Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity

Naomi Du Bois; Alain Desire Bigirimana; Attila Korik; Lisette Gaju Kéthina; Eugène Rutembesa; Jean Mutabaruka; Leon Mutesa; Girijesh Prasad; Stefan Jansen; Damien Coyle

doi:10.1016/j.dib.2022.108066

Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity

Naomi Du Bois
, Alain Desire Bigirimana
, Attila Korik
, Lisette Gaju Kéthina
, Eugène Rutembesa
, Jean Mutabaruka
, Leon Mutesa
, Girijesh Prasad
, Stefan Jansen
, Damien Coyle

Queens University Belfast
Department of Clinical Psychology, College of Medicine and Health Sciences, University of Rwanda (UR)
Centre for Human Genetics, School of Medicine and Pharmacy, College of Medicine and Health Sciences, University of Rwanda (UR)

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

228 Downloads (Pure)

Abstract

The datasets described here comprise electroencephalography (EEG) data and psychometric data freely available on data.mendeley.com. The EEG data is available in .mat formatted files containing the EEG signal values structured in two-dimensional (2D) matrices, with channel data and trigger information in rows , and samples in columns (having a sampling rate of 250Hz). Twenty-nine female survivors of the 1994 genocide against the Tutsi in Rwanda, underwent a psychological assessment before and after an intervention aimed at reducing Post-Traumatic Stress Disorder (PTSD) symptom severity. Three measures of trauma and four measures of wellbeing were assessed using empirically validated standardised assessments. The pre- and post- intervention psychometric data were analysed using non-parametric statistical methods and the post-intervention data were further evaluated according to diagnostic assessment rules to determine clinically relevant improvements for each group. The participants were assigned to a control group (CG, n = 9), a motor-imagery group (MI, n = 10), and a neurofeedback group (NF, n = 10). Participants in the latter two groups received Brain-Computer Interface (BCI) based training as a treatment intervention over a sixteen-day period, between the pre- and post- clinical interviews. The training involved presenting feedback visually via a videogame, based on real-time analysis of the EEG recorded data during the BCI-based treatment session. Participants were asked to regulate (NF) or intentionally modulate (MI) brain activity to affect/control the game.

Original language	English
Article number	108066
Pages (from-to)	1-15
Number of pages	15
Journal	Data in Brief, Elsevier
Volume	42
Early online date	26 Mar 2022
DOIs	https://doi.org/10.1016/j.dib.2022.108066
Publication status	Published (in print/issue) - 30 Jun 2022

Bibliographical note

Funding Information:
We would like to thank Mrs Caritas Umurerwa, Mr Vestine Mukantwali, and Mrs Immaculee Uwayezu, for their contribution to the project. We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No. EP/T022175 and DC is grateful for the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1) and the Spatial Computing and Neurotechnology Innovation Hub, funded by The Department for the Economy, Northern Ireland.

Funding Information:
We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No. EP/T022175 and DC is grateful for the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1) and the Spatial Computing and Neurotechnology Innovation Hub, funded by The Department for the Economy, Northern Ireland.

Funding Information:
This research was supported by funding from the United Kingdom Research Innovation (UKRI) Global Challenges Research Fund (GCRF), under Grant 71574R. N. du Bois, A. Bigirimana, G. Prasad and D. Coyle were supported in part by the Northern Ireland Functional Brain Mapping Facility Project through Invest NI and the Ulster University under Grant 1303/101154803.

Publisher Copyright:
© 2022

Funding

Funding Information: We would like to thank Mrs Caritas Umurerwa, Mr Vestine Mukantwali, and Mrs Immaculee Uwayezu, for their contribution to the project. We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No. EP/T022175 and DC is grateful for the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1) and the Spatial Computing and Neurotechnology Innovation Hub, funded by The Department for the Economy, Northern Ireland. Funding Information: We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No. EP/T022175 and DC is grateful for the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1) and the Spatial Computing and Neurotechnology Innovation Hub, funded by The Department for the Economy, Northern Ireland. Funding Information: This research was supported by funding from the United Kingdom Research Innovation (UKRI) Global Challenges Research Fund (GCRF), under Grant 71574R. N. du Bois, A. Bigirimana, G. Prasad and D. Coyle were supported in part by the Northern Ireland Functional Brain Mapping Facility Project through Invest NI and the Ulster University under Grant 1303/101154803. Publisher Copyright: © 2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 16 Peace, Justice and Strong Institutions
SDG 17 Partnerships for the Goals

Keywords

wearable EEG
post-traumatic stress disorder (PTSD)
Rwanda
brain-computer interface (BCI)
neurofeedback
neurotechnology
motor-imagery
decoding accuracy
theta-alpha ratio index

Access to Document

10.1016/j.dib.2022.108066

Datainbrief_revised_v220311Author Accepted Manuscript, 1 MBLicence: CC BY
2022_duBois-et-al_DIBFinal published version, 1.59 MBLicence: CC BY-NC-ND

Cite this

Du Bois, N., Bigirimana, A. D., Korik, A., Gaju Kéthina, L., Rutembesa, E., Mutabaruka, J., Mutesa, L., Prasad, G., Jansen, S., & Coyle, D. (2022). Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity. Data in Brief, Elsevier, 42, 1-15. Article 108066. https://doi.org/10.1016/j.dib.2022.108066

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abstract = "The datasets described here comprise electroencephalography (EEG) data and psychometric data freely available on data.mendeley.com. The EEG data is available in .mat formatted files containing the EEG signal values structured in two-dimensional (2D) matrices, with channel data and trigger information in rows , and samples in columns (having a sampling rate of 250Hz). Twenty-nine female survivors of the 1994 genocide against the Tutsi in Rwanda, underwent a psychological assessment before and after an intervention aimed at reducing Post-Traumatic Stress Disorder (PTSD) symptom severity. Three measures of trauma and four measures of wellbeing were assessed using empirically validated standardised assessments. The pre- and post- intervention psychometric data were analysed using non-parametric statistical methods and the post-intervention data were further evaluated according to diagnostic assessment rules to determine clinically relevant improvements for each group. The participants were assigned to a control group (CG, n = 9), a motor-imagery group (MI, n = 10), and a neurofeedback group (NF, n = 10). Participants in the latter two groups received Brain-Computer Interface (BCI) based training as a treatment intervention over a sixteen-day period, between the pre- and post- clinical interviews. The training involved presenting feedback visually via a videogame, based on real-time analysis of the EEG recorded data during the BCI-based treatment session. Participants were asked to regulate (NF) or intentionally modulate (MI) brain activity to affect/control the game. ",

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note = "Funding Information: We would like to thank Mrs Caritas Umurerwa, Mr Vestine Mukantwali, and Mrs Immaculee Uwayezu, for their contribution to the project. We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No. EP/T022175 and DC is grateful for the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1) and the Spatial Computing and Neurotechnology Innovation Hub, funded by The Department for the Economy, Northern Ireland. Funding Information: We are grateful for access to the Tier 2 High Performance Computing resources provided by the Northern Ireland High Performance Computing (NI-HPC) facility funded by the UK Engineering and Physical Sciences Research Council (EPSRC), Grant No. EP/T022175 and DC is grateful for the UKRI Turing AI Fellowship 2021-2025 funded by the EPSRC (grant number EP/V025724/1) and the Spatial Computing and Neurotechnology Innovation Hub, funded by The Department for the Economy, Northern Ireland. Funding Information: This research was supported by funding from the United Kingdom Research Innovation (UKRI) Global Challenges Research Fund (GCRF), under Grant 71574R. N. du Bois, A. Bigirimana, G. Prasad and D. Coyle were supported in part by the Northern Ireland Functional Brain Mapping Facility Project through Invest NI and the Ulster University under Grant 1303/101154803. Publisher Copyright: {\textcopyright} 2022",

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Du Bois, N, Bigirimana, AD, Korik, A, Gaju Kéthina, L, Rutembesa, E, Mutabaruka, J, Mutesa, L, Prasad, G, Jansen, S & Coyle, D 2022, 'Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity', Data in Brief, Elsevier, vol. 42, 108066, pp. 1-15. https://doi.org/10.1016/j.dib.2022.108066

Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity. / Du Bois, Naomi; Bigirimana, Alain Desire; Korik, Attila et al.
In: Data in Brief, Elsevier, Vol. 42, 108066, 30.06.2022, p. 1-15.

Research output: Contribution to journal › Article › peer-review

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AU - Gaju Kéthina, Lisette

AU - Rutembesa, Eugène

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AU - Mutesa, Leon

AU - Prasad, Girijesh

AU - Jansen, Stefan

AU - Coyle, Damien

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KW - motor-imagery

KW - decoding accuracy

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Du Bois N, Bigirimana AD, Korik A, Gaju Kéthina L, Rutembesa E, Mutabaruka J et al. Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity. Data in Brief, Elsevier. 2022 Jun 30;42:1-15. 108066. Epub 2022 Mar 26. doi: 10.1016/j.dib.2022.108066

Electroencephalography and psychological assessment datasets to determine the efficacy of a low-cost, wearable neurotechnology intervention for reducing Post-Traumatic Stress Disorder symptom severity

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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