# Riemannian classification of EEG signals with missing values

arXiv: https://arxiv.org/abs/2110.10011 · HTML: https://mlsanity.com/paper/2110.10011/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [MOABB: Trustworthy algorithm benchmarking for BCIs](https://mlsanity.com/paper/1805.06427/) (2018)
- [Imputation and low-rank estimation with Missing Not At Random data](https://mlsanity.com/paper/1812.11409/) (2018)
- [A Plug&Play P300 BCI Using Information Geometry](https://mlsanity.com/paper/1409.0107/) (2014)
- [Robust low-rank covariance matrix estimation with a general pattern of missing values](https://mlsanity.com/paper/2107.10505/) (2021)
- [EEG Channel Interpolation Using Deep Encoder-decoder Netwoks](https://mlsanity.com/paper/2009.12244/) (2020)

## Built on by (most cited first, 2 of 2)

- [Riemannian Geometry-Based EEG Approaches: A Literature Review](https://mlsanity.com/paper/2407.20250/) (2024)
- [Towards Generalizable Learning Models for EEG-Based Identification of Pain Perception](https://mlsanity.com/paper/2508.11691/) (2025)

## Abstract (arXiv)

This paper proposes a strategy to handle missing data for the classification of electroencephalograms using covariance matrices. It relies on the observed-data likelihood within an expectation-maximization algorithm. This approach is compared to two existing state-of-the-art methods: (i) covariance matrices computed with imputed data; (ii) Riemannian averages of partially observed covariance matrix. All approaches are combined with the minimum distance to Riemannian mean classifier and applied to a classification task of two widely known paradigms of brain-computer interfaces. In addition to be applicable for a wider range of missing data scenarios, the proposed strategy generally performs better than other methods on the considered real EEG data.