EEG | NINE Lab

Age-related Reorganization of Corticomuscular Connectivity During Locomotor Perturbations

Sun, 28 Sep 2025 00:00:00 +0000

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Paper
Code and data available upon request

Abstract

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.

Key findings: Young vs. older adult corticomuscular connectivity

Citation

@ARTICLE{Shirazi2025-cmc,
  title    = "Age-related Reorganization of Corticomuscular Connectivity During
              Locomotor Perturbations",
  author   = "Shirazi, Seyed Yahya and Tasin, Shahamat Mustavi and Huang,
              Helen J",
  journal  = "bioRxiv",
  pages    = "2025.09.28.679054",
  month    =  "28~" # sep,
  year     =  2025,
  doi      = "10.1101/2025.09.28.679054",
  language = "en"
}

NeurIPS 2025 EEG Foundation Challenge Now Open

Wed, 10 Sep 2025 00:00:00 +0000

The NeurIPS 2025 EEG Foundation Challenge is now open for submissions! As one of the organizers, I’m excited to invite the global research community to participate in this groundbreaking competition focused on advancing cross-task and cross-subject EEG decoding.

Challenge Overview

This challenge aims to push the boundaries of EEG analysis by developing foundation models capable of generalizing across different tasks and subjects. Participants will work with large-scale EEG datasets to create models that can:

Decode EEG signals across multiple cognitive tasks
Generalize to new subjects without extensive retraining
Handle the inherent variability in EEG data

Key Dates

Challenge Opens: September 2025
Submission Deadline: November 2025
Winners Announced: December 2025 at NeurIPS

Why Participate?

This challenge represents a unique opportunity to:

Contribute to advancing EEG analysis methodologies
Work with unprecedented large-scale EEG datasets
Compete for prizes and recognition at NeurIPS 2025
Collaborate with leading researchers in neuroscience and machine learning

Get Involved

For more information and to register, visit the official challenge website. We look forward to seeing innovative approaches that will shape the future of EEG analysis and brain-computer interfaces.

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

Thu, 03 Oct 2024 00:00:00 +0000

Download & Explore

📄 Paper - Full research publication
📊 HBN Dataset Portal - Access all 11 dataset releases with technical documentation
📈 Data Dashboard - Explore demographics, mental health patterns, and data quality metrics

Abstract

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 “analysis-ready” data from its high-density (128-channel) electroencephalographic (EEG) recording sessions formatted as Brain Imaging Data Structure (BIDS) datasets (HBN-EEG) with behavioral and task-condition events annotated using Hierarchical Event Descriptors (HED), making it analysis-ready for many purposes, without ‘forensic’ search for unreported details. We also ensured individual data files data and event integrity and marked inconsistencies. HBN-EEG sessions include six tasks, three with no participant behavioral input and three including button press responses following task instructions. Openly available participant information includes age, gender, and four psychopathology dimensions (internalizing, externalizing, attention, and p-factor) derived from bifactor model of questionnaire data. Currently, HBN-EEG data from more than 3,000 participants is freely available on NEMAR (nemar.org) and OpenNeuro in the form of eleven dataset releases, with further dataset releases to follow. The HBN-EEG dataset is intended to support the development and validation of EEG analysis methods, including machine learning and deep learning approaches, and to facilitate the development of EEG-based biomarkers for psychiatric disorders.

The number of subjects per HBN-EEG dataset release

Citation

@ARTICLE{Shirazi2024-ye,
  title    = "{HBN}-{EEG}: The {FAIR} implementation of the Healthy Brain
              Network ({HBN}) electroencephalography dataset",
  author   = "Shirazi, Seyed Yahya and Franco, Alexandre and Hoffmann, Mauricio
              Scopel and Esper, Nathalia and Truong, Dung and Delorme, Arnaud
              and Milham, Michael and Makeig, Scott",
  journal  = "bioRxiv",
  pages    = "2024.10.03.615261",
  month    =  "3~" # oct,
  year     =  2024,
  doi      = "10.1101/2024.10.03.615261",
  language = "en"
}

Age-related Reorganization of Corticomuscular Connectivity During Locomotor Perturbations

Sat, 28 Sep 2024 00:00:00 +0000

Download

Paper
Code and data available upon request

Abstract

Key findings: Young vs. older adult corticomuscular connectivity

Citation

@ARTICLE{Shirazi2025-cmc,
  title    = "Age-related Reorganization of Corticomuscular Connectivity During
              Locomotor Perturbations",
  author   = "Shirazi, Seyed Yahya and Tasin, Shahamat Mustavi and Huang,
              Helen J",
  journal  = "bioRxiv",
  pages    = "2025.09.28.679054",
  month    =  "28~" # sep,
  year     =  2025,
  doi      = "10.1101/2025.09.28.679054",
  language = "en"
}

The Lab Streaming Layer for Synchronized Multimodal Recording

Wed, 14 Feb 2024 00:00:00 +0000

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Paper
Code

Abstract

Accurately recording the interactions of humans or other organisms with their environment or 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) offers a software-based approach to synchronizing data streams based on per-sample time stamps and time synchronization across a common 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 ensure precise, continuous data recording, even in the face of interruptions. The LSL ecosystem has grown to support over 150 data acquisition device classes as of Feb 2024, and establishes interoperability with and among 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 and it is now supported by a wide range of software packages, including major stimulus presentation tools, real-time analysis packages, and brain-computer interfaces. Outside of basic science, research, and development, LSL has been used as a resilient and transparent backend in scenarios ranging from art installations to stage performances, interactive experiences, and commercial deployments. In neurobehavioral studies and other neuroscience applications, LSL facilitates the complex task of capturing organismal dynamics and environmental changes using multiple data streams at a common timebase while capturing time details for every data frame.

The overall LSL design for synchronized data recording

Citation

@ARTICLE{Kothe2024-zm,
  title    = "The Lab Streaming Layer for Synchronized Multimodal Recording",
  author   = "Kothe, Christian and Shirazi, Seyed Yahya and Stenner, Tristan and
              Medine, David and Boulay, Chadwick and Crivich, Matthew I and
              Mullen, Tim and Delorme, Arnaud and Makeig, Scott",
  journal  = "bioRxiv",
  pages    = "2024.02.13.580071",
  month    =  feb,
  year     =  2024,
  language = "en"
}

Biosignal Active Noise Cancellation

Thu, 03 Aug 2023 00:00:00 +0000

Innovation

We developed a revolutionary dual-electrode EEG system that dramatically improves signal quality through active noise cancellation. This patented technology (US Patent Application No. 2023/0240581A1) represents a significant advancement in biosignal detection.

Key Features

Dual-electrode design: Each sensor contains two electrodes - one for biosignals, one for environmental noise
Active noise cancellation: Real-time separation of brain signals from environmental interference
Enhanced comfort: Elastic geodesic net structure for comfortable long-term wear
Reduced setup time: Simplified electrode placement and preparation

Technical Approach

Hardware Innovation

Geodesic net structure with elastic conductive elements
Paired electrode configuration at each sensor location
Integrated analog-to-digital conversion at each electrode

Signal Processing

Advanced noise separation algorithms
Reverse Artifact Subspace Reconstruction (rASR)
Real-time processing capabilities
Multi-modal signal integration

Applications

Brain-Computer Interfaces: Enhanced signal quality for BCI applications
Clinical EEG: Improved diagnostic capabilities in noisy environments
Mobile EEG: Enabling high-quality recordings outside laboratory settings
Research: Opening new possibilities for naturalistic neuroscience studies

Impact

This technology addresses fundamental challenges in EEG recording:

Reduces environmental electrical interference by >80%
Decreases setup time by 50%
Enables recordings in previously unsuitable environments
Maintains signal quality during subject movement

Patent & Publications

US Patent Application 2023/0240581A1
Technical details and validation studies in preparation

Collaborators

UCSD Swartz Center for Computational Neuroscience
Institute for Neural Computation

Differential Theta-Band Signatures of the Anterior Cingulate and Motor Cortices During Seated Locomotor Perturbations

Thu, 04 Feb 2021 00:00:00 +0000

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Abstract

Quantifying motor and cortical responses to perturbations during seated locomotor tasks such as recumbent stepping and cycling will expand and improve the understanding of locomotor adaptation processes beyond just perturbed gait. Using a perturbed recumbent stepping protocol, we hypothesized motor errors and anterior cingulate activity would decrease with time, and perturbation timing would influence electrocortical elicitation. Young adults (n = 17) completed four 10-minute arms and legs stepping tasks, with perturbations applied at every left or right leg extension-onset or mid-extension. A random no-perturbation ``catch" stride occurred in every five perturbed strides. We instructed subjects to follow a pacing cue and to step smoothly, and we quantified temporal and spatial motor errors. We used high-density electroencephalography to estimate sources of electrocortical fluctuations shared among >70% of subjects. Temporal and spatial errors did not decrease from early to late for either perturbed or catch strides. Interestingly, spatial errors post-perturbation did not return to pre-perturbation levels, suggesting use-dependent learning occurred. Theta (3-8 Hz) synchronization in the anterior cingulate cortex and left and right supplementary motor areas (SMA) emerged near the perturbation event, and extension-onset perturbations elicited greater theta-band power than mid-extension perturbations. Even though motor errors did not adapt, anterior cingulate theta synchronization decreased from early to late perturbed strides, but only during the right-side tasks. Additionally, SMA mainly demonstrated specialized, not contralateral, lateralization. Overall, seated locomotor perturbations produced differential theta-band responses in the anterior cingulate and SMAs, suggesting that tuning perturbation parameters, e.g., timing, can potentially modify electrocortical responses.

The cortical areas active in response to mechanical perturbations during seated locomotor tasks

Citation

@INPROCEEDINGS{Shirazi2019-ke,
  title     = "Influence of Mismarking Fiducial Locations on {EEG} Source
               Estimation*",
  author    = "Shirazi, S Y and Huang, H J",
  booktitle = "2019 9th International IEEE/EMBS Conference on Neural Engineering
               (NER)",
  pages     = "377--380",
  month     =  Mar,
  year      =  2019
}

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

Wed, 06 Nov 2019 00:00:00 +0000

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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. Here, we compared the reliabilities 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 0.001 cm. 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.

The five digitizing methods tested in this study

Citation

@ARTICLE{Shirazi2019-im,
  title = "More Reliable {EEG} Electrode Digitizing Methods Can Reduce Source
        Estimation Uncertainty, but Current Methods Already Accurately
        Identify Brodmann Areas",
  author= "Shirazi, Seyed Yahya and Huang, Helen J",
  journal= "Front. Neurosci.",
  volume=  13,
  pages= 1159,
  year= 2019
}