# Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative Models

arXiv: https://arxiv.org/abs/2211.02630 · HTML: https://mlsanity.com/paper/2211.02630/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 29 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [BciPy: Brain-Computer Interface Software in Python](https://mlsanity.com/paper/2002.06642/) (2020)

## Built on by (most cited first, 3 of 3)

- [Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach](https://mlsanity.com/paper/2402.17987/) (2024)
- [Stabilizing Subject Transfer in EEG Classification with Divergence Estimation](https://mlsanity.com/paper/2310.08762/) (2023)
- [MarkovType: A Markov Decision Process Strategy for Non-Invasive Brain-Computer Interfaces Typing Systems](https://mlsanity.com/paper/2412.15862/) (2024)

## Abstract (arXiv)

We study the problem of inferring user intent from noninvasive electroencephalography (EEG) to restore communication for people with severe speech and physical impairments (SSPI). The focus of this work is improving the estimation of posterior symbol probabilities in a typing task. At each iteration of the typing procedure, a subset of symbols is chosen for the next query based on the current probability estimate. Evidence about the user's response is collected from event-related potentials (ERP) in order to update symbol probabilities, until one symbol exceeds a predefined confidence threshold. We provide a graphical model describing this task, and derive a recursive Bayesian update rule based on a discriminative probability over label vectors for each query, which we approximate using a neural network classifier. We evaluate the proposed method in a simulated typing task and show that it outperforms previous approaches based on generative modeling.