Effect of audiovisual stimulation on adult memory performance based electroencephalography wavelet analysis

Daud, Syarifah Noor Syakiylla Sayed; Sudirman, Rubita

doi:10.1016/j.bspc.2022.103659

Cited by 3 publications

(4 citation statements)

References 53 publications

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“…Wavelet analysis has good time-frequency localization characteristics and good adaptive ability, and Time-frequency analysis of signals has good results. The author uses wavelet analysis technology to predict the capacity margin of the power system, which has good application value [5][6] .…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent prediction of capacity margin in different time periods based on wavelet analysis

Sun,

Yang,

Chen

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Begin In order to understand the intelligent prediction of capacity margin in different time periods, the author proposes a research on intelligent prediction of capacity margin in different time periods based on wavelet analysis. The author first uses wavelet decomposition and neural networks as tools to predict electricity prices in different time periods. The changes in the electricity price sequence during different time periods are relatively single, which is conducive to the learning and training of neural networks, thereby improving prediction accuracy. Secondly, compare the predicted results of time slot capacity margin based on wavelet analysis technology with the actual values. Finally, the experimental results indicate that the average relative percentage error of short-term electricity price prediction can reach 11.40%. The intelligent Jun page measurement method for capacity margin based on wavelet analysis proposed by the author can effectively improve the prediction accuracy of power grid capacity margin, and has strong practicality and effectiveness.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent prediction of capacity margin in different time periods based on wavelet analysis

Sun,

Yang,

Chen

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…Table 1. Classification of brain waves and their properties [39]. The scientific literature confirms that EEG signals are influenced by sounds heard and various odors inhaled [40,41].…”

Section: Brain Rhythms Amplitude (μV) Frequency (Hz) Brain Statesmentioning

confidence: 99%

“…Table 1. Classification of brain waves and their properties [39]. annoyance and the level of consciousness were assessed.…”

Section: Introductionmentioning

confidence: 99%

Study on the Effect of Dalbergia pinnata (Lour.) Prain Essential Oil on Electroencephalography upon Stimulation with Different Auditory Effects

He,

Qin,

et al. 2024

Molecules

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Dalbergia pinnata (Lour.) Prain (D. pinnata) is a valuable medicinal plant, and its volatile parts have a pleasant aroma. In recent years, there have been a large number of studies investigating the effect of aroma on human performance. However, the effect of the aroma of D. pinnata on human psychophysiological activity has not been reported. Few reports have been made about the effects of aroma and sound on human electroencephalographic (EEG) activity. This study aimed to investigate the effects of D. pinnata essential oil in EEG activity response to various auditory stimuli. In the EEG study, 30 healthy volunteers (15 men and 15 women) participated. The electroencephalogram changes of participants during the essential oil (EO) of D. pinnata inhalation under white noise, pink noise and traffic noise stimulations were recorded. EEG data from 30 electrodes placed on the scalp were analyzed according to the international 10–20 system. The EO of D. pinnata had various effects on the brain when subjected to different auditory stimuli. In EEG studies, delta waves increased by 20% in noiseless and white noise environments, a change that may aid sleep and relaxation. In the presence of pink noise and traffic noise, alpha and delta wave activity (frontal pole and frontal lobe) increased markedly when inhaling the EO of D. pinnata, a change that may help reduce anxiety. When inhaling the EO of D. pinnata with different auditory stimuli, women are more likely to relax and get sleepy compared to men.

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“…Brainwave signals were categorized into different frequency bands. Theta waves (4-7.5 Hz) are typically associated with relaxation and meditation; alpha waves (8-11.5 Hz) are related to states of attention and relaxation; beta waves (12-24.5 Hz) are related to states of thinking and learning; and gamma waves (25-45 Hz) are associated with high focus and cognitive states [35]. The system primarily references the activity of alpha and beta waves to infer user attention, relaxation, or tension.…”

Section: E Valence-arousal Emotional Model Based On Eegmentioning

confidence: 99%

A Post-Stroke Rehabilitation System With Compensatory Movement Detection Using Virtual Reality and Electroencephalogram Technologies

Shih,

Lin,

Chen

et al. 2024

IEEE Access

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Stroke is a leading cause of global population mortality and disability, imposing burdens on patients and caregivers, and significantly affecting the quality of life of patients. Therefore, in this study, we aimed to explore the application of virtual reality technology in physical therapy by using immersive interactive training and designing rehabilitation modes for individual and group settings. We also aimed to provide patients with stroke with a comprehensive home-based rehabilitation and treatment plan, ultimately enhancing training effectiveness. Patients can engage in home-based rehabilitation through this system and undergo functional, mirror, and constraint-induced therapies tailored to different task contents. Simultaneously, using brain-computer interface technology, an emotion analysis mechanism was designed to map the patients' brainwave signal data onto a two-dimensional space of positive-negative valence arousal; this approach can enable remote physical therapists to discern the patients' emotional states during the rehabilitation process through virtual spaces, facilitating timely adjustments to rehabilitation tasks. Moreover, to prevent compromised effectiveness owing to improper training postures leading to compensation, the system offers real-time identification and recording, promptly issuing alerts when compensation occurs. The system provides a multiuser virtual rehabilitation space, enabling timely corrections and data observations, offering patients with stroke a home-based rehabilitation program, thereby realizing a localized aging care model.

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Effect of audiovisual stimulation on adult memory performance based electroencephalography wavelet analysis

Cited by 3 publications

References 53 publications

Intelligent prediction of capacity margin in different time periods based on wavelet analysis

Intelligent prediction of capacity margin in different time periods based on wavelet analysis

Study on the Effect of Dalbergia pinnata (Lour.) Prain Essential Oil on Electroencephalography upon Stimulation with Different Auditory Effects

A Post-Stroke Rehabilitation System With Compensatory Movement Detection Using Virtual Reality and Electroencephalogram Technologies

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