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Source Data Selection for Brain–Computer Interfaces Based on Simple Features

Heskebeck, Frida LU orcid ; Bergeling, Carolina LU orcid and Bernhardsson, Bo LU orcid (2026) In IEEE Access 14. p.36191-36201
Abstract

Carefully selecting the source data is crucial to achieve high performance of transfer learning methods for brain–computer interfaces (BCIs). Especially so in settings where a large amount of source data is available, and finding the optimal source is not computationally feasible. This paper presents a novel method for source selection, the so-called Transfer Performance Predictor (TPP) method. The TPP method is based on computationally simple features, a choice made to enable real-time implementation and reduce calibration time. The presented method outperforms other comparable source selection methods in BCI settings where a large amount of source data is available. By using the TPP method, source selection can be performed quickly... (More)

Carefully selecting the source data is crucial to achieve high performance of transfer learning methods for brain–computer interfaces (BCIs). Especially so in settings where a large amount of source data is available, and finding the optimal source is not computationally feasible. This paper presents a novel method for source selection, the so-called Transfer Performance Predictor (TPP) method. The TPP method is based on computationally simple features, a choice made to enable real-time implementation and reduce calibration time. The presented method outperforms other comparable source selection methods in BCI settings where a large amount of source data is available. By using the TPP method, source selection can be performed quickly with good results for transfer learning performance, which means that the BCI calibration time can be reduced and a new target user can more quickly start using the BCI.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Brain–computer interface, calibration, cross subject, machine learning, Riemannian geometry, source data selection, transfer learning
in
IEEE Access
volume
14
pages
11 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:105032130187
ISSN
2169-3536
DOI
10.1109/ACCESS.2026.3670905
language
English
LU publication?
yes
id
eac7ae52-e476-415b-a9a8-ef56000e31d4
date added to LUP
2026-04-20 12:58:00
date last changed
2026-04-20 12:58:34
@article{eac7ae52-e476-415b-a9a8-ef56000e31d4,
  abstract     = {{<p>Carefully selecting the source data is crucial to achieve high performance of transfer learning methods for brain–computer interfaces (BCIs). Especially so in settings where a large amount of source data is available, and finding the optimal source is not computationally feasible. This paper presents a novel method for source selection, the so-called Transfer Performance Predictor (TPP) method. The TPP method is based on computationally simple features, a choice made to enable real-time implementation and reduce calibration time. The presented method outperforms other comparable source selection methods in BCI settings where a large amount of source data is available. By using the TPP method, source selection can be performed quickly with good results for transfer learning performance, which means that the BCI calibration time can be reduced and a new target user can more quickly start using the BCI.</p>}},
  author       = {{Heskebeck, Frida and Bergeling, Carolina and Bernhardsson, Bo}},
  issn         = {{2169-3536}},
  keywords     = {{Brain–computer interface; calibration; cross subject; machine learning; Riemannian geometry; source data selection; transfer learning}},
  language     = {{eng}},
  pages        = {{36191--36201}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Access}},
  title        = {{Source Data Selection for Brain–Computer Interfaces Based on Simple Features}},
  url          = {{http://dx.doi.org/10.1109/ACCESS.2026.3670905}},
  doi          = {{10.1109/ACCESS.2026.3670905}},
  volume       = {{14}},
  year         = {{2026}},
}