Signal-Processing | NINE Lab

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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Paper

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
}