Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models

Manfred Hartmann; Lukas Lechner; Asbjoern Wulff Helge; Esben Ahrens; Martin Bachler; Bernhard Hametner; Gerhard Gritsch; Tilmann Kluge; Manfred Hartmann

doi:10.1109/bsn58485.2023.10331106

Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models

Manfred Hartmann (Vortragende:r), Lukas Lechner, Asbjoern Wulff Helge, Esben Ahrens, Martin Bachler, Bernhard Hametner, Gerhard Gritsch, Tilmann Kluge, Manfred Hartmann

Medical Signal Analysis

Publikation: Beitrag in Buch oder Tagungsband › Beitrag in Tagungsband mit Posterpräsentation › Begutachtung

Abstract

The increasing availability of minimally invasive electroencephalogram (EEG) devices for ultra-long-term recordings has opened new possibilities for advanced EEG analysis, but the resulting large amount of generated data leads to a strong need for computational analyses. Deep neural networks (DNNs) have shown to be powerful for this purpose, but the lack of annotated data from these novel devices is a barrier to DNN training. We propose a novel technique based on fine-tuning of linear pre-processing filters, which is capable of compensating for variations in electrode positions and amplifier characteristics and enables training of models for subcutaneous EEG on largely available scalp EEG data. The effectiveness of the method is demonstrated on a state-of-the-art EEG-based sleep scoring model, where we show that the performance on a database used for training can be retained on the subcutaneous EEG by fine-tuning on data from only three subjects.

Originalsprache	Englisch
Titel	2023 IEEE 19th International Conference on Body Sensor Networks (BSN)
Untertitel	Conference Proceedings
Erscheinungsort	Boston, Massachusetts, USA
Kapitel	Monday, October 9, 2023
Seiten	1-4
Seitenumfang	4
DOIs	https://doi.org/10.1109/bsn58485.2023.10331106
Publikationsstatus	Veröffentlicht - 9 Okt. 2023
Veranstaltung	IEEE-EMBS International Conference on Body Sensor Networks – Sensor and Systems for Digital Health: Sensors and Systems for Digital Health. - MIT Media Lab, Boston, Cambridge, USA/Vereinigte Staaten Dauer: 9 Okt. 2023 → 11 Okt. 2023 https://bsn.embs.org/2023/

Publikationsreihe

Name	International Workshop on Wearable and Implantable Body Sensor Networks (BSN)
Herausgeber (Verlag)	IEEE
Band	15

Konferenz

Konferenz	IEEE-EMBS International Conference on Body Sensor Networks – Sensor and Systems for Digital Health
Kurztitel	IEEE BSN 2023
Land/Gebiet	USA/Vereinigte Staaten
Stadt	Boston, Cambridge
Zeitraum	9/10/23 → 11/10/23
Internetadresse	https://bsn.embs.org/2023/

Research Field

Medical Signal Analysis

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10.1109/bsn58485.2023.10331106

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Diese Publikation zitieren

Hartmann, M., Lechner, L., Helge, A. W., Ahrens, E., Bachler, M., Hametner, B., Gritsch, G., Kluge, T., & Hartmann, M. (2023). Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models. in 2023 IEEE 19th International Conference on Body Sensor Networks (BSN): Conference Proceedings (S. 1-4). Artikel [P48] (International Workshop on Wearable and Implantable Body Sensor Networks (BSN); Band 15).. https://doi.org/10.1109/bsn58485.2023.10331106

Hartmann, Manfred ; Lechner, Lukas ; Helge, Asbjoern Wulff et al. / Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models. 2023 IEEE 19th International Conference on Body Sensor Networks (BSN): Conference Proceedings. Boston, Massachusetts, USA, 2023. S. 1-4 (International Workshop on Wearable and Implantable Body Sensor Networks (BSN)).

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abstract = "The increasing availability of minimally invasive electroencephalogram (EEG) devices for ultra-long-term recordings has opened new possibilities for advanced EEG analysis, but the resulting large amount of generated data leads to a strong need for computational analyses. Deep neural networks (DNNs) have shown to be powerful for this purpose, but the lack of annotated data from these novel devices is a barrier to DNN training. We propose a novel technique based on fine-tuning of linear pre-processing filters, which is capable of compensating for variations in electrode positions and amplifier characteristics and enables training of models for subcutaneous EEG on largely available scalp EEG data. The effectiveness of the method is demonstrated on a state-of-the-art EEG-based sleep scoring model, where we show that the performance on a database used for training can be retained on the subcutaneous EEG by fine-tuning on data from only three subjects.",

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author = "Manfred Hartmann and Lukas Lechner and Helge, {Asbjoern Wulff} and Esben Ahrens and Martin Bachler and Bernhard Hametner and Gerhard Gritsch and Tilmann Kluge and Manfred Hartmann",

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Hartmann, M , Lechner, L, Helge, AW, Ahrens, E, Bachler, M , Hametner, B , Gritsch, G , Kluge, T & Hartmann, M 2023, Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models. in 2023 IEEE 19th International Conference on Body Sensor Networks (BSN): Conference Proceedings., [P48], International Workshop on Wearable and Implantable Body Sensor Networks (BSN), Bd. 15, Boston, Massachusetts, USA, S. 1-4, IEEE-EMBS International Conference on Body Sensor Networks – Sensor and Systems for Digital Health, Boston, Cambridge, Massachusetts, USA/Vereinigte Staaten, 9/10/23. https://doi.org/10.1109/bsn58485.2023.10331106

Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models. / Hartmann, Manfred (Vortragende:r); Lechner, Lukas; Helge, Asbjoern Wulff et al.
2023 IEEE 19th International Conference on Body Sensor Networks (BSN): Conference Proceedings. Boston, Massachusetts, USA, 2023. S. 1-4 [P48] (International Workshop on Wearable and Implantable Body Sensor Networks (BSN); Band 15).

Publikation: Beitrag in Buch oder Tagungsband › Beitrag in Tagungsband mit Posterpräsentation › Begutachtung

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T1 - Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models

AU - Lechner, Lukas

AU - Helge, Asbjoern Wulff

AU - Ahrens, Esben

AU - Bachler, Martin

AU - Hametner, Bernhard

AU - Gritsch, Gerhard

AU - Kluge, Tilmann

AU - Hartmann, Manfred

A2 - Hartmann, Manfred

N1 - © 2023 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

PY - 2023/10/9

Y1 - 2023/10/9

N2 - The increasing availability of minimally invasive electroencephalogram (EEG) devices for ultra-long-term recordings has opened new possibilities for advanced EEG analysis, but the resulting large amount of generated data leads to a strong need for computational analyses. Deep neural networks (DNNs) have shown to be powerful for this purpose, but the lack of annotated data from these novel devices is a barrier to DNN training. We propose a novel technique based on fine-tuning of linear pre-processing filters, which is capable of compensating for variations in electrode positions and amplifier characteristics and enables training of models for subcutaneous EEG on largely available scalp EEG data. The effectiveness of the method is demonstrated on a state-of-the-art EEG-based sleep scoring model, where we show that the performance on a database used for training can be retained on the subcutaneous EEG by fine-tuning on data from only three subjects.

AB - The increasing availability of minimally invasive electroencephalogram (EEG) devices for ultra-long-term recordings has opened new possibilities for advanced EEG analysis, but the resulting large amount of generated data leads to a strong need for computational analyses. Deep neural networks (DNNs) have shown to be powerful for this purpose, but the lack of annotated data from these novel devices is a barrier to DNN training. We propose a novel technique based on fine-tuning of linear pre-processing filters, which is capable of compensating for variations in electrode positions and amplifier characteristics and enables training of models for subcutaneous EEG on largely available scalp EEG data. The effectiveness of the method is demonstrated on a state-of-the-art EEG-based sleep scoring model, where we show that the performance on a database used for training can be retained on the subcutaneous EEG by fine-tuning on data from only three subjects.

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M3 - Conference Proceedings with Poster Presentation

SN - 979-8-3503-3841-6

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Hartmann M , Lechner L, Helge AW, Ahrens E, Bachler M , Hametner B et al. Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models. in 2023 IEEE 19th International Conference on Body Sensor Networks (BSN): Conference Proceedings. Boston, Massachusetts, USA. 2023. S. 1-4. [P48]. (International Workshop on Wearable and Implantable Body Sensor Networks (BSN)). doi: 10.1109/bsn58485.2023.10331106

Fine-tuning of pre-processing filters enables scalp-EEG based training of subcutaneous EEG models

Abstract

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