Generalized EEG Data Acquisition and Processing System

Le, Vinh D.; Carthen, Chase D.; Kamaruddin, Norhaslinda; Tavakkoli, Alireza; Dascalu, Sergiu M.; Harris, Frederick C.

doi:10.1007/978-3-031-28332-1_20

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Firstly, although this study has provided valuable insights into the association between crowding and users' emotions, caution should be applied in generalizing the findings due to potential variations in crowding perceptions resulting from environmental factors across different seasons. Further research is required to explore and compare the effects of users' perceptions of crowding in diverse environmental contexts, which would necessitate the design of specialized EEG equipment capable of capturing data in these specific settings [89]. Secondly, the EEG equipment utilized in this study was primarily suited for controlled indoor environments characterized by tranquility and smoothness.…”

Section: Limitations Of the Study And Future Perspectivesmentioning

confidence: 99%

Examining the Impact of Crowding Perception on the Generation of Negative Emotions among Users of Small Urban Micro Public Spaces

Zhang,

Qi,

Zhang

2023

Sustainability

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The improvement of urban congestion and the mood of the populace is vital for achieving the United Nations Sustainable Development Goals. This paper aims to test the hypothesis that crowding in urban micro public spaces (UMPSs) affects emotions, and two different methods were used: a psychological questionnaire to measure whether crowding in such spaces elicits negative emotional responses from users and a portable electroencephalography (EEG) device to explore emotional responses. This study was conducted in Shenyang, China, based on the city’s relevant policies and the type and number of micro spaces and micro public spaces, proving that Shenyang City has paid more attention to planning micro spaces and micro public spaces in its urban development. The results show that 1. UMPS crowding awakens negative emotions in users, which affects their intention to revisit the UMPS, and 2. If the UMPS is more attractive to users, it also improves the negative emotions due to crowding, which implies that the attractiveness of the environment does play an important moderating role. This study may inspire the construction concept of UMPSs in different countries and cities, complementing the planning of urban public spaces to provide more social equity. The creation of UMPS has a positive effect on neighborhood interactions, community network construction, and the reproduction of social capital, which can contribute to the sustainable development of cities.

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Section: Limitations Of the Study And Future Perspectivesmentioning

confidence: 99%

Examining the Impact of Crowding Perception on the Generation of Negative Emotions among Users of Small Urban Micro Public Spaces

Zhang,

Qi,

Zhang

2023

Sustainability

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“…GEDAPS (Le et al, 2023) is the software suite used to autonomously display the emotional stimuli images presented to participants in this study. GEDAPS removes the need for a facilitator to manually record timings and metadata within an EEG user study.…”

Section: Softwarementioning

confidence: 99%

Evaluating an Elevated Signal-to-Noise Ratio in EEG Emotion Recognition

Estreito,

Le,

Harris Jr.

et al. 2023

International Journal of Software Innovation

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Predicting valence and arousal values from EEG signals has been a steadfast research topic within the field of affective computing or emotional AI. Although numerous valid techniques to predict valence and arousal values from EEG signals have been established and verified, the EEG data collection process itself is relatively undocumented. This creates an artificial learning curve for new researchers seeking to incorporate EEGs within their research workflow. In this article, a study is presented that illustrates the importance of a strict EEG data collection process for EEG affective computing studies. The work was evaluated by first validating the effectiveness of a machine learning prediction model on the DREAMER dataset, then showcasing the lack of effectiveness of the same machine learning prediction model on cursorily obtained EEG data.

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