NeuroPlace: Categorizing urban places according to mental states.
Urban spaces have a great impact on how people's emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0183890&type=printable |
| _version_ | 1826579025573707776 |
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| author | Lulwah Al-Barrak Eiman Kanjo Eman M G Younis |
| author_facet | Lulwah Al-Barrak Eiman Kanjo Eman M G Younis |
| author_sort | Lulwah Al-Barrak |
| collection | DOAJ |
| description | Urban spaces have a great impact on how people's emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture. |
| first_indexed | 2024-12-21T18:32:52Z |
| format | Article |
| id | doaj.art-ab834d1cbfda44a19f96accfc9594a38 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2025-03-14T14:11:43Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-ab834d1cbfda44a19f96accfc9594a382025-02-27T05:36:13ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01129e018389010.1371/journal.pone.0183890NeuroPlace: Categorizing urban places according to mental states.Lulwah Al-BarrakEiman KanjoEman M G YounisUrban spaces have a great impact on how people's emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical, robust, and reliable as part of our on-going research. In addition, we present some enabling applications using the proposed architecture.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0183890&type=printable |
| spellingShingle | Lulwah Al-Barrak Eiman Kanjo Eman M G Younis NeuroPlace: Categorizing urban places according to mental states. PLoS ONE |
| title | NeuroPlace: Categorizing urban places according to mental states. |
| title_full | NeuroPlace: Categorizing urban places according to mental states. |
| title_fullStr | NeuroPlace: Categorizing urban places according to mental states. |
| title_full_unstemmed | NeuroPlace: Categorizing urban places according to mental states. |
| title_short | NeuroPlace: Categorizing urban places according to mental states. |
| title_sort | neuroplace categorizing urban places according to mental states |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0183890&type=printable |
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