# Minimizing subject-dependent calibration for BCI with Riemannian transfer learning

arXiv: https://arxiv.org/abs/2111.12071 · HTML: https://mlsanity.com/paper/2111.12071/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [MOABB: Trustworthy algorithm benchmarking for BCIs](https://mlsanity.com/paper/1805.06427/) (2018)

## Built on by (most cited first, 6 of 6)

- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding](https://mlsanity.com/paper/2401.10746/) (2024)
- [Evaluating the structure of cognitive tasks with transfer learning](https://mlsanity.com/paper/2308.02408/) (2023)
- [Multi-Armed Bandits in Brain-Computer Interfaces](https://mlsanity.com/paper/2205.09584/) (2022)
- [Learning aligned EEG representations with subject-specific encoders](https://mlsanity.com/paper/2606.16462/) (2026)
- [Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding](https://mlsanity.com/paper/2609.33880/) (2026)

## Abstract (arXiv)

Calibration is still an important issue for user experience in Brain-Computer Interfaces (BCI). Common experimental designs often involve a lengthy training period that raises the cognitive fatigue, before even starting to use the BCI. Reducing or suppressing this subject-dependent calibration is possible by relying on advanced machine learning techniques, such as transfer learning. Building on Riemannian BCI, we present a simple and effective scheme to train a classifier on data recorded from different subjects, to reduce the calibration while preserving good performances. The main novelty of this paper is to propose a unique approach that could be applied on very different paradigms. To demonstrate the robustness of this approach, we conducted a meta-analysis on multiple datasets for three BCI paradigms: event-related potentials (P300), motor imagery and SSVEP. Relying on the MOABB open source framework to ensure the reproducibility of the experiments and the statistical analysis, the results clearly show that the proposed approach could be applied on any kind of BCI paradigm and in most of the cases to significantly improve the classifier reliability. We point out some key features to further improve transfer learning methods.