RaveDev

Our project proposed a novel amplifier clip and integrates state-of-the-art data analysis modules to publicly available UI tools for iEEG research.

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Abstract

Recording and analyzing neural data has opened new doors in neuroscience discovery in the past decade, but the field suffers from lack of scalability and reproducibility. Individual labs often develop custom means to address recording issues and create their own data processing pipelines from scratch, making the process error prone and hard to replicate. Our project aims to enhance data acquisition stability and data analysis procedures for intracranial electroencephalography (iEEG) experiments through two separate solutions: a 3D printable clip mechanism to secure the connection between the amplifier and the signal-receiving device, and a new data analysis module for performing state-of-the-art power spectrum analysis in RAVE, a no-code iEEG data analysis platform. Both products are scalable and allow research labs to use cheap, standardized approaches to data acquisition and analysis that make the process user-friendly and faster to execute. Further, a no-code environment enhances access to computational neuroscience to researchers from non-computational backgrounds who may lack the coding skills and resources to effectively utilize current code-based analysis approaches, increasing diversity in the field.

Project Objectives

Our project introduces two solutions to these challenges:

  • To improve data acquisition, we propose a secure clip mechanism to stabilize the connection between the amplifier and the signal-receiving device, ensuring reliable data collection during procedures.
  • For streamlined result interpretation, we develop an advanced data analysis module that integrates with an existing software platform RAVE to enhance the usability, scalability, and effectiveness of iEEG data analysis for researchers.

This proposed solution has several key advantages:

  1. Easy attachment and removal of amplifier
  2. Compact physical design to fit within limited space during data acquisition
  3. Improved quality of neural recordings
  4. Centralization of all data wrangling and analysis onto a single software platform
  5. Quick, dynamic visualization of data with support for parameter adjustment
  6. Access to a state-of-the-art signal processing toolkit for all regardless of technical skills
  7. Easy reproducibility of data processing steps to facilitate reproduction of results

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