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Relevant bibliographies by topics / Audio-EEG analysis

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Journal articles

Academic literature on the topic 'Audio-EEG analysis'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Audio-EEG analysis"

1

Reddy Katthi, Jaswanth, and Sriram Ganapathy. "Deep Correlation Analysis for Audio-EEG Decoding." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 2742–53. http://dx.doi.org/10.1109/tnsre.2021.3129790.

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Geng, Bingrui, Ke Liu, and Yiping Duan. "Human Perception Intelligent Analysis Based on EEG Signals." Electronics 11, no. 22 (2022): 3774. http://dx.doi.org/10.3390/electronics11223774.

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The research on brain cognition provides theoretical support for intelligence and cognition in computational intelligence, and it is further applied in various fields of scientific and technological innovation, production and life. Use of the 5G network and intelligent terminals has also brought diversified experiences to users. This paper studies human perception and cognition in the quality of experience (QoE) through audio noise. It proposes a novel method to study the relationship between human perception and audio noise intensity using electroencephalogram (EEG) signals. This kind of phys

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3

Dasenbrock, Steffen, Sarah Blum, Stefan Debener, Volker Hohmann, and Hendrik Kayser. "A Step towards Neuro-Steered Hearing Aids: Integrated Portable Setup for Time- Synchronized Acoustic Stimuli Presentation and EEG Recording." Current Directions in Biomedical Engineering 7, no. 2 (2021): 855–58. http://dx.doi.org/10.1515/cdbme-2021-2218.

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Abstract Aiming to provide a portable research platform to develop algorithms for neuro-steered hearing aids, a joint hearing aid - EEG measurement setup was implemented in this work. The setup combines the miniaturized electroencephalography sensor technology cEEGrid with a portable hearing aid research platform - the Portable Hearing Laboratory. The different components of the system are connected wirelessly, using the lab streaming layer framework for synchronization of audio and EEG data streams. Our setup was shown to be suitable for simultaneous recording of audio and EEG signals used in

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Lee, Yi Yeh, Aaron Raymond See, Shih Chung Chen, and Chih Kuo Liang. "Effect of Music Listening on Frontal EEG Asymmetry." Applied Mechanics and Materials 311 (February 2013): 502–6. http://dx.doi.org/10.4028/www.scientific.net/amm.311.502.

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Frontal EEG asymmetry has been recognized as a useful method in determining emotional states and psychophysiological conditions. For the current research, resting prefrontal EEG was measured before, during and after listening to sad music video. Data were recorded and analyzed using a wireless EEG module with digital results sent via Bluetooth to a remote computer for further analysis. The relative alpha power was utilized to determine EEG asymmetry indexes. The results indicated that even if a person had a stronger right hemisphere in the initial phase a significant shift first occurred durin

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Hadjidimitriou, Stelios K., Asteris I. Zacharakis, Panagiotis C. Doulgeris, Konstantinos J. Panoulas, Leontios J. Hadjileontiadis, and Stavros M. Panas. "Revealing Action Representation Processes in Audio Perception Using Fractal EEG Analysis." IEEE Transactions on Biomedical Engineering 58, no. 4 (2011): 1120–29. http://dx.doi.org/10.1109/tbme.2010.2047016.

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6

Reshetnykov, Denys S. "EEG Analysis of Person Familiarity with Audio-Video Data Assessing Task." Upravlâûŝie sistemy i mašiny, no. 4 (276) (August 2018): 70–83. http://dx.doi.org/10.15407/usim.2018.04.070.

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7

Kumar, Himanshu, Subha D. Puthankattil, and Ramakrishnan Swaminathan. "ANALYSIS OF EEG RESPONSE FOR AUDIO-VISUAL STIMULI IN FRONTAL ELECTRODES AT THETA FREQUENCY BAND USING THE TOPOLOGICAL FEATURES." Biomedical Sciences Instrumentation 57, no. 2 (2021): 333–39. http://dx.doi.org/10.34107/yhpn9422.04333.

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Emotions are the fundamental intellectual capacity of humans characterized by perception, attention, and behavior. Emotions are characterized by psychophysiological expressions. Studies have been performed by analyzing Electroencephalogram (EEG) responses from various lobes of the brain under all frequency bands. In this work, the EEG response of the theta band in the frontal lobe is analyzed extracting topological features during audio-visual stimulation. This study is carried out using the EEG signals from the public domain database. In this method, the signals are projected in higher dimens

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8

Ribeiro, Estela, and Carlos Eduardo Thomaz. "A Whole Brain EEG Analysis of Musicianship." Music Perception 37, no. 1 (2019): 42–56. http://dx.doi.org/10.1525/mp.2019.37.1.42.

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The neural activation patterns provoked in response to music listening can reveal whether a subject did or did not receive music training. In the current exploratory study, we have approached this two-group (musicians and nonmusicians) classification problem through a computational framework composed of the following steps: Acoustic features extraction; Acoustic features selection; Trigger selection; EEG signal processing; and Multivariate statistical analysis. We are particularly interested in analyzing the brain data on a global level, considering its activity registered in electroencephalog

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9

Gao, Chenguang, Hirotaka Uchitomi, and Yoshihiro Miyake. "Influence of Multimodal Emotional Stimulations on Brain Activity: An Electroencephalographic Study." Sensors 23, no. 10 (2023): 4801. http://dx.doi.org/10.3390/s23104801.

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This study aimed to reveal the influence of emotional valence and sensory modality on neural activity in response to multimodal emotional stimuli using scalp EEG. In this study, 20 healthy participants completed the emotional multimodal stimulation experiment for three stimulus modalities (audio, visual, and audio-visual), all of which are from the same video source with two emotional components (pleasure or unpleasure), and EEG data were collected using six experimental conditions and one resting state. We analyzed power spectral density (PSD) and event-related potential (ERP) components in r

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10

Lee, Tae-Ju, Seung-Min Park, and Kwee-Bo Sim. "Electroencephalography Signal Grouping and Feature Classification Using Harmony Search for BCI." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/754539.

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This paper presents a heuristic method for electroencephalography (EEG) grouping and feature classification using harmony search (HS) for improving the accuracy of the brain-computer interface (BCI) system. EEG, a noninvasive BCI method, uses many electrodes on the scalp, and a large number of electrodes make the resulting analysis difficult. In addition, traditional EEG analysis cannot handle multiple stimuli. On the other hand, the classification method using the EEG signal has a low accuracy. To solve these problems, we use a heuristic approach to reduce the complexities in multichannel pro

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