If you are interested in any of the projects below, please contact Christian Antfolk
Cortical mapping of multisensory integration using EEG and Virtual Reality
Background: Dexterous hand control is realized through complex relationships between motor commands, movement execution, and sensory feedback, which are centered in central neural system (CNS). Sensory feedback is critical for natural movement planning and execution and will likewise be compulsory for successful neuroprostheses. Multiple sensory modalities, compared to unimodal feedback, have the potential to achieve maximally precise movements (Risso & Valle, 2022). Virtual Reality (VR) has been proposed as a powerful tool to develop realistic scenarios that mimics multisensory stimulation characterizing real-life experiences (Sengül et al., 2013). Although multisensory integration in somatosensory feedback restoration is important, the neural mechanisms of multisensory feedback integration between different sensory modalities (e.g., visual, auditory, tactile) are still elusive. Electroencephalogram (EEG) is promising for revealing the neural mechanisms of multisensory integration implemented by VR (Marucci et al., 2021), which may provide evidence for improving artificial sensory feedback in bionic limbs.
Objective: Develop and implement VR-based multiple sensory stimuli experiments. (MSc) Record multi-channel EEG dataset during VR scenarios and tactile stimulations. (BSc) Analyze stimuli-evoked EEG data to characterize the neural mechanisms of multisensory integration. (MSc)
Approach: A relevant experimental protocol needs to be build using VR to perform visual and/or auditory stimuli with the Unity Real-Time Development Platform, together with tactile stimulators to perform tactile feedback. EEG data will be recorded under different feedback stimuli conditions. EEG data analysis pipeline will be developed and tested in MATLAB and/or Python.
Relevant skills and background: Biomedical signal processing, MATLAB or/and Python programming, Unity Real-Time Development Platform, Experimental implementation and data acquisition.
Equipment to be used: EEG setup, OtBioelettronica Quattrocento bioamplifier, VR, tactile stimulators, PC with relevant software packages.
Relevant literature:
Marucci, M., Di Flumeri, G., Borghini, G., Sciaraffa, N., Scandola, M., Pavone, E. F., Babiloni, F., Betti, V., & Aricò, P. (2021). The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence. Scientific Reports, 11(1). doi.org/10.1038/s41598-021-84196-8
Risso, G., & Valle, G. (2022). Multisensory Integration in Bionics: Relevance and Perspectives. In Current Physical Medicine and Rehabilitation Reports. Springer Science and Business Media B.V. doi.org/10.1007/s40141-022-00350-x
Sengül, A., Rognini, G., Van Elk, M., Aspell, J. E., Bleuler, H., & Blanke, O. (2013). Force feedback facilitates multisensory integration during robotic tool use. Experimental Brain Research, 227(4), 497–507. https://doi.org/10.1007/s00221-013-3526-0
Contact information: Neuroengineering group, Christian Antfolk, Nebojsa Malesevic, Jia Liu
Implementing a GUI to analyse and edit decomposed results from high-density surface electrodes
Background: Voluntary movements start in the brain as instructions and pass the spinal cord to the specific nerves that control the skeletal muscle of interest through the so-called motor units. By using high-density surface electrodes on the skin, it is possible to indirectly extract neural information (spike trains) from the nerves. This extraction is based on blind deconvolution of the recorded electrical signals (Farina et al., 2014). Furthermore, our group has implemented a popular decomposition algorithm (Negro et al., 2016). These algorithms often converge to previously extracted spike trains and require manual editing (Del Vecchio et al., 2020). For this reason, we need to implement a GUI to analyse these spike trains that we can use for manual editing.
Objective: To implement a GUI to analyse decomposed results from high-density surface electrodes. The GUI should be able to load decomposed results (spike trains) and raw signals, calculate features associated with these signals and allow manual editing of individual spikes. The implementation should preferably be done in MATLAB. However, if Python is preferred, that works as well.
Relevant skills: MATLAB/Python programming, (bio)signal processing and modelling.
Equipment to be used: PC with relevant software.
Relevant literature:
Farina, D., Merletti, R., & Enoka, R. M. (2014). The extraction of neural strategies from the surface EMG: an update. Journal of Applied Physiology, 117(11), 1215-1230.
Negro, F., Muceli, S., Castronovo, A. M., Holobar, A., & Farina, D. (2016). Multi-channel intramuscular and surface EMG decomposition by convolutive blind source separation. Journal of Neural Engineering, 13(2), 026027.
Del Vecchio, A., Holobar, A., Falla, D., Felici, F., Enoka, R. M., & Farina, D. (2020). Tutorial: Analysis of motor unit discharge characteristics from high-density surface EMG signals. Journal of Electromyography and Kinesiology, 53, 102426.
Contact information: Neuroengineering group, Christian Antfolk, Robin Rohlén
Developing a spatiotemporal simulation model of recruitment and rate coding organization in motor unit pools
Background:The voluntary movements, e.g., picking up a glass of water, start in the brain as instructions and passes along the spinal cord to the specific nerves that control the skeletal muscle of interest through the so-called motor units. By using different techniques on (or in) the skeletal muscle, researchers and clinicians can extract information about the nerves, the nerve-muscle connection, and/or the events in the muscle given the neural information. Recent innovations using an ultrasound technique have shown the possibility of identifying motor units complementing existing techniques, particularly spatiotemporal mechanical information. However, there are a lot of unknowns about the spatiotemporal responses of motor units in real data. For this reason, an extensive simulation model will improve our understanding of this type of data and can be used to develop decomposition algorithms with ground truth.
Objective: To develop and implement a spatiotemporal simulation model of recruitment and rate coding organization in motor unit pools. The model’s output will be the spatiotemporal mechanical responses of the recruited motor units, i.e., images over time. The work will extend the famous model of Fuglevand et al. (1993) by including a multi-parameter analytic model of the muscle twitch (Raikova et al., 2007) and wave propagations of the motor unit territories (Rohlén et al., 2020). The implementation will be done in MATLAB or Python (NumPy/SciPy).
Relevant skills: MATLAB/Python programming, (bio)signal processing and modelling.
Equipment to be used: PC with relevant software.
Relevant literature:
Fuglevand, A. J., Winter, D. A., & Patla, A. E. (1993). Models of recruitment and rate coding organization in motor-unit pools. Journal of neurophysiology, 70(6), 2470-2488.
Raikova, R., Celichowski, J., Pogrzebna, M., Aladjov, H., & Krutki, P. (2007). Modeling of summation of individual twitches into unfused tetanus for various types of rat motor units. Journal of Electromyography and Kinesiology, 17(2), 121-130.
Rohlén, R., Stålberg, E., Stöverud, K. H., Yu, J., & Grönlund, C. (2020). A method for identification of mechanical response of motor units in skeletal muscle voluntary contractions using ultrafast ultrasound imaging—simulations and experimental tests. IEEE Access, 8, 50299-50311.
Contact information: Neuroengineering group, Christian Antfolk, Robin Rohlén
Embedded signal processing of EMG signals using a Teensy 4.0 and ADS1299
Objective: Implement signal processing/machine learning/deep learning algorithms for advanced prosthesis control using an embedded platform
Approach: In this work, signal processing on the Arduino platform Teensy 4.0 will be performed. EMG-data will be captured from an ADS1299 chip from Texas Instruments and algorithms developed for the Teensy 4.0 microcontroller leveraging the CMSIS functionality of the M7 core . Implementation of time-domain and frequency domain features will be performed. Simple proportional control and machine learning algorithms will be performed on the platform.
Relevant skills: Electronics, signal processing, embedded software and programming experience with the Arduino environment and writing libraries.
Equipment to be used: Teensy 4.0, ADS1299 and breadboard or similar prototyping equipment.
Relevant literature: ADS1299 datasheet, CMSIS manuals
E. Mastinu, B. Hakansson, and M. Ortiz-Catalan, “Low-cost, open source bioelectric signal acquisition system,” in 2017 IEEE 14th International Conference on Wearable and Implantable Body Sensor Networks (BSN), May 2017, pp. 19–22, doi: 10.1109/BSN.2017.7935997.
M. B. Pires, J. J. A. M. Junior, and S. L. Stevan, “Development of an 8 channel sEMG wireless device based on ADS1299 with Virtual Instrumentation,” Aug. 2018, Accessed: Oct. 20, 2021. [Online]. Available: arxiv.org/abs/1808.03711v1.
Contact information: Neuroengineering group, Christian Antfolk
Wireless EMG/EEG/ECG data-acquisition using the ADS1299
Objective: Develop a platform capable of performing data-acquisition and sending the acquired data wirelessly to a PC
Approach: An eight-channel EMG, ECG and EEG chip, the ADS1299, will be used in this work. An Arduino platform with Wifi/Bluetooth capabilities will be sourced in order to perform real-time data streaming from the platform to a PC. This work consists of evaluating different modes of communication (TCP/IP, UDP, data sockets etc) between the platform and the PC and validate the real-time streaming capability of the system.
Relevant skills and background: Communications, electronics and programming preferably experience with the Arduino environment and writing libraries.
Equipment to be used: Relevant Arduino type platform (Teensy, Feather etc) and ADS1299 chips, breadboards etc.
Relevant literature: Manuals, whitepapers
Z. Wang, W. Li, C. Chen, C. Sun, and W. Chen, “A multichannel reconfigurable EEG acquisition system design with felt-based soft material electrodes,” in 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), May 2018, pp. 1–6, doi: 10.1109/I2MTC.2018.8409883.
U. Rashid, I. Niazi, N. Signal, and D. Taylor, “An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299,” Sensors, vol. 18, no. 11, p. 3721, Nov. 2018, doi: 10.3390/s18113721.
Contact information: Neuroengineering group, Christian Antfolk
Finger force regression using EMG + EEG
Objective: Record a dataset for finger force prediction based on EMG and EEG signals. (BSc) Develop an algorithm (or several algorithms) that could be used to detect onset of the movement, type (which finger is moving) and the force. (MSc)
Approach: Experimental work including the data-acquisition and signal processing of both EMG and EEG signals together with force exerted by the hand. A relevant experimental protocol needs to be defined based on experiment. Algorithms will be developed and tested in Python using relevant libraries (scikit-learn, numpy, scipy etc).
Relevant skills and background: Python programming, signal processing, aptitude for experimental work.
Equipment to be used: OtBioelettronica Quattrocento bioamplifier, force measuring device.
Relevant literature:
Malešević, N.; Andersson, G.; Björkman, A.; Controzzi, M.; Cipriani, C.; Antfolk, C. Instrumented Platform for Assessment of Isometric Hand Muscles Contractions. Meas. Sci. Technol. 2019, 30 (6), 065701. doi.org/10.1088/1361-6501/ab0eae.
OTBioelettronica Quattrocento Manual.
Contact information: Neuroengineering group, Christian Antfolk, Nebojsa Malesevic
Characterization of the artificial sensations produced by electrical stimulation
Objective: Find optimal parameters (frequency, amplitude, electrode placement etc) of surface electrical stimulation in order to generate specific sensations.
Approach: This is exploratory work to find correlations between surface electrical stimulation parameters (such as amplitude, frequency, electrode position etc) and percepts of the stimulus. The main experimental equipment and GUI for executing tests are already developed and available. The project will rely heavily on experimental work, which will consist of tests on multiple volunteers and for different combinations of stimulation parameters. The protocol consists of placing one electrode over the median nerve, stimulating with a set of stimulation parameters, and obtaining feedback from the volunteer regarding the sensation that is artificially generated. Using the recorded database, a method should be developed to show influence of different stimulation parameters on sensation type and location.
Relevant skills and background: Programming skills (LabVIEW, Python) and knowledge of statistical methods
Equipment to be used: In house built stimulator, commercial stimulator, LabVIEW, Python
Relevant literature: Rudervall C. Can electrical pulses be adapted to create life-like perceptions? https://www.lunduniversity.lu.se/lup/publication/9033004
G. Chai, X. Sui, S. Li, L. He, and N. Lan, “Characterization of evoked tactile sensation in forearm amputees with transcutaneous electrical nerve stimulation,” J. Neural Eng., vol. 12, no. 6, 2015, doi: 10.1088/1741-2560/12/6/066002.
M. Johnson, “Transcutaneous Electrical Nerve Stimulation: Mechanisms, Clinical Application and Evidence,” Rev. Pain, vol. 1, no. 1, pp. 7–11, Aug. 2007, doi: 10.1177/204946370700100103.
Contact information: Neuroengineering group, Nebojsa Malesevic
Machine learning for multi-label classification of HD-sEMG signals with applications in prosthesis control
Background: Decoding movement intent from high-density surface EMG (HD-sEMG) can naturally be viewed as a multi-label classification problem, where each label represents a single kinematic degree of freedom.
Objective: Investigate, implement, and evaluate some selection of state-of-the-art multi-label or multi-output classification frameworks for the purpose of HD-sEMG movement decoding.
Approach: We have an in-house dataset of HD-sEMG with concurrent visual stimulus signals that will be used in this project. Algorithms to extract information pertaining to movement intent with respect to multiple degrees of freedom simultaneously from HD-sEMG will be developed, tuned, and tested. Development and testing will be carried out mainly in Python using appropriate software libraries (e.g. numpy/scipy, sklearn, keras, tensorflow, pytorch).
Relevant skills and background: Signal processing, Python programming, Statistics, Machine learning.
Equipment to be used: PC with relevant software packages (Python)
Relevant literature:
Olsson, A. E.; Sager, P.; Andersson, E.; Björkman, A.; Malešević, N.; Antfolk, C. Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth. Sci. Rep. 2019, 9 7244. doi.org/10.1038/s41598-019-43676-8
Krasoulis, A.; Nazarpour, K. Myoelectric digit action decoding with multi-output, multi-class classification: an offline analysis. Sci. Rep. 2020, 10 16872. doi.org/10.1038/s41598-020-72574-7
Contact information: Neuroengineering group, Christian Antfolk, Alexander Olsson
Hand/wrist force estimation using intra-muscular EMG
Background: Intra-muscular electromyography (iEMG) can be used to measure latent muscle activity with high spatial resolution. It is possible to use iEMG to estimate the force with which a single forearm muscle contracts, although the degree to which activity in nearby muscles impact the resultant force exerted by the hand remains unknown.
Objective: Develop and evaluate learning algorithms for finger force regression based on iEMG.
Approach: We have an in-house dataset of forearm iEMG with concurrent finger force measurements that will be used in this project. Algorithms to infer the force generated based on iEMG will developed, tuned, and tested. Development and testing will be carried out mainly in Python using appropriate software libraries (e.g. numpy/scipy, sklearn, keras, tensorflow, pytorch).
Relevant skills and background: Signal processing, Python programming, Statistics, Machine learning.
Equipment to be used: PC with relevant software packages (MATLAB and Python)
Relevant literature:
Malešević, N.; Andersson, G.; Björkman, A.; Controzzi, M.; Cipriani, C.; Antfolk, C. Instrumented Platform for Assessment of Isometric Hand Muscles Contractions. Meas. Sci. Technol. 2019, 30 (6), 065701. doi.org/10.1088/1361-6501/ab0eae.
Malesevic, N.; Björkman, A.; Andersson, G. S.; Matran-Fernandez, A.; Citi, L.; Cipriani, C.; Antfolk, C. A Database of Multi-Channel Intramuscular Electromyogram Signals during Isometric Hand Muscles Contractions. Sci. Data 2020, 7 (1), 1–12. doi.org/10.1038/s41597-019-0335-8.
Olsson, A.; Malesevic, N.; Bjorkman, A.; Antfolk, C. Exploiting the Intertemporal Structure of the Upper-Limb SEMG: Comparisons between an LSTM Network and Cross-Sectional Myoelectric Pattern Recognition Methods. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; IEEE, 2019; pp 6611–6615. doi.org/10.1109/EMBC.2019.8856648.
Contact information: Neuroengineering group, Christian Antfolk, Alexander Olsson
Fine-tuning of proportional myocontrol systems
Background: When using pattern recognition algorithms operating on myoelectric signals (EMG) for upper limb prosthesis control, data collection and model fitting are typically carried out only once, after which the resulting model is held constant. A real-time training phase, undertaken after the initial model fitting, could potentially be used to increase both model accuracy and the proficiency of the user.
Objective: Develop and evaluate an iterative recalibration procedure (with appertaining learning algorithms) for a myoelectric control interface.
Approach: An in-house dataset of surface EMG gathered with a Myo armband is available for offline evaluation of relevant myocontrol algorithms. Several algorithms to this end are already available, although modifications will have to be made in order to allow for online fine-tuning. Emphasis will be put on implementing appropriate countermeasures for known issues with online machine learning (e.g. data imbalance, catastrophic forgetting) relevant for the specific application. Development and testing will be carried out mainly in Python using appropriate software libraries (e.g. numpy/scipy, sklearn, keras, tensorflow, pytorch).
Relevant skills and background: Data acquisition, Signal processing, Experiment design, Python programming, Statistics, Machine learning.
Equipment to be used: PC with relevant software packages (Python), Thalmic Myo armband, Auxiliary biosignal acquisition systems (force rig, data glove, etc.).
Relevant literature:
Scheme, E.; Lock, B.; Hargrove, L.; Hill, W.; Kuruganti, U; Englehart, K. Motion Normalized Proportional Control for Improved Pattern Recognition-Based Myoelectric Control. IEEE Trans. Neural Syst. Rehabilitation Eng. 2013 22 (1). doi.org/10.1109/TNSRE.2013.2247421
Ameri, A.; Akhaee, A. A.; Scheme, E., Englehart, K. Regression convolutional neural network for improved simultaneous EMG control. J Neural Eng. 2019 16 (3). doi.org/10.1088/1741-2552/ab0e2e
Contact information: Neuroengineering group, Christian Antfolk, Alexander Olsson
Characterization of the hand grasping using sensor fusion
Objective: Record different hand grasps with multiple wearable sensors positioned on hand and wrist. Develop and evaluate algorithms that could be used to detect the type of grasp.
Approach: Work on this project comprises familiarizing with different sensor technologies, including mm-wave radar, inertial measurement unit (IMU), and sensorized glove (Cyberglove). The technical part of the project includes (1) finding an optimal way of placing multiple sensors on hand, and (2) synchronizing sensors data during recording (or offline). The practical part of the project consists of the recording of different objects grasps by multiple volunteers. Using the recorded database, a machine learning based method should be developed to detect different grasp types.
Relevant skills and background: Programming skills (LabVIEW and/or Python, and/or Matlab), and machine learning skills
Equipment to be used: mm-wave radar senor (from Acconeer), IMU (from Bosch), and dataglove (Cyberglove)
Relevant literature:
Y. Liu, L. Jiang, H. Liu, and D. Ming, “A Systematic Analysis of Hand Movement Functionality: Qualitative Classification and Quantitative Investigation of Hand Grasp Behavior,” Front. Neurorobot., vol. 15, p. 46, Jun. 2021, doi: 10.3389/fnbot.2021.658075.
F. Stival, S. Michieletto, M. Cognolato, E. Pagello, H. Müller, and M. Atzori, “A quantitative taxonomy of human hand grasps,” J. Neuroeng. Rehabil., vol. 16, no. 1, p. 28, Dec. 2019, doi: 10.1186/s12984-019-0488-x.
Contact information: Neuroengineering group, Nebojsa Malesevic
EMG control of prosthetic hand based on isotonic movements
Objective: Record a database of high-density surface EMG (HDsEMG) signals during isotonic contractions of different muscles. Implement and test existing algorithms to estimate muscle force and joint angle.
Approach: The first aim of this project is to record a database of HDsEMG signals during isotonic muscle contractions. The equipment for recording HDsEMG signals is available in the Neuroengineering laboratory, including EMG amplifier for 400 channels, high-density electrodes, recording software and weights. The following tasks should be conducted to record the database: (1) familiarization with the equipment and the software, (2) investigate which muscles can be recorded with the setup available at our laboratory, (3) defining of recording protocol, (4) recording of HDsEMG on at least 10 volunteers. The second aim of this project is to use the recorded database to design and test machine-learning algorithms for the estimation of load and joint angle. The basic algorithms will be given/provided by the supervisors. The task will be to tune the algorithms for this specific application and construct methods for training and testing of the algorithms (splitting the data, providing ground truth...).
Relevant skills and background: Programming skills (LabVIEW and/or Python, and/or Matlab), and machine learning skills
Equipment to be used: OtBioelettronica Quattrocento amplifier, joint angle measurement system (Cyberglove), PC with relevant software.
Relevant literature:
Contact information: Neuroengineering group, Christian Antfolk, Nebojsa Malesevic
Adaptation to perturbations during execution of functional tasks with prosthetic hand (while imposing perturbation of EMG control and feedback)
Objective: Conduct an experiment on the influence of perturbations in prosthetic control or feedback on the execution of functional tasks. Analyze the results of the experiment.
Approach: The aim of this project is to design, conduct and analyze the results of the experiment based on pick-and-place protocol. Healthy volunteers will be wearing a socket on their hand, on which a prosthetic hand will be mounted. By contracting their muscles, the hand will close and open. When squeezing a senzorized device, vibration feedback will be given to the user to indicate squeezing force. During the experiment, the control parameters (gains of EMG amplifiers that define transfer muscle contraction -> force of prosthetic hand) should be changed from time to time. Also, type feedback (number of feedback levels, direct or inverse proportionality between grip force and vibration strength) and will be changed from time to time. The goal is to evaluate how much time is needed for subjects to adapt to new control or feedback paradigm. To do so, several performance indicators will be tracked, such as duration of individual pick-and-place tasks, errors (dropped bottle). In addition, using eye-tracking device it will be possible to evaluate the amount of focus that a person dedicates to each task, indicating personal confidence in the control of prosthetic hand.
Relevant skills and background: Programming skills (Arduino, LabVIEW and/or Python, and/or Matlab)
Equipment to be used: Eye-tracking device (Tobii), Prosthetic hand (OttoBock), myocontrol sensors, miniature vibrational motor and sensorized object.
Relevant literature:
J. V. V. Parr, S. J. Vine, N. R. Harrison, and G. Wood, “Examining the Spatiotemporal Disruption to Gaze When Using a Myoelectric Prosthetic Hand,” J. Mot. Behav., vol. 50, no. 4, pp. 416–425, Jul. 2018, doi: 10.1080/00222895.2017.1363703.
Contact information: Neuroengineering group, Christian Antfolk, Nebojsa Malesevic
Eye-tracking for functional tests
Objective: Design a protocol for hand functional tests. Compare conventional performance metrics with the eye movement
Approach: The aim of the project is to find correlation (or absence of correlation) between conventional ways of quantifying performance during hand functional tests (such as number of repetitions in a specified time, number of errors...) and eye-movement parameters (such as number of saccades and fixations during each repetition). The recording protocol should be based on one of the common hand function test (such as ARAT).
Relevant skills and background: Signal processing (Python/Matlab), Statistics.
Equipment to be used: Eye-tracking device (Tobii or similar), objects and equipment required for performing functional tests.
Relevant literature:
Å. Nordin, M. A. Murphy, and A. Danielsson, “Intra-rater and inter-rater reliability at the item level of the Action Research arm test for patients with stroke,” J. Rehabil. Med., vol. 46, no. 8, pp. 738–745, Sep. 2014.
J. V. V. Parr, S. J. Vine, N. R. Harrison, and G. Wood, “Examining the Spatiotemporal Disruption to Gaze When Using a Myoelectric Prosthetic Hand,” J. Mot. Behav., vol. 50, no. 4, pp. 416–425, Jul. 2018, doi: 10.1080/00222895.2017.1363703.
Contact information: Neuroengineering group, Christian Antfolk, Nebojsa Malesevic
Generation of synthetic electromyography (EMG) data
Background: Generating reliable synthetic data is important for improving our understanding of real EMG recordings and will be directly relevant for mass testing of algorithms related to decomposition of such signals. The EMG signal is generated by motor units which are groups of cylindrical muscle fibers that contract in unison. The motor unit action potential is spread out both in time and along the length of the fibers and passes through the body which acts as a volume conductor. The implementation of a comprehensive and adaptable mathematical model from this thesis work, will be used to assess decomposition algorithms for large amounts of data with ground truth.
Objective: Implement a comprehensive and adaptable mathematical model for the generation of EMG data. Approach: The work will consist of defining and implementing a realistic mathematical model, with many variable parameters. This includes motor unit characteristics (like shape and firing frequency) and volume conductor parameters (like conductivity and tissue layers), as well as noise. The implementation will be done in MATLAB or Pyhon (numpy/scipy).
Relevant skills and background: Python/Matlab programming, (bio) signal processing and modelling.
Equipment to be used: PC with relevant software.
Relevant literature:
Merletti, R., Conte, L. L., Avignone, E., & Guglielminotti, P. (1999). Modeling of surface myoelectric signals. I. Model implementation. IEEE transactions on biomedical engineering, 46(7), 810-820.
Merletti, R., Roy, S. H., Kupa, E., Roatta, S., & Granata, A. (1999). Modeling of surface myoelectric signals. II. Model-based signal interpretation. IEEE Transactions on biomedical engineering, 46(7), 821-829.
Farina, D., Mesin, L., Martina, S., & Merletti, R. (2004). A surface EMG generation model with multilayer cylindrical description of the volume conductor. IEEE Transactions on Biomedical Engineering, 51(3), 415-426.
Contact information: Neuroengineering group, Christian Antfolk, Jonathan Lundsberg