AN ONLINE SPIKE DETECTION AND MONITORING FRAMEWORK IN IEEG RECORDED USING BRAIN INTERCHANGE DEVICE.

In this study, we developed and validated an online analysis framework in MATLAB Simulink for recording and analysis of intracranial electroencephalography (iEEG). This framework aims to detect interictal spikes in patients with epilepsy as the data is being recorded. An online spike detection was performed over 10-minute interictal iEEG data recorded with Brain Interchange CorTec in three human subjects.

Mitigating Effects of Packet Loss in Wireless Neural Data Transfer: Exploring the Influence of Low-Cost Recovery Methods on Spikes and HFOs in iEEG.

The wireless transmission of neural data may pose the risk of packet loss (PL), potentially compromising signal quality or, in extreme cases, causing complete data loss. Addressing lost packets is essential to ensure data integrity and preserve vital neural patterns. This study investigates the effect of PL interference on epilepsy neuro biomarkers, focusing specifically on interictal epileptiform spikes and high frequency oscillations (HFOs), and the performance of the low computational cost interpolation methods.

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.

Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone.

Background: While high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice.

Methods: In response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data.

Surviving extreme radiation.

Tiny animals known as tardigrades use a combination of DNA repair machinery and a novel protein to mend their genome after intense ionizing radiation.