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.