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Improving post stroke upper limb rehabilitation using Dual Tasks
This project aims to advance post-stroke rehabilitation for the upper limb by leveraging dual-task performance as a primary intervention strategy and corticomuscular coherence (CMC) as a key analytical tool. Specifically, we investigate the relationship between CMC and muscular and cognitive fatigue during dual-task activities. Using synchronized EMG and EEG data collected via the Quattrocento system from OTBioelettronica, we examine CMC variations at the onset of these signals during solo and dual motor-cognitive tasks. Focusing on motor (e.g., reach-and-grab) and cognitive (e.g., reading) activities, we aim to develop algorithms for automatically detecting EMG and EEG signal onsets. The ultimate goal is to validate whether CMC coherence increases during dual tasks, providing insights to guide fatigue management and cognitive-motor training interventions. Conducted in collaboration with IIT Genoa and led by Dr. Marianna Semprini, this research seeks to enhance post-stroke recovery by deepening our understanding of cognitive-motor interactions through CMC.
Keywords: corticol-muscular-coherence, EEG, EMG, signal processing, biomedical engineering, rehabilitation, post-stroke
This project aims to explore the relationship between corticomuscular coherence (CMC) and both muscular and cognitive fatigue during dual-task performance. We will utilize synchronized EMG and EEG data collected with the Quattrocento system from OTBioelettronica to investigate variations in CMC between the onset of EMG and EEG signals.
Our primary objective is to develop a pipeline for capturing synchronized EMG and EEG recordings during solo and dual motor and cognitive tasks. We will examine how CMC changes at the onset of these signals and its association with muscular and cognitive fatigue. To achieve this, we will collect data using the Quattrocento system during motor tasks (reach-and-grab, finger movements) and cognitive tasks (reading, mathematical operations), as well as their combination. We will analyze the data to identify CMC variations across task conditions, guided by established methods for calculating and interpreting fatigue indicators. Concurrently, we will develop algorithms to automatically detect the onset of EMG and EEG signals using available datasets.
The goal is to validate the hypothesis that CMC increases in coherence during dual-tasks by performing solo and dual tasks with and without coherence. We anticipate creating a comprehensive dataset to inform the development of interventions for fatigue management or cognitive-motor training programs.
This project, conducted in collaboration with IIT Genoa and led by Dr. Marianna Semprini, seeks to advance our understanding of the interplay between cognitive and muscular processes through the lens of CMC.
**Available:**
• Spiking simulator with chip equations
• A mixed-signal neuromorphic chip, the DYNAPSEx
• OTBio quattrocento: https://otbioelettronica.it/en/quattrocento/
• Available dataset on EMG and EEG
**Requirements**
Programming skills in Python, interest in neuronal systems (biological or artificial), and knowledge about UI development (preferred but not mandatory).
Bibliography
• Babiloni, F., Marzano, N., Del Percio, C., Rossini, P. M., & Inuggi, A. (2018). Exploration of neural synchronization during motor tasks in healthy subjects using high-resolution electroencephalography (EEG).Scientific Reports, 8(1), 4673 [1].
• Li, X., Miao, Q., Zhang, D., & Li, S. (2019). A study on the integration of EEG and EMG for movement classification. Journal of Physics: Conference Series, 1141(1), 012002 [2].
• Colamarino, G., My<bos> et al. (2021). Corticomuscular coherence in physiological and pathological conditions:State of the art and perspectives for clinical applications. Neuroscience & Biobehavioral Reviews, 129, 104-121 [3].
• De Luca, C. J. (2009). Motor unit control: Understanding muscle performance in health and disease. [4] (This source does not have a DOI or a relevant URL for the specific topic but is a well-regarded book on motor unit control)
• Enoka, C. T. (2008). Neuromuscular fatigue: Changes in voluntary muscle activation. Journal of Physiology,568(Pt 1), 11-17 [5].
This project aims to explore the relationship between corticomuscular coherence (CMC) and both muscular and cognitive fatigue during dual-task performance. We will utilize synchronized EMG and EEG data collected with the Quattrocento system from OTBioelettronica to investigate variations in CMC between the onset of EMG and EEG signals. Our primary objective is to develop a pipeline for capturing synchronized EMG and EEG recordings during solo and dual motor and cognitive tasks. We will examine how CMC changes at the onset of these signals and its association with muscular and cognitive fatigue. To achieve this, we will collect data using the Quattrocento system during motor tasks (reach-and-grab, finger movements) and cognitive tasks (reading, mathematical operations), as well as their combination. We will analyze the data to identify CMC variations across task conditions, guided by established methods for calculating and interpreting fatigue indicators. Concurrently, we will develop algorithms to automatically detect the onset of EMG and EEG signals using available datasets. The goal is to validate the hypothesis that CMC increases in coherence during dual-tasks by performing solo and dual tasks with and without coherence. We anticipate creating a comprehensive dataset to inform the development of interventions for fatigue management or cognitive-motor training programs.
This project, conducted in collaboration with IIT Genoa and led by Dr. Marianna Semprini, seeks to advance our understanding of the interplay between cognitive and muscular processes through the lens of CMC.
**Available:** • Spiking simulator with chip equations • A mixed-signal neuromorphic chip, the DYNAPSEx • OTBio quattrocento: https://otbioelettronica.it/en/quattrocento/ • Available dataset on EMG and EEG
**Requirements** Programming skills in Python, interest in neuronal systems (biological or artificial), and knowledge about UI development (preferred but not mandatory). Bibliography • Babiloni, F., Marzano, N., Del Percio, C., Rossini, P. M., & Inuggi, A. (2018). Exploration of neural synchronization during motor tasks in healthy subjects using high-resolution electroencephalography (EEG).Scientific Reports, 8(1), 4673 [1]. • Li, X., Miao, Q., Zhang, D., & Li, S. (2019). A study on the integration of EEG and EMG for movement classification. Journal of Physics: Conference Series, 1141(1), 012002 [2]. • Colamarino, G., My<bos> et al. (2021). Corticomuscular coherence in physiological and pathological conditions:State of the art and perspectives for clinical applications. Neuroscience & Biobehavioral Reviews, 129, 104-121 [3]. • De Luca, C. J. (2009). Motor unit control: Understanding muscle performance in health and disease. [4] (This source does not have a DOI or a relevant URL for the specific topic but is a well-regarded book on motor unit control) • Enoka, C. T. (2008). Neuromuscular fatigue: Changes in voluntary muscle activation. Journal of Physiology,568(Pt 1), 11-17 [5].
Master Thesis.pdf
