Ayyoubi Amir Hossein, Besheli Behrang Fazli, Swamy Chandra Prakash, Okkabaz Jhan L, Miller Kai J, Worrell Gregory A, Ince Nuri F
Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA.
Department of Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN, USA.
Conf Proc (Midwest Symp Circuits Syst). 2024 Aug;2024:1319-1323. doi: 10.1109/mwscas60917.2024.10658832. Epub 2024 Sep 16.
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.
本研究提出了一种用于同步双脑交互(BIC)系统记录的新型数据采集框架。该设置通过提供多达64个通道的访问权限来扩展数据记录能力。该环境利用我们的Simulink模型,纳入了使用主时钟进行同步和基于电子邮件的状态更新功能。我们在实验室模拟中评估了该框架,观察到系统之间同步后延迟为38毫秒。我们还证明,通过调整主时钟分辨率和数据缓冲区大小,该误差可降至低至5毫秒。我们高精度估计了单元的采样频率,以避免失步。我们在与临床系统同时进行颅内脑电图(iEEG)记录时评估了该设置,并对同步后的iEEG进行了尖峰检测。我们观察到双BIC与临床系统之间的相似度超过95%。此外,我们探索了系统之间接地和参考连接的最佳配置,以实现最高信号质量,并研究了双系统操作中频率干扰的影响。