Boyla O. Mainsah — accepted AI-conference papers

2026

A Data-Centric Analysis of the Impact of Training Data Quality vs. Quantity on P300 Brain-Computer Interface Performance (Student Abstract)

Arnav Gupta, Albert Liu, Eliza Haines, Riyadh Alghamdi, Aniketh S. Kota, Leslie M. Collins +1

AAAI 2026technical

The current standard for training brain-computer interface (BCI) machine learning models is user-specific. There is a high interest in developing generic models that are trained on data from other users to minimize BCI calibration time; however, this is limited by noisy, non-stationary brain signals

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2025

Assessing the Impact of Population Data Domain Differences on Transfer Learning in P300-based Brain-Computer Interfaces (Student Abstract)

Rally Lin, Christina Mo, Reyan Shariff, Darrick Zhang, Abdullah Alumar, Kaleb Kassaw +2

AAAI 2025technical

Brain-computer interfaces (BCIs) can provide a means of communication for individuals with severe neuromuscular diseases, the target end-users. While personalized BCI machine learning models are the current standard, models trained on data from other users could reduce BCI calibration time. We use a…

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2018

Augmented Latent Dirichlet Allocation (Lda) Topic Model with Gaussian Mixture Topics

Kedar S. Prabhudesai, Boyla O. Mainsah, Leslie M. Collins, Chandra S. Throckmorton

ICASSP 2018accepted

Latent Dirichlet allocation (LDA) is a statistical model that is often used to discover topics or themes in a large collection of documents. In the LDA model, topics are modeled as discrete distributions over a finite vocabulary of words. The LDA is also a popular choice to model other datasets span…

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