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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Vydáno v:	Frontiers in human neuroscience Ročník 17; s. 1129362
Hlavní autoři:	Gemborn Nilsson, Martin, Tufvesson, Pex, Heskebeck, Frida, Johansson, Mikael
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Research Foundation 27.06.2023 Frontiers Media S.A
Témata:	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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Abstract	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 ).
AbstractList	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).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). 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). 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 entirely 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 present an open-source framework with a modular and customizable hardware-independent design, comprising a human-in-the-loop (HIL) model training and retraining, real-time stimulus control, transfer learning, and cloud computing to enable a distributed BCI-HIL research framework for 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 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 ).
Author	Gemborn Nilsson, Martin Tufvesson, Pex Heskebeck, Frida Johansson, Mikael
AuthorAffiliation	1 Department of Automatic Control, Lund University , Lund , Sweden 3 Department of Psychology, Lund University , Lund , Sweden 2 Ericsson Research , Lund , Sweden
AuthorAffiliation_xml	– name: 2 Ericsson Research , Lund , Sweden – name: 3 Department of Psychology, Lund University , Lund , Sweden – name: 1 Department of Automatic Control, Lund University , Lund , Sweden
Author_xml	– sequence: 1 givenname: Martin surname: Gemborn Nilsson fullname: Gemborn Nilsson, Martin – sequence: 2 givenname: Pex surname: Tufvesson fullname: Tufvesson, Pex – sequence: 3 givenname: Frida surname: Heskebeck fullname: Heskebeck, Frida – sequence: 4 givenname: Mikael surname: Johansson fullname: Johansson, Mikael
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37441434$$D View this record in MEDLINE/PubMed
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CitedBy_id	crossref_primary_10_1016_j_ifacol_2023_10_1612 crossref_primary_10_1371_journal_pone_0327791
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Copyright	Copyright © 2023 Gemborn Nilsson, Tufvesson, Heskebeck and Johansson. 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2023 Gemborn Nilsson, Tufvesson, Heskebeck and Johansson. 2023 Gemborn Nilsson, Tufvesson, Heskebeck and Johansson
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Keywords	online real-time research framework EEG brain-computer interface
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 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
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Snippet	Brain-computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in... Brain computer interfaces (BCIs) translate brain activity into digital commands for interaction with the physical world. The technology has great potential in...
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SubjectTerms	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
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