EEG-Based Methods to Characterize Memorised Visual Space

Mauro Nascimben; Thomas Zoëga Ramsøy; Luis Emilio Bruni

doi:10.1007/978-3-030-50726-8_72

EEG-Based Methods to Characterize Memorised Visual Space

Mauro Nascimben^*, Thomas Zoëga Ramsøy, Luis Emilio Bruni

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Abstract

One second of memory maintenance was evaluated to determine EEG metrics ability to track memory load and its variations connected with the lateral presentation of objects in the visual hemi-field. An initial approach focused on features gathered from the N2pc time series to detect the memory load using ensemble learners. Conversely, the secondary approach employed a regularised support vector classifier to predict the area of N2pc event-related components, identifying 6 levels of memory load and stimulus location.

Original language	English
Title of host publication	HCI International 2020 - Posters : 22nd International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I
Editors	Constantine Stephanidis, Margherita Antona
Number of pages	8
Publisher	Springer
Publication date	2020
Pages	549-556
ISBN (Print)	978-3-030-50725-1
ISBN (Electronic)	978-3-030-50726-8
DOIs	https://doi.org/10.1007/978-3-030-50726-8_72
Publication status	Published - 2020
Event	22nd International Conference on Human-Computer Interaction, HCII 2020 - Copenhagen, Denmark Duration: 19 Jul 2020 → 24 Jul 2020

Conference

Conference	22nd International Conference on Human-Computer Interaction, HCII 2020
Country/Territory	Denmark
City	Copenhagen
Period	19/07/2020 → 24/07/2020

Series	Communications in Computer and Information Science
Volume	1224 CCIS
ISSN	1865-0929

Bibliographical note

Funding Information:
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie Grant Agreement No 813234.

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Memory load
Retention period
Visual working memory

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10.1007/978-3-030-50726-8_72

https://zenodo.org/record/4226134/files/Nascimben4492.pdf?download=1

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Nascimben, Mauro ; Zoëga Ramsøy, Thomas ; Bruni, Luis Emilio. / EEG-Based Methods to Characterize Memorised Visual Space. HCI International 2020 - Posters: 22nd International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I. editor / Constantine Stephanidis ; Margherita Antona. Springer, 2020. pp. 549-556 (Communications in Computer and Information Science, Vol. 1224 CCIS).

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title = "EEG-Based Methods to Characterize Memorised Visual Space",

abstract = "One second of memory maintenance was evaluated to determine EEG metrics ability to track memory load and its variations connected with the lateral presentation of objects in the visual hemi-field. An initial approach focused on features gathered from the N2pc time series to detect the memory load using ensemble learners. Conversely, the secondary approach employed a regularised support vector classifier to predict the area of N2pc event-related components, identifying 6 levels of memory load and stimulus location.",

keywords = "Memory load, Retention period, Visual working memory",

author = "Mauro Nascimben and {Zo{\"e}ga Rams{\o}y}, Thomas and Bruni, {Luis Emilio}",

note = "Funding Information: This project has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie Grant Agreement No 813234. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG. ; 22nd International Conference on Human-Computer Interaction, HCII 2020 ; Conference date: 19-07-2020 Through 24-07-2020",

year = "2020",

doi = "10.1007/978-3-030-50726-8_72",

language = "English",

isbn = "978-3-030-50725-1",

series = "Communications in Computer and Information Science",

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editor = "Constantine Stephanidis and Margherita Antona",

booktitle = "HCI International 2020 - Posters",

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Nascimben, M, Zoëga Ramsøy, T & Bruni, LE 2020, EEG-Based Methods to Characterize Memorised Visual Space. in C Stephanidis & M Antona (eds), HCI International 2020 - Posters: 22nd International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I. Springer, Communications in Computer and Information Science, vol. 1224 CCIS, pp. 549-556, 22nd International Conference on Human-Computer Interaction, HCII 2020, Copenhagen, Denmark, 19/07/2020. https://doi.org/10.1007/978-3-030-50726-8_72

EEG-Based Methods to Characterize Memorised Visual Space. / Nascimben, Mauro; Zoëga Ramsøy, Thomas; Bruni, Luis Emilio.
HCI International 2020 - Posters: 22nd International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I. ed. / Constantine Stephanidis; Margherita Antona. Springer, 2020. p. 549-556 (Communications in Computer and Information Science, Vol. 1224 CCIS).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - EEG-Based Methods to Characterize Memorised Visual Space

AU - Nascimben, Mauro

AU - Zoëga Ramsøy, Thomas

AU - Bruni, Luis Emilio

N1 - Funding Information: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie Grant Agreement No 813234. Publisher Copyright: © 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - One second of memory maintenance was evaluated to determine EEG metrics ability to track memory load and its variations connected with the lateral presentation of objects in the visual hemi-field. An initial approach focused on features gathered from the N2pc time series to detect the memory load using ensemble learners. Conversely, the secondary approach employed a regularised support vector classifier to predict the area of N2pc event-related components, identifying 6 levels of memory load and stimulus location.

AB - One second of memory maintenance was evaluated to determine EEG metrics ability to track memory load and its variations connected with the lateral presentation of objects in the visual hemi-field. An initial approach focused on features gathered from the N2pc time series to detect the memory load using ensemble learners. Conversely, the secondary approach employed a regularised support vector classifier to predict the area of N2pc event-related components, identifying 6 levels of memory load and stimulus location.

KW - Memory load

KW - Retention period

KW - Visual working memory

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DO - 10.1007/978-3-030-50726-8_72

M3 - Article in proceeding

AN - SCOPUS:85088745713

SN - 978-3-030-50725-1

T3 - Communications in Computer and Information Science

SP - 549

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BT - HCI International 2020 - Posters

A2 - Stephanidis, Constantine

A2 - Antona, Margherita

PB - Springer

T2 - 22nd International Conference on Human-Computer Interaction, HCII 2020

Y2 - 19 July 2020 through 24 July 2020

ER -

Nascimben M, Zoëga Ramsøy T, Bruni LE. EEG-Based Methods to Characterize Memorised Visual Space. In Stephanidis C, Antona M, editors, HCI International 2020 - Posters: 22nd International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I. Springer. 2020. p. 549-556. (Communications in Computer and Information Science, Vol. 1224 CCIS). doi: 10.1007/978-3-030-50726-8_72

EEG-Based Methods to Characterize Memorised Visual Space

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Bibliographical note

Keywords

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