Human-building interaction under various indoor temperatures through neural-signal electroencephalogram (EEG) methods

Shan, Xin; Yang, En-Hua; Zhou, Jin; Chang, Victor Wei-Chung

doi:10.1016/j.buildenv.2017.12.004

Cited by 88 publications

(35 citation statements)

References 39 publications

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“…Other research groups have studied longitudinal EEG data from individual subjects such as an investigation involving 103-day single channel EEG data on emotion recognition in daily life [49]. Nowadays, fundamental knowledge on emotion recognition or affective computing using brain wave activity has been applied in broad areas such as studies of people with depression [50], stress in construction workers [51], and interactions between ambient temperature in building and the occupant [52].…”

Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. Additional 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.

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Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

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“…Only in one study, they used machine learning method to build a model to predict individual thermal comfort based on EEG. 23 Therefore, in this paper, the feasibility of building a thermal comfort discriminant model is explored based on EEG signals to achieve continuous monitoring of individual thermal sensation.…”

Section: Introductionmentioning

confidence: 99%

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

2020

Indoor Air

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Thermal comfort is an important factor for the design of buildings. Although it has been well recognized that many physiological parameters are linked to the state of thermal comfort or discomfort of humans, how to use physiological signal to judge the state of thermal comfort has not been well studied. In this paper, the feasibility of continuously determining feelings of personal thermal comfort was discussed by using electroencephalogram (EEG) signals in private space. In the study, 22 subjects were exposed to thermally comfortable and uncomfortably hot environments, and their EEG signals were recorded. Spectral power features of the EEG signals were extracted, and an ensemble learning method using linear discriminant analysis or support vector machine as a sub-classifier was used to build the discriminant model. The results show that an average discriminate accuracy of 87.9% can be obtained within a detection window of 60 seconds. This study indicates that it is feasible to distinguish whether a person feels comfortable or too hot in their private space by multi-channel EEG signals without interruption and suggests possibility for further applications in neuroergonomics. K E Y W O R D S EEG-based individual thermal comfort model, electroencephalogram, linear discriminant analysis, power spectral density, support vector machine, thermal comfort How to cite this article: Wu M, Li H, Qi H. Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments. Indoor Air. 2020;30:534-543.

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“…They are characterized by different frequencies, as follows: δ by 1-4 Hz, θ by 4-8 Hz, α by 8-14 Hz, and β by 14-30 Hz. δ-band is primarily related to sleep, θ-band is related to drowsiness, α-band is often present in relaxed awareness without attention, and β-band is related to active thinking [2]. He et al [3] used EEG to assess sleep quality.…”

Section: Introductionmentioning

confidence: 99%

Changes in EEG signals during the cognitive activity at varying air temperature and relative humidity

Zhu

Liu

Wargocki

2019

J Expo Sci Environ Epidemiol

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In this study, we examined changes in EEG signals during the cognitive activity at different air temperatures and relative humidities (RH). Thirty-two healthy young people acclimatized to the subtropical climate of Changsha, China, were recruited as subjects. They experienced four air temperature levels (26, 30, 33, and 37°C) and two relative humidity levels (50 and 70%) in a climate chamber. During 175 min-long exposures to each thermal condition, they performed cognitive tasks and their EEG signals were measured. Relative humidity of 70% and increased temperature at this relative humidity significantly increased the relative power of δ-band and significantly decreased relative power of θ-band, α-band, and βband. This may suggest that subjects were more sleepy but less drowsy, and it was more difficult for them to think clearly. At the same time, subjective evaluations indicated that they could be less alert and it was harder for them to think. However, no changes in performance of tasks measuring cognitive abilities were observed. It remains therefore unclear whether EEG can be a credible marker of changes in cognitive activity as a result of changes in indoor environmental quality in buildings and the future experiments should closely examine this issue.

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Human-building interaction under various indoor temperatures through neural-signal electroencephalogram (EEG) methods

Cited by 88 publications

References 39 publications

Consumer Grade Brain Sensing for Emotion Recognition

Consumer Grade Brain Sensing for Emotion Recognition

Using electroencephalogram to continuously discriminate feelings of personal thermal comfort between uncomfortably hot and comfortable environments

Changes in EEG signals during the cognitive activity at varying air temperature and relative humidity

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