An Adaptive Approach for Task-Driven BCI Calibration

Frida Heskebeck; Carolina Bergeling

Abstract

One of the most significant obstacles for the everyday use of systems based on BrainComputer Interfaces (BCIs) is the tediousness of calibration. Successful improvements on calibration, particularly the time needed and the userexperience, have been made with, e.g., transfer learning, gamification, and task estimation [1, 2, 3]. In
this work, we present an adaptive approach to BCI systems’ calibration with a model that evaluates if more calibration is needed. We inspect the model in its simplest form to showcase its versatility.

Original language	English
Publication status	Published - 2021 Jun
Event	BCI meeting 2021 - Virtual Duration: 2021 Jun 7 → 2021 Jun 9 https://bcisociety.org/bci-meeting/

Conference

Conference	BCI meeting 2021
Period	2021/06/07 → 2021/06/09
Internet address	https://bcisociety.org/bci-meeting/

Subject classification (UKÄ)

Control Engineering

Free keywords

BCI
Calibration
EEG
brain-computer interface

Access to Document

Abstract.Frida.HeskebeckFinal published version, 142 KB

https://bcisociety.org/wp-content/uploads/2021/05/vBCI-Abstract-Book-.pdf

1 Licentiate Thesis

On Calibration Algorithms for Real-Time Brain-Computer Interfaces
Heskebeck, F., 2023 Oct 13, Lund: Department of Automatic Control, Lund Institute of Technology, Lund University.
Research output: Thesis › Licentiate Thesis

Open Access
File

2 Finished

Optimizing the Next Generation Brain Computer Interfaces using Cloud Computing
Heskebeck, F. (Researcher), Bernhardsson, B. (Supervisor), Bergeling, C. (Assistant supervisor) & Hägglund, T. (Assistant supervisor)
2019/08/05 → 2024/08/01
Project: Dissertation
Realtime Individualization of Brain Computer Interfaces
Bergeling, C. (Researcher), Bernhardsson, B. (PI), Sandsten, M. (PI), Heskebeck, F. (Research student) & Anderson, R. (Researcher)
2019/06/01 → 2022/06/01
Project: Research

Cite this

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title = "An Adaptive Approach for Task-Driven BCI Calibration",

abstract = "One of the most significant obstacles for the everyday use of systems based on BrainComputer Interfaces (BCIs) is the tediousness of calibration. Successful improvements on calibration, particularly the time needed and the userexperience, have been made with, e.g., transfer learning, gamification, and task estimation [1, 2, 3]. Inthis work, we present an adaptive approach to BCI systems{\textquoteright} calibration with a model that evaluates if more calibration is needed. We inspect the model in its simplest form to showcase its versatility.",

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author = "Frida Heskebeck and Carolina Bergeling",

year = "2021",

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TY - CONF

T1 - An Adaptive Approach for Task-Driven BCI Calibration

AU - Heskebeck, Frida

AU - Bergeling, Carolina

PY - 2021/6

Y1 - 2021/6

N2 - One of the most significant obstacles for the everyday use of systems based on BrainComputer Interfaces (BCIs) is the tediousness of calibration. Successful improvements on calibration, particularly the time needed and the userexperience, have been made with, e.g., transfer learning, gamification, and task estimation [1, 2, 3]. Inthis work, we present an adaptive approach to BCI systems’ calibration with a model that evaluates if more calibration is needed. We inspect the model in its simplest form to showcase its versatility.

AB - One of the most significant obstacles for the everyday use of systems based on BrainComputer Interfaces (BCIs) is the tediousness of calibration. Successful improvements on calibration, particularly the time needed and the userexperience, have been made with, e.g., transfer learning, gamification, and task estimation [1, 2, 3]. Inthis work, we present an adaptive approach to BCI systems’ calibration with a model that evaluates if more calibration is needed. We inspect the model in its simplest form to showcase its versatility.

KW - BCI

KW - Calibration

KW - EEG

KW - brain-computer interface

M3 - Abstract

T2 - BCI meeting 2021

Y2 - 7 June 2021 through 9 June 2021

ER -

An Adaptive Approach for Task-Driven BCI Calibration

Abstract

Conference

Subject classification (UKÄ)

Free keywords

Access to Document

Fingerprint

Research output

On Calibration Algorithms for Real-Time Brain-Computer Interfaces

Projects

Optimizing the Next Generation Brain Computer Interfaces using Cloud Computing

Realtime Individualization of Brain Computer Interfaces

Cite this