprecise but Fun: Playful Interaction Using Electromyography

Karolus, J., Thanheiser, S., Peterson, D., Viot, N., Kosch, T., Schmidt, A., & Wozniak, P. W. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3546725 · Cited by 11 · see citing works

Big Data Analytics in Bioinformatics

Patel, D. T. (2019) · Biotechnology · doi:10.4018/978-1-5225-8903-7.ch080 · Cited by 4 · see citing works

The Spectrum of Big Data Analytics

Sun, Z., & Huo, Y. (2019) · Journal of Computer Information Systems · doi:10.1080/08874417.2019.1571456 · Cited by 61 · see citing works

Big Data Analytics in Bioinformatics

Patel, D. T. (2018) · Advances in web technologies and engineering book series · doi:10.4018/978-1-5225-3004-6.ch017 · Cited by 1 · see citing works

Role of Big Data Analytics in Cellular Network Design

Mannava, P. (2015) · International Journal of Scientific Research in Science and Technology · doi:10.32628/ijsrst207254 · Cited by 8 · see citing works

Hands-On Deep Learning with R

Pawlus, M., & Devine, R. (2020) · CERN Document Server (European Organization for Nuclear Research)