# Sparse Bayesian Modeling of EEG Channel Interactions Improves P300 Brain-Computer Interface Performance

arXiv: https://arxiv.org/abs/2602.17772 · HTML: https://mlsanity.com/paper/2602.17772/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 59 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Builds on (most cited first, 14 of 14)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Scalar-on-Image Regression via the Soft-Thresholded Gaussian Process](https://mlsanity.com/paper/1604.03192/) (2016)
- [Universal Joint Feature Extraction for P300 EEG Classification using Multi-task Autoencoder](https://mlsanity.com/paper/1808.06541/) (2018)
- [Model Selection for High Dimensional Quadratic Regression via Regularization](https://mlsanity.com/paper/1501.00049/) (2014)
- [Scalable Bayesian inference for heat kernel Gaussian processes on manifolds](https://mlsanity.com/paper/2405.13342/) (2024)
- [Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior](https://mlsanity.com/paper/1810.00274/) (2018)
- [Bayesian Inference on Brain-Computer Interfaces via GLASS](https://mlsanity.com/paper/2304.07401/) (2023)
- [Thresholded Multiscale Gaussian Processes with Application to Bayesian Feature Selection for Massive Neuroimaging Data](https://mlsanity.com/paper/1504.06074/) (2015)
- [Penalized Interaction Estimation for Ultrahigh Dimensional Quadratic Regression](https://mlsanity.com/paper/1901.07147/) (2019)
- [End-to-end P300 BCI using Bayesian accumulation of Riemannian probabilities](https://mlsanity.com/paper/2203.07807/) (2022)
- [Latent Subgroup Identification in Image-on-scalar Regression](https://mlsanity.com/paper/2307.00129/) (2023)
- [Bayesian Image-on-Image Regression via Deep Kernel Learning based Gaussian Processes](https://mlsanity.com/paper/2311.05649/) (2023)
- [Modeling Hypergraphs with Diversity and Heterogeneous Popularity](https://mlsanity.com/paper/2501.04251/) (2025)
- [Bayesian Image Mediation Analysis](https://mlsanity.com/paper/2310.16284/) (2023)

## Abstract (arXiv)

Electroencephalography (EEG)-based P300 brain-computer interfaces (BCIs) enable communication without physical movement by detecting stimulus-evoked neural responses. Accurate and efficient decoding remains challenging due to high dimensionality, temporal dependence, and complex interactions across EEG channels. Existing approaches often treat channels independently or rely on black-box models, limiting interpretability and personalization. We propose a sparse Bayesian time-varying regression framework that explicitly models pairwise EEG channel interactions while performing temporal feature selection, where a relaxed-thresholded Gaussian process prior induces structured sparsity in both channel-specific and interaction effects, enabling interpretable identification of task-relevant channels and channel pairs. Applied to a public P300 speller dataset of 55 participants, our method achieves a 96.4% median character-level accuracy using all sequence repetitions and attains the best overall decoding performance among competing statistical and deep learning approaches. Incorporating channel interactions yields subgroup-specific gains in character-level accuracy, particularly among participants abstained from alcohol (median 7%, up to 14%). Importantly, our approach improves median BCI-Utility by more than 10% at its optimal operating point, achieving peak throughput after only six sequence repetitions. These results demonstrate that explicitly modeling structured EEG channel interactions within a principled Bayesian framework enhances accuracy and user-centric throughput, and supports personalization in P300 BCI systems.