Seyed Yahya Shirazi

The Lab Streaming Layer (LSL) presents a software-based solution for synchronizing data streams across multiple instruments in neurophysiological research. Utilizing per-sample time stamps and LAN-based time synchronization, LSL ensures accurate, continuous recording despite varying device clocks. It automatically corrects for network delays and jitters, maintaining data integrity through disruptions. Supporting over 150 device classes and compatible with numerous programming languages, LSL has become a vital tool for integrating diverse data acquisition systems. Its robustness and adaptability have extended its application beyond research, into art, performance, and commercial realms, making it a cornerstone for multimodal data collection and synchronization.

We developed a novel EEG system with a dual-electrode net structure for noise reduction and precise biosignal capture. Incorporating advanced software for signal processing, this invention enhances EEG accuracy, reduces setup complexity, and broadens EEG applications, including brain-computer interfaces, through real-time noise separation and immersive noise layering techniques.

We show that the effects of fatigue on muscle coordination and neural drive can be reliably characterized using a non-parametric functional muscle network. The network demonstrated a consistent decrease in connectivity after the fatigue intervention, as indicated by network degree, weighted clustering coefficient (WCC), and global efficiency. The graph metrics displayed consistent and significant decreases at the group level, individual subject level, and individual muscle level. The proposed approach has the potential to be a sensitive biomarker of fatigue with superior performance to conventional spectrotemporal measures.

This project investigates different re-referencing approaches for high-density EEG recordings, evaluating their effectiveness in reducing artifacts and improving source localization accuracy. The work contributes to best practices for EEG preprocessing pipelines.

We show that InfoMuNet, a novel functional biomarker based on a nonlinear network graph of muscle connectivity, can quantify the role of sensory information on motor performance. We demonstrate its potential use in precision rehabilitation interventions.

We explored the potential of a functional muscle network to differentiate vocal tasks. The network robustly differentiated vocal tasks, while classic muscle activation assessment failed to differentiate. The study also discovered tasks with the highest network involvement, which may be utilized in the future to monitor voice disorders and rehabilitation.

This project explores how sensory feedback can enhance motor control during desk cycling, with applications in rehabilitation and workplace wellness. The research focuses on optimizing user experience and therapeutic benefits through intelligent feedback systems.

We demonstrate that seated locomotor perturbations produce differential theta-band responses in the anterior cingulate and supplementary motor areas, suggesting that tuning perturbation parameters can potentially modify electrocortical responses.

Download Paper Code and data Abstract Electroencephalography (EEG) and source estimation can be used to identify brain areas activated during a task, which could offer greater insight on cortical dynamics. Source estimation requires knowledge of the locations of the EEG electrodes. This could be provided with a template or obtained by digitizing the EEG electrode locations. Operator skill and inherent uncertainties of a digitizing system likely produce a range of digitization reliabilities, which could affect source estimation and the interpretation of the estimated source locations....

Mismarking fiducial locations can systematically change EEG source locations. We inestigated this effect by systematically moving the fiducial locations to simulate such errors.