Relevant bibliographies by topics / Audio-EEG analysis

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  1. 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 physiological signal can be used to analyze the user’s cognitive process through transformation and feature calculation, so as to overcome the deficiency of traditional subjective evaluation. Experimental and analytical results show that the EEG signals in frequency domain can be used for feature learning and calculation to measure changes in user-perceived audio noise intensity. In the experiment, the user’s noise tolerance limit for different audio scenarios varies greatly. The noise power spectral density of soothing audio is 0.001–0.005, and the noise spectral density of urgent audio is 0.03. The intensity of information flow in the corresponding brain regions increases by more than 10%. The proposed method explores the possibility of using EEG signals and computational intelligence to measure audio perception quality. In addition, the analysis of the intensity of information flow in different brain regions invoked by different tasks can also be used to study the theoretical basis of computational intelligence.
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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 a pilot study (n=5) to perform an auditory Oddball experiment. The analysis showed that the setup can reliably capture typical event-related potential responses. Furthermore, linear discriminant analysis was successfully applied for single-trial classification of P300 responses. The study showed that time-synchronized audio and EEG data acquisition is possible with the Portable Hearing Laboratory research platform.
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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 during audio-video stimulation and was followed by a further inclination to left EEG asymmetry as measured after the stimulation. Furthermore the current research was able to use prefrontal EEG to produce results that were mostly measured at the frontal lobe. It was also able to provide significant changes in results using audio and video stimulation as to previous experiments that made use of audio stimulation. In the future, more experiments can be conducted to obtain a better understanding of a person’s appreciation or dislike toward a certain video, commercial or other multimedia contents through the aid of convenient EEG module.
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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 dimensional space to find out the geometrical properties. Features, namely the center of gravity and perimeter of the boundary space, are used to quantify the changes in the geometrical properties of the signal, and the features are subject to the Wilcoxon rank-sum test for statistical significance. Different electrodes in the frontal region under the same audio-visual stimulus showed similar variations in the geometry of the boundary in higher-dimensional space. Further, the electrodes, Fp1 and F3, showed a statistical significance of p < 0.05 in differentiating arousal states, and the Fp1 electrode showed a statistical significance in differentiating valence emotional state. Thus, the topological features extracted from the frontal electrodes in theta band could differentiate arousal and valence emotional states and be of significant clinical relevance.
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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 electroencephalogram (EEG) signals on a given time instant. Our experiment's results—with 26 volunteers (13 musicians and 13 nonmusicians) who listened the classical music Hungarian Dance No. 5 from Johannes Brahms—have shown that is possible to linearly differentiate musicians and nonmusicians with classification accuracies that range from 69.2% (test set) to 93.8% (training set), despite the limited sample sizes available. Additionally, given the whole brain vector navigation method described and implemented here, our results suggest that it is possible to highlight the most expressive and discriminant changes in the participants brain activity patterns depending on the acoustic feature extracted from the audio.
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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 response to multimodal emotional stimuli, for spectral and temporal analysis. PSD results showed that the single modality (audio only/visual only) emotional stimulation PSD differed from multi-modality (audio-visual) in a wide brain and band range due to the changes in modality and not from the changes in emotional degree. The most pronounced N200-to-P300 potential shifts occurred in monomodal rather than multimodal emotional stimulations. This study suggests that emotional saliency and sensory processing efficiency perform a significant role in shaping neural activity during multimodal emotional stimulation, with the sensory modality being more influential in PSD. These findings contribute to our understanding of the neural mechanisms involved in multimodal emotional stimulation.
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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 problems and classification. In this study, we build a group of stimuli using the HS algorithm. Then, the features from common spatial patterns are classified by the HS classifier. To confirm the proposed method, we perform experiments using 64-channel EEG equipment. The subjects are subjected to three kinds of stimuli: audio, visual, and motion. Each stimulus is applied alone or in combination with the others. The acquired signals are processed by the proposed method. The classification results in an accuracy of approximately 63%. We conclude that the heuristic approach using the HS algorithm on the BCI is beneficial for EEG signal analysis.
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