Meta-Analysis of Scholarly and Societal Impact in Brain-Computer Interface Research

Overview of the Project:

This project conducts a comprehensive meta-analysis of the BCI research landscape, examining how different subfields (e.g., motor imagery, P300, SSVEP, affective BCI) perform in terms of scholarly citations, altmetric attention, and societal framing. While most BCI studies focus on technical performance (e.g., signal classification), little is known about their real-world influence, scholarly and public visibility, or alignment with broader societal goals such as accessibility, inclusion, or ethical deployment.

The study aims to study different types of BCI research and their academic visibility, public engagement through social media or news outlets, and societal relevance. This will help uncover structural patterns in the field and inform future research design, communication strategies, and funding priorities.

Goal of the Research:

The primary goal is to map and compare scholarly, public, and societal impact across the BCI literature using open-access metadata and altmetric indicators. The study seeks to answer:

  • Which BCI paradigms attract the most citations and online attention?
  • How often is BCI research framed in terms of societal relevance (e.g., accessibility, neuroethics)?
  • Are there disconnects between technically innovative and socially impactful BCI work?

By synthesizing publication data, altmetrics, and content annotations, this research will generate new insights into how different types of BCI research resonate within and beyond academia.

Tasks and Techniques:

  • Corpus Building: Extracting metadata for BCI-related publications (title, abstract, year, authors, citations, journal).
  • Altmetric Enrichment: Use Altmetric.com or Crossref Event Data APIs to retrieve online attention data (e.g., Twitter, blogs, news, Mendeley readers).
  • Content Annotation: Classify papers by BCI modality (e.g., MI, P300), application area, and presence of societal impact framing using keyword-based and manual tagging.
  • Bibliometric and Altmetric Analysis: Perform trend analysis, correlation studies (e.g., citations vs. altmetrics), and topic modeling or clustering.
  • Societal Insight Synthesis: Identify papers and subfields with high societal relevance and map geographic or disciplinary trends.

How Will Students Participate?

Undergraduate students will take active roles in both the data analysis and interpretation phases. They will:

  • Collect and clean article metadata and altmetric data using Python (e.g., Pandas, Requests, Altmetric API).
  • Implement keyword tagging and basic NLP to identify BCI type and societal framing.
  • Run statistical and trend analyses using tools like Scikit-learn, Matplotlib, and Seaborn.
  • Collaboratively interpret findings, produce visualizations, and contribute to drafting the results and discussion sections.
  • Learn reproducible research methods and gain experience at the intersection of neuroscience, informetrics, and science communication.

 

Name of research group, project, or lab
The BrainReach Project
Why join this research group or lab?

Join a unique, interdisciplinary project exploring how BCI research makes an impact - scientifically, publicly, and socially. You will gain hands-on experience in bibliometrics, altmetrics, and content analysis, while learning valuable skills in data science, NLP, and science communication. This is a chance to work at the intersection of neuroscience and scholarly impact, contributing to how future neurotechnologies are understood, used, and valued.

Representative publication
s11192-016-2077-0.pdf
Logistics Information:
Project categories
Computer Science & Engineering
Information Technology, Analytics, and Operations
Student ranks applicable
Junior
Senior
Student qualifications

1. Research and Data Retrieval

  • Experience with systematic literature review methods and meta-analysis
  • Familiarity with bibliographic databases (e.g., Scopus, PubMed).
  • Ability to use reference management tools (e.g., Zotero, EndNote, Mendeley).

2. Programming and Data Handling

  • Basic proficiency in Python or R for data processing and analysis.
  • Ability to work with APIs (e.g., Altmetric.com, Crossref Event Data).
  • Skills in data cleaning, merging, and preprocessing (e.g., using Pandas).

3. Text and Content Analysis

  • Understanding of qualitative content analysis and thematic coding.
  • Experience with text analysis tools
  • Ability to classify and tag documents based on content and metadata.

4. Statistical Analysis

  • Knowledge of basic statistics (correlation, regression, descriptive analysis).
  • Experience with statistical packages (e.g., Scikit-learn, statsmodels, SPSS).
  • Competence in interpreting and visualizing quantitative results.

5. Data Visualization

  • Familiarity with visualization libraries (e.g., Matplotlib, Seaborn).
  • Ability to create clear and interpretable charts to communicate findings.

6. Scientific Writing and Communication

  • Academic writing skills for composing summaries, analyses, and research reports.
  • Ability to synthesize complex results into coherent insights.
  • Awareness of science communication and research impact frameworks (e.g., bibliometrics, altmetrics).
Hours per week
2 credits / 6-12 hours
3 credits / 12+ hours
Compensation
Research for Credit
Number of openings
1
Techniques learned
  • Systematic literature review and metadata extraction
  • Bibliometric and altmetric analysis
  • Text classification using NLP
  • Data cleaning and merging (Python)
  • Correlation and trend analysis
  • Visualization (Matplotlib, Seaborn)
  • Societal impact tagging and interpretation
Project start
August 2025
Contact Information:
Mentor
Aarthy Nagarajan
anagaraj@nd.edu
Visiting Assistant Professor
Name of project director or principal investigator
Prof. Aarthy Nagarajan
Email address of project director or principal investigator
anagaraj@nd.edu
1 sp. | 0 appl.
Hours per week
2 credits / 6-12 hours (+1)
2 credits / 6-12 hours3 credits / 12+ hours
Project categories
Computer Science & Engineering (+1)
Computer Science & EngineeringInformation Technology, Analytics, and Operations