The effect of noise content and level on cognitive performance measured by electroencephalography (EEG)

Ke, Jinjing; Du, Jing; Luo, Xiaowei

doi:10.1016/j.autcon.2021.103836

Cited by 56 publications

(39 citation statements)

References 62 publications

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“…It allows us to study brain activity everywhere: We could record EEG next to a construction site, while driving through the city, or in the classroom. These contexts are already of interest to researchers (e.g., Ko et al, 2017;Getzmann et al, 2018;Ke et al, 2021). In these everyday contexts, we can also investigate individual differences in sound processing: Why do some individuals notice some sounds that other people miss?…”

Section: Discussionmentioning

confidence: 99%

Real-Time Audio Processing of Real-Life Soundscapes for EEG Analysis: ERPs Based on Natural Sound Onsets

Hölle

Blum

Kissner

et al. 2022

Front. Neuroergonomics

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With smartphone-based mobile electroencephalography (EEG), we can investigate sound perception beyond the lab. To understand sound perception in the real world, we need to relate naturally occurring sounds to EEG data. For this, EEG and audio information need to be synchronized precisely, only then it is possible to capture fast and transient evoked neural responses and relate them to individual sounds. We have developed Android applications (AFEx and Record-a) that allow for the concurrent acquisition of EEG data and audio features, i.e., sound onsets, average signal power (RMS), and power spectral density (PSD) on smartphone. In this paper, we evaluate these apps by computing event-related potentials (ERPs) evoked by everyday sounds. One participant listened to piano notes (played live by a pianist) and to a home-office soundscape. Timing tests showed a stable lag and a small jitter (< 3 ms) indicating a high temporal precision of the system. We calculated ERPs to sound onsets and observed the typical P1-N1-P2 complex of auditory processing. Furthermore, we show how to relate information on loudness (RMS) and spectra (PSD) to brain activity. In future studies, we can use this system to study sound processing in everyday life.

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Section: Discussionmentioning

confidence: 99%

Real-Time Audio Processing of Real-Life Soundscapes for EEG Analysis: ERPs Based on Natural Sound Onsets

Hölle

Blum

Kissner

et al. 2022

Front. Neuroergonomics

View full text Add to dashboard Cite

show abstract

“…It allows us to study brain activity everywhere: We could record EEG next to a construction site, while driving through the city, or in the classroom. These contexts are already of interest to researchers (e.g., Getzmann, Arnau, Karthaus, Reiser, & Wascher, 2018;Ke, Du, & Luo, 2021;Ko, Komarov, Hairston, Jung, & Lin, 2017). In these everyday contexts, we can also investigate individual differences in sound processing: Why do some individuals notice some sounds that other people miss?…”

Section: Discussionmentioning

confidence: 99%

Real-time audio processing of real-life soundscapes for EEG analysis: ERPs based on natural sound onsets

Hölle

Blum

Kissner

et al. 2021

Preprint

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With smartphone-based mobile electroencephalography (EEG), we can investigate sound perception beyond the lab. To understand sound perception in the real world, we need to relate naturally occurring sounds to EEG data. For this, EEG and audio information need to be synchronized precisely, only then it is possible to capture fast and transient evoked neural responses and relate them to individual sounds. We have developed Android applications (AFEx and Record-a) that allow for the concurrent acquisition of EEG data and audio features, i.e., sound onsets, average signal power (RMS) and power spectral density (PSD) on smartphone. In this paper, we evaluate these apps by computing event-related potentials (ERPs) evoked by everyday sounds. One participant listened to piano notes (played live by a pianist) and to a home-office soundscape. Timing tests showed that the temporal precision of the system is very good. We calculated ERPs to sound onsets and observed the typical P1-N1-P2 complex of auditory processing. Furthermore, we show how to relate information on loudness (RMS) and spectra (PSD) to brain activity. In future studies, we can use this system to study sound processing in everyday life.

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“…Moreover, high noise level has a significant and negative effect on quality of patient care, nurses’ mental workload and anxiety levels [ 11 , 12 ]. Cognitive functions such as memory, attention, concentration and problem solving are also influenced by exposure to noises with various characteristics such as frequency, content, and intensity [ 13 , 14 ]. These adverse health effects induced by chronic exposure to noise levels above recommended guidelines have a synergistic effect, disturbing nurses’ performance and quality of patient care.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care

et al. 2022

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Background Hospital noise can adversely impact nurses’ health, their cognitive function and emotion and in turn, influence the quality of patient care and patient safety. Thus, the aim of this study was to predict the contributing roles of exposure to hospital noise, staff noise-sensitivity and annoyance, on the quality of patient care. Methods This descriptive and cross-sectional study was carried out among nurses in an Iranian hospital. To determine nurses’ noise exposure level, the noise was measured in 1510 locations across the hospital in accordance with ISO 9612 standards using KIMO DB 300/2 sound level meter and analyzer. An online survey was used to collect nurses’ individual data. Study questionnaires included demographics, Weinstein noise sensitivity scale, noise annoyance scale, and quality of patient care scale. Finally, to analyze the data, Bayesian Networks (BNs), as probabilistic and graphical models, were used. Results For the high noise exposure state, high noise sensitivity, and high annoyance, with the probability of 100%, the probability of delivering a desirable quality of patient care decreased by 21, 14, and 23%, respectively. Moreover, at the concurrently high noise exposure and high noise sensitivity with the probability of 100%, the desirable quality of patient care decreased by 26%. The Bayesian most influence value was related to the association of noise exposure and annoyance (0.636). Moreover, annoyance had the highest association with the physical aspect of quality of care (0.400) and sensitivity had the greatest association with the communication aspect (0.283). Conclusion Annoyance induced from environmental noise and personal sensitivity affected the quality of patient care adversely. Moreover, noise and sensitivity had a separate direct adverse effect upon the quality of patient care, and their co-occurrence reduced the potential for delivering quality patient care.

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The effect of noise content and level on cognitive performance measured by electroencephalography (EEG)

Cited by 56 publications

References 62 publications

Real-Time Audio Processing of Real-Life Soundscapes for EEG Analysis: ERPs Based on Natural Sound Onsets

Real-Time Audio Processing of Real-Life Soundscapes for EEG Analysis: ERPs Based on Natural Sound Onsets

Real-time audio processing of real-life soundscapes for EEG analysis: ERPs based on natural sound onsets

A Bayesian network model to predict the role of hospital noise, annoyance, and sensitivity in quality of patient care

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