An open-source human-in-the-loop BCI research framework: method and design

Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in several applied areas, ranging from medical applications to entertainment industry, and creates new conditions for basic research in cognitiv...

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Veröffentlicht in:Frontiers in human neuroscience Jg. 17; S. 1129362
Hauptverfasser: Gemborn Nilsson, Martin, Tufvesson, Pex, Heskebeck, Frida, Johansson, Mikael
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Research Foundation 27.06.2023
Frontiers Media S.A
Schlagworte:
Algorithms
Brain research
brain-computer interface
Classification
Cognitive ability
Control Engineering
Data processing
EEG
Electrical Engineering, Electronic Engineering, Information Engineering
Electroencephalography
Elektroteknik och elektronik
Engineering and Technology
Experiments
Human Neuroscience
Machine learning
Medical imaging
Nervous system
Neural networks
Neuroimaging
Neurosciences
online
Paradigms
real-time
Reglerteknik
research framework
Signal processing
Software
Stimulus control
Teknik
Transfer learning
online
real-time
research framework
EEG
brain-computer interface
ISSN:1662-5161, 1662-5161
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Zusammenfassung:Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in several applied areas, ranging from medical applications to entertainment industry, and creates new conditions for basic research in cognitive neuroscience. The BCIs of today, however, offer only crude online classification of the user's current state of mind, and more sophisticated decoding of mental states depends on time-consuming offline data analysis. The present paper addresses this limitation directly by leveraging a set of improvements to the analytical pipeline to pave the way for the next generation of online BCIs. Specifically, we introduce an open-source research framework that features a modular and customizable hardware-independent design. This framework facilitates human-in-the-loop (HIL) model training and retraining, real-time stimulus control, and enables transfer learning and cloud computing for the online classification of electroencephalography (EEG) data. Stimuli for the subject and diagnostics for the researcher are shown on separate displays using web browser technologies. Messages are sent using the Lab Streaming Layer standard and websockets. Real-time signal processing and classification, as well as training of machine learning models, is facilitated by the open-source Python package Timeflux. The framework runs on Linux, MacOS, and Windows. While online analysis is the main target of the BCI-HIL framework, offline analysis of the EEG data can be performed with Python, MATLAB, and Julia through packages like MNE, EEGLAB, or FieldTrip. The paper describes and discusses desirable properties of a human-in-the-loop BCI research platform. The BCI-HIL framework is released under MIT license with examples at: bci.lu.se/bci-hil (or at: github.com/bci-hil/bci-hil ).
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Edited by: Ines Chihi, University of Luxembourg, Luxembourg
These authors have contributed equally to this work and share first authorship
Reviewed by: Sheng-Fu Liang, National Cheng Kung University, Taiwan; Yaqi Chu, Chinese Academy of Sciences, China; Sergio José Rodríguez Méndez, Australian National University, Australia
ISSN:1662-5161
1662-5161
DOI:10.3389/fnhum.2023.1129362