Biology subjects

Shirazi, S. Y.

Publications and source records attributed to Shirazi, S. Y..

13 recordsLinked to original sources

bioRxiv2026

EMG-BIDS: an extension to the Brain Imaging Data Structure for electromyography

Electromyography (EMG) is fundamental to clinical assessment, rehabilitation, neuromuscular research, and human-machine interfaces. Despite decades of use, no widely adopted standard exists for organizing and sharing EMG data, limiting reusability and large-scale data aggregation. We present EMG-BIDS, an extension to the Brain Imaging Data Structure (BIDS) that standardizes the organization of EMG recordings. EMG-BIDS addresses challenges unique to EMG, including diverse electrode types (surface or intramuscular, single channel to high-density arrays), heterogeneous electrode placements across anatomical locations, montages (e.g., monopolar or bipolar sensor designs), and the critical need for transparent documentation of sensor positioning. The specification introduces hierarchical coordinate systems that link local electrode grids to anatomical landmarks, enabling precise and reproducible placement documentation. EMG-BIDS is now part of BIDS as of version 1.11.0, supported by existing tools, including MNE-BIDS and EEGLAB. We demonstrate the specification through public datasets, including high-density surface EMG recordings. EMG-BIDS provides the foundation for FAIR (Findable, Accessible, Interoperable, Reusable) EMG data sharing, enabling meta-analyses, multi-site studies, and machine learning applications that require standardized, well-documented datasets.

neuroscience↗

bioRxiv2026

Secure LSL: A Unified Encryption Architecture for the Lab Streaming Layer

ObjectiveThe Lab Streaming Layer (LSL) protocol, widely adopted for synchronized multimodal biosignal recording in neuroscience research, transmits all data in plaintext, exposing sensitive neural and physiological recordings to interception and tampering and creating regulatory liability for clinical and commercial deployments across most major international jurisdictions. We present Secure LSL, the unified encryption architecture for the protocol: a novel security layer that authenticates devices and encrypts biosignal streams through transparent, drop-in modifications to the core library, requiring no application changes and no recompilation for dynamically linked clients. ApproachWe implement encryption at the liblsl core library level using a shared keypair authorization model with ChaCha20-Poly1305 authenticated encryption. All authorized devices share a common Ed25519 keypair, and public key verification during connection establishment ensures only authorized devices communicate. The architecture enforces network-wide security consensus, requiring all connected devices to operate in either secure or insecure mode, eliminating vulnerable mixed environments, and operates transparently with zero code changes to existing applications. Main ResultsThe architecture preserves application programming interface (API) transparency, so existing applications need no code changes (legacy devices must update to connect to secured outlets). Across five hardware platforms spanning x86 desktop, Apple Silicon laptop, embedded ARM single-board, and Xtensa microcontroller targets, encryption adds sub-millisecond latency in all desktop and embedded ARM configurations, with overhead in the single-digit percent range (approximately 4 to 9%, the lowest values within measurement noise of zero) for typical 64-channel, 1000-Hz configurations. A clean-room ESP32 implementation extends transparent encryption to dual-core microcontrollers with no measurable push-path overhead and approximately 2 kB additional static random-access memory (SRAM) consumption, enabling secured wearable and ambulatory biosensor deployments. SignificanceBy implementing security within the protocol core rather than requiring application-level changes, we transform LSL from a research-only protocol to a security-capable platform for clinical settings, multi-institution collaborations, and commercial products, while preserving its zero-configuration philosophy.

neuroscience↗

bioRxiv2025

Age-related Reorganization of Corticomuscular Connectivity During Locomotor Perturbations

Locomotor perturbations elicit cortical and muscular responses that help minimize motor errors through neural processes involving multiple brain regions. The anterior cingulate cortex monitors motor errors, the supplementary motor areas integrate sensory and executive control, and the posterior parietal cortices process sensorimotor predictions, while muscles show increased activation and co-contraction patterns. With aging, these neural control strategies shift; older adults demonstrate less flexible cortical and muscular responses, using compensatory overactivation and simpler muscle synergies to maintain performance comparable to young adults. We investigated corticomuscular connectivity patterns during perturbed recumbent stepping in seventeen young adults (age 25{+/-}4.9 years) and eleven older adults (age 68{+/-}3.6 years) using high-density EEG (128 electrodes) and EMG from six bilateral muscles. Brief mechanical perturbations (200ms of increased resistance) were applied at left or right leg extension-onset or mid-extension during continuous stepping at 60 steps per minute. We applied independent component analysis, source localization, and direct directed transfer function to quantify bidirectional information flow between cortical clusters and muscles in theta (3-8 Hz), alpha (8-13 Hz), and beta (13-35 Hz) bands. Young adults demonstrated concentrated electrocortical sources in anterior cingulate cortex, bilateral supplementary motor areas, and bilateral posterior parietal cortices, with strong theta-band synchronization following perturbations. In contrast, older adults showed fewer differentiated cortical sources, particularly lacking distinct anterior cingulate activity, and exhibited only minimal synchronization changes. Baseline corticomuscular connectivity was significantly stronger in older adults compared to young adults (p=0.012), suggesting fundamental differences in resting motor control states. During perturbations, young adults employed flexible, task-specific connectivity modulation involving error-processing networks, with the anterior cingulate showing selective bidirectional connectivity changes with specific muscles. Older adults relied on more diffuse (i.e., not focused to specific brain area) connectivity patterns dominated by motor and posterior parietal cortices, with strong connections to multiple upper and lower limb muscles simultaneously. These findings reveal an age-related strategic reorganization from dynamic, error-driven neural control to a more constrained, stability-focused approach that may reflect compensation for sensorimotor changes. The distinct connectivity signatures establish perturbed recumbent stepping as a valuable tool for assessing corticomuscular communication and provide normative benchmarks for developing targeted rehabilitation interventions to restore efficient motor control in aging and neurological populations.

neuroscience↗

bioRxiv2025

Simulating Scalp EEG from Ultrahigh-Density ECoG Data Illustrates Cortex to Scalp Projection Patterns

Ultrahigh-density electrocorticography (ECoG) provides unprecedented spatial resolution for recording cortical electrical activity. This study uses simulated scalp projections from an ECoG recording to challenge the assumption that channel-level electroencephalography (EEG) reflects only local field potentials near the recording electrode. Using a 1024-electrode ECoG array placed on the primary motor cortex during finger movements, we applied Adaptive Mixture Independent Component Analysis (AMICA) to decompose activity into maximally independent grid activity components and projected these to 207 simulated EEG scalp electrode channels using a high-definition MR image-based electrical forward-problem head model. Our findings demonstrate how cortical surface-recorded potentials propagate to scalp electrodes both far from and near to the generating location. This work has significant implications for interpreting both EEG and ECoG data in clinical and research applications. Clinical RelevanceThis study provides insights for interpreting scalp EEG data, demonstrating that scalp channel activity represents a complex mixture of distributed cortical source activities rather than primarily activity generated nearest to the scalp electrodes. These findings may hopefully spur improvement in EEG-based diagnostics for neurological disorders.

neuroscience↗

bioRxiv2024

HBN-EEG: The FAIR implementation of the Healthy Brain Network (HBN) electroencephalography dataset

The Child Mind Institute (CMI) Healthy Brain Network (HBN) project has recorded phenotypic, behavioral, and neuroimaging data from [~]5,000 children and young adults between the ages of 5 and 21. Here, we present HBN-EEG, the "analysis-ready" data from its high-density (128-channel) electroencephalographic (EEG) recording sessions formatted as Brain Imaging Data Structure (BIDS) datasets. HBN-EEG also includes behavioral and task-condition events annotated using Hierarchical Event Descriptors (HED), making the datasets analysis-ready for many purposes without forensic search for unreported details. We also ensured data consistency and event integrity and marked inconsistencies. HBN-EEG sessions include six tasks, three with no participant behavioral input (passive tasks) and three including button press responses following task instructions (active tasks). Openly available participant information includes age, gender, and four psychopathology dimensions (internalizing, externalizing, attention, and p-factor) derived from a bifactor model of questionnaire data. Currently, HBN-EEG data from more than 2,600 participants is freely available on NEMAR (nemar.org) and OpenNeuro in the form of nine dataset releases, with further dataset releases to follow.

neuroscience↗

bioRxiv2024

The Lab Streaming Layer for Synchronized Multimodal Recording

Accurately recording the interactions of humans or other organisms with their environment and other agents requires synchronized data access via multiple instruments, often running independently using different clocks. Active, hardware-mediated solutions are often infeasible or prohibitively costly to build and run across arbitrary collections of input systems. The Lab Streaming Layer (LSL) framework offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common local area netowrk (LAN). Built from the ground up for neurophysiological applications and designed for reliability, LSL offers zero-configuration functionality and accounts for network delays and jitters, making connection recovery, offset correction, and jitter compensation possible. These features can ensure continuous, millisecond-precise data recording, even in the face of interruptions. In this paper, we present an overview of LSL architecture, core features, and performance in common experimental contexts. We also highlight practical considerations and known pitfalls when using LSL, including the need to take into account input device throughput delays that LSL cannot itself measure or correct. The LSL ecosystem has grown to support over 150 data acquisition device classes and to establish interoperability between client software written in several programming languages including C/C++, Python, MATLAB, Java, C#, JavaScript, Rust, and Julia. The resilience and versatility of LSL have made it a major data synchronization platform for multimodal human neurobehavioral recording, now supported by a wide range of software packages including major stimulus presentation tools, real-time analysis envirnoments, and brain-computer interface applications. Beyond basic science, research, and development, LSL has been used as a resilient and transparent back-end in deployment scenarios including interactive art installations, stage performances, and commercial products. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes occurring within and across multiple data streams on a common timeline.

neuroscience↗

bioRxiv2023

Linear versus Nonlinear Muscle Networks: A Case Study to Decode Hidden Synergistic Patterns During Dynamic Lower-limb Tasks

This paper, for the first time, compares the behaviors of nonlinear versus linear muscle networks in decoding hidden peripheral synergistic neural patterns during dynamic functional tasks. In this paper, we report a case study during which one healthy subject conducts a series of four lower limb repetitive tasks. Specifically, the paper focuses on tasks that involve the right knee joint, including walking, sit-tostand, stepping, and drop-jump. Twelve muscles were recorded using the Delsys Trigno system. The linear muscle network was generated using coherence analysis, and the nonlinear network was generated using Spearmans correlation. The results show that the degree, clustering coefficient, and global efficiency of the muscle network have the highest value among tasks in the linear domain for the walking task, while a low linear synergistic network behavior for the sit-to-stand is observed. On the other hand, the results show that the nonlinear functional muscle network decodes high connectivity (degree) and clustering coefficient and efficiency for the sit-tostand when compared with other tasks. We have also developed a two-dimensional functional connectivity plane composed of linear and nonlinear features and shown that it can span the lower-limb dynamic task space. The results of this paper for the first time highlight the importance of observing both linear and nonlinear connectivity patterns, especially for complex dynamic tasks. It should also be noted that through a simultaneous EEG recording (using BrainVision System), we have shown that, indeed, cortical activity may indirectly explain highly-connected nonlinear muscle network for the sit-to-stand task, highlighting the importance of nonlinear muscle network as a neurophysiological window of observation beyond the periphery.

bioengineering↗

bioRxiv2022

Low-frequency motor cortex EEG predicts four levels of rate of change of force during ankle dorsiflexion

The movement-related cortical potential (MRCP) is a low-frequency component of the electroencephalography (EEG) signal recorded from the motor cortex and its neighboring cortical areas. Since the MRCP encodes motor intention and execution, it may be utilized as an interface between patients and neurorehabilitation technologies. This study investigates the EEG signal recorded from the Cz electrode to discriminate between four levels of rate of force development (RFD) of the tibialis anterior muscle. For classification, three feature sets were evaluated to describe the EEG traces. These were (i) MRCP morphological characteristics in the{delta} -band such as amplitude and timing, (ii) MRCP statistical characteristics in the{delta} -band such as mean, standard deviation, and kurtosis, and (iii) wideband time-frequency features in the 0.5-90 Hz range. Using a support vector machine for classification, the four levels of RFD were classified with a mean (SD) accuracy of 82% (7%) accuracy when using the time-frequency feature space, and with an accuracy of 75% (12%) when using the MRCP statistical characteristics. It was also observed that some of the key features from the statistical and morphological sets responded monotonically to the intensity of the RFD. Examples are slope and standard deviation in the (0, 1)s window for the statistical, and min1 and minn for the morphological sets. This monotonical response of features explains the observed performance of the{delta} -band MRCP and corresponding high discriminative power. Results from temporal analysis considering the pre-movement phase ((-3, 0)s) and three windows of the post-movement phase ((0, 1)s, (1, 2)s, and (2, 3)s)) suggest that the complete MRCP waveform represents high information content regarding the planning, execution, duration, and ending of the isometric dorsiflexion task using the tibialis anterior muscle. Results shed light on the role of{delta} -band in translating to motor command, with potential applications in neural engineering systems.

bioengineering↗

bioRxiv2022

InfoMuNet: Information-theory-based Functional Muscle Network Tracks Sensorimotor Integration Post-stroke

Sensory information is critical for motor coordination. However, understanding sensorimotor integration is complicated, especially in individuals with nervous system impairment. This research presents a novel functional biomarker, based on a nonlinear network graph of muscle connectivity, called InfoMuNet, to quantify the role of sensory information in motor performance. Thirty-two individuals with post-stroke hemiparesis performed a grasp-and-lift task while muscle activities were measured using eight surface electromyography (sEMG) sensors. Subjects performed the task with their affected hand before and after exposure to the sensory stimulation elicited by performing the task with the less-affected hand. For the first time, this work shows that InfoMuNet robustly quantifies functional muscle connectivity improvements in the affected hand after exposure of the less-affected side to sensory information. >90% of the subjects conformed with the improvement resulting from this sensory exposure. InfoMuNet also shows high sensitivity to tactile, kinesthetic, and visual input alterations at the subject level, highlighting the potential use in precision rehabilitation interventions.

neuroscience↗

bioRxiv2021

Non-parametric Functional Muscle Network as a Robust Biomarker of Fatigue

The possibility of muscle fatigue detection using surface electromyography has been explored and multiple biomarkers, such as median frequency, have been suggested. However, there are contradictory reports in the literature which results in an inconsistent understanding of the biomarkers of fatigue. Thus, there is an unmet need for a statistically robust sEMG-based biomarker for fatigue detection. This paper, for the first time, demonstrates the superior capability of a non-parametric muscle network to reliably detect fatigue-related changes. Seven healthy volunteers completed a lower limb exercise protocol, which consisted of 30s of a sit-to-stand exercise before and after the completion of fatiguing leg press sets. A non-parametric muscle network was constructed, using Spearmans power correlation and showed a very reliable decrease in network metrics associated with fatigue (degree, weighted clustering coefficient (WCC)). The network metrics displayed a significant decrease at the group level (degree, WCC: p < 0.001), individual subject level (degree: p < 0.035 WCC: p < 0.004) and particular muscle level (degree: p < 0.017). Regarding the decrease in mean degree connectivity at particular muscles, all seven subjects followed the group trend. In contrast to the robust results achieved by the proposed non-parametric muscle network, classical spectrotemporal measurements showed heterogeneous trends at the particular muscle and individual subject levels. Thus, this paper for the first time shows that non-parametric muscle network is a reliable biomarker of fatigue and could be used in a broad range of applications.

neuroscience↗

bioRxiv2021

Characterization of Cervical-Cranial Muscle Network in Correlation with Vocal Features

Objective evaluation of physiological responses using non-invasive methods has attracted great interest regarding the assessment of vocal performance and disorders. This paper, for the first time, demonstrates that the topographical features of the cervical-cranial intermuscular coherence network generated using surface electromyography (sEMG) have a strong potential for detecting subtle changes in vocal performance. For this purpose, in this paper, 12 sEMG signals were collected from six cervical and cranial muscles bilaterally. Data were collected from four subjects without a history of a voice disorder performing a series of vocal tasks. The vocal tasks were varied phonation (an /a/ sustained for the maximal duration with combinations of two levels of loudness and two levels of pitch), a pitch glide from low to high, singing a familiar song, spontaneous speech, and reading with different loudness levels. The varied phonation tasks showed the median degree, and weighted clustering coefficient of the coherence-based intermuscular network ascends monotonically, with a high effect size (|rrb| = 0.52). The set of tasks, including pitch glide, singing, and speech, was significantly distinguishable using the network features as both degree and weighted clustering coefficient had a very high effect size (|rrb| > 0.83) across these tasks. Also, pitch glide has the highest degree and weighted clustering coefficient among all tasks (degree > 0.6, weighted clustering coefficient > 0.6). Spectrotemporal features performed far less effective than the proposed functional muscle network metrics to differentiate the vocal tasks. The highest effect size for spectrotemporal features was only |rrb| = 0.19. In this paper, for the first time, the power of a cervical-cranial muscle network has been demonstrated as a neurophysiological window to vocal performance. The results also shed light on the tasks with the highest network involvement, which may be potentially used in monitoring vocal disorders and tracking rehabilitation progress.

neuroscience↗

bioRxiv2019

More Reliable EEG Electrode Digitizing Methods Can Reduce Source Estimation Uncertainty, But Current Methods Already Accurately Identify Brodmann Areas

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. Here, we compared the reliability of five digitizing methods (ultrasound, structured-light 3D scan, infrared 3D scan, motion capture probe, and motion capture) and determined the relationship between digitization reliability and source estimation uncertainty, assuming other contributors to source estimation uncertainty were constant. We digitized a mannequin head using each method five times and quantified the reliability and validity of each method. We created five hundred sets of electrode locations based on our reliability results and applied a dipole fitting algorithm (DIPFIT) to perform source estimation. The motion capture method, which recorded the locations of markers placed directly on the electrodes had the best reliability with an average electrode variability of 0.001cm. Then, in order of decreasing reliability were the method using a digitizing probe in the motion capture system, an infrared 3D scanner, a structured-light 3D scanner, and an ultrasound digitization system. Unsurprisingly, uncertainty of the estimated source locations increased with greater variability of EEG electrode locations and less reliable digitizing systems. If EEG electrode location variability was ~ 1 cm, a single source could shift by as much as 2 cm. To help translate these distances into practical terms, we quantified Brodmann area accuracy for each digitizing method and found that the average Brodmann area accuracy for all digitizing methods was > 80%. Using a template of electrode locations reduced the Brodmann area accuracy to ~ 50%. Overall, more reliable digitizing methods can reduce source estimation uncertainty, but the significance of the source estimation uncertainty depends on the desired spatial resolution. For accurate Brodmann area identification, any of the digitizing methods tested can be used confidently.

neuroscience↗

bioRxiv2019

Influence of Mismarking Fiducial Locations on EEG Source Estimation

Mismarking locations of the fiducials can have a significant influence on the digitized electrode locations and cortical source estimation using high-density EEG. Under-standing and quantifying how uncertainties in the fiducial locations affect the locations of cortical sources is important for interpreting EEG analyses. We systematically shifted fiducial locations to investigate the relationship between variations of fiducial locations and the corresponding estimations of the source locations. We quantified the uncertainty of the dipole locations using the enclosing volume of the dipole locations and the maximum width of the dipole cluster. Shifting fiducial locations 1.5 cm increased the uncertainty of the dipole locations to span a volume >1 cm3 and about 2.5 cm wide. Results suggest that the fiducials need to be digitized accurately within at least 0.5 cm of the absolute actual fiducial location to limit the uncertainty of a dipole location to <1 cm. Additionally, we used random fiducial shift combinations to estimate the effects of combinations of the fiducial shifts on dipole location estimation. This analysis showed that dipole locations were within the bounds of our dipole estimation uncertainty volumes. Based on the outcomes, we suggest marking fiducials carefully before placement of the cap and to use a digitization method with an accuracy of <0.5 cm.

neuroscience↗