A Synchronous iEEG Data Acquisition Framework for Dual Brain Interchange Systems

This study presents a new data acquisition Framework for synchronous dual Brain Interchange (BIC) systems recording. The setup expands the capacity for data recording by offering access to up to 64 channels. The environment utilizes our Simulink model, incorporating functionalities for synchronization using a master clock and email-based status updates. We evaluated the framework in the lab simulations, and we observed a 38 ms post-synchronization delay between the systems. We also demonstrated that this error can be minimized to as low as 5 ms through adjustments in the master clock resolution and data buffer size. We estimated units' sampling frequency with high accuracy to avoid desynchronization. We evaluated the setup on the intracranial EEG (iEEG) recording simultaneously with the clinical system and performed spike detection on the post-synchronized iEEG. We observed over 95% similarity rate between the dual BIC and clinical system. Additionally, we explored the optimal configuration for ground and reference connections between systems to achieve the highest signal quality, along with investigating the implications of frequency interference in dual-system operations.

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Publication Details

Published
2024-08-11
DOI
https://doi.org/10.1109/mwscas60917.2024.10658832
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.34

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article

A Synchronous iEEG Data Acquisition Framework for Dual Brain Interchange Systems

Nuri F. Ince, Chandra Prakash Swamy, Kai J. Miller, Gregory A. Worrell et al.
1 citations
EEG and Brain-Computer Interfaces
0.34
article

A Synchronous iEEG Data Acquisition Framework for Dual Brain Interchange Systems

Nuri F. Ince, Chandra Prakash Swamy, Kai J. Miller, Gregory A. Worrell, Behrang Fazli Besheli, Amir Hossein Ayyoubi, Jhan L. Okkabaz
article en
1 citations

Abstract

This study presents a new data acquisition Framework for synchronous dual Brain Interchange (BIC) systems recording. The setup expands the capacity for data recording by offering access to up to 64 channels. The environment utilizes our Simulink model, incorporating functionalities for synchronization using a master clock and email-based status updates. We evaluated the framework in the lab simulations, and we observed a 38 ms post-synchronization delay between the systems. We also demonstrated that this error can be minimized to as low as 5 ms through adjustments in the master clock resolution and data buffer size. We estimated units' sampling frequency with high accuracy to avoid desynchronization. We evaluated the setup on the intracranial EEG (iEEG) recording simultaneously with the clinical system and performed spike detection on the post-synchronized iEEG. We observed over 95% similarity rate between the dual BIC and clinical system. Additionally, we explored the optimal configuration for ground and reference connections between systems to achieve the highest signal quality, along with investigating the implications of frequency interference in dual-system operations.

Vol. 2024
Mayo Clinic (US), WinnMed (US), Mayo Clinic in Arizona (US)
Mayo Clinic, National Institutes of Health
Openalex Percentile: Top 41%
EEG and Brain-Computer Interfaces
0.34
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