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Investigating Corticomuscular Coherence during single and dual tasks during upper limb rehabilitation
This project explores the relationship between corticomuscular coherence (CMC) and muscular and
cognitive fatigue during dual-task performance. We will utilize synchronized EMG and EEG data collected with the
The Quattrocento system from OTBioelettronica will 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 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 of 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].
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 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
Requirements Programming skills in Python, interest in neuronal systems (biological or artificial), and knowledge of 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].
