Distinct Patterns of Functional Connectivity During the Comprehension of Natural, Narrative Speech

Zhu, Yongjie; Liu, Jia; Ristaniemi, Tapani; Cong, Fengyu

doi:10.1142/s0129065720500070

Cited by 12 publications

(6 citation statements)

References 75 publications

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“…As for multi-channels EEG features, we performed principal component analysis (PCA) method to realize dimensionality reduction. PCA and related techniques have been applied to describe the fluctuation of EEG measurements during the resting state (Leonardi et al, 2013), continuous movie-watching task (Demirtaş et al, 2019), and whole-brain connectivity dynamics (Allen et al, 2014;Zhu et al, 2020). PCA is a method accepted by many researches to reduce the dimensionality of multi-channel or whole brain features, and then to study dynamic fluctuation.…”

Section: Relationship Between Eeg Dynamical Features and Real-time Response During Sustained Attention Taskmentioning

confidence: 99%

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Wan

Cui

Gao

et al. 2021

Front. Hum. Neurosci.

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Measuring and identifying the specific level of sustained attention during continuous tasks is essential in many applications, especially for avoiding the terrible consequences caused by reduced attention of people with special tasks. To this end, we recorded EEG signals from 42 subjects during the performance of a sustained attention task and obtained resting state and three levels of attentional states using the calibrated response time. EEG-based dynamical complexity features and Extreme Gradient Boosting (XGBoost) classifier were proposed as the classification model, Complexity-XGBoost, to distinguish multi-level attention states with improved accuracy. The maximum average accuracy of Complexity-XGBoost were 81.39 ± 1.47% for four attention levels, 80.42 ± 0.84% for three attention levels, and 95.36 ± 2.31% for two attention levels in 5-fold cross-validation. The proposed method is compared with other models of traditional EEG features and different classification algorithms, the results confirmed the effectiveness of the proposed method. We also found that the frontal EEG dynamical complexity measures were related to the changing process of response during sustained attention task. The proposed dynamical complexity approach could be helpful to recognize attention status during important tasks to improve safety and efficiency, and be useful for further brain-computer interaction research in clinical research or daily practice, such as the cognitive assessment or neural feedback treatment of individuals with attention deficit hyperactivity disorders, Alzheimer’s disease, and other diseases which affect the sustained attention function.

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Section: Relationship Between Eeg Dynamical Features and Real-time Response During Sustained Attention Taskmentioning

confidence: 99%

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Wan

Cui

Gao

et al. 2021

Front. Hum. Neurosci.

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“…Some examples include perceived audio quality assessment (Mehta and Kliewer, 2017 ) and semantic processing (Zhang et al, 2019 ). The effect of acoustic challenges, age-related hearing loss, and comprehension of speech on functional connectivity were also investigated in Bidelman et al ( 2018 , 2019 ), and Zhu et al ( 2020 ), respectively.…”

Section: Introductionmentioning

confidence: 99%

Speech to noise ratio improvement induces nonlinear parietal phase synchrony in hearing aid users

et al. 2022

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ObjectivesComprehension of speech in adverse listening conditions is challenging for hearing-impaired (HI) individuals. Noise reduction (NR) schemes in hearing aids (HAs) have demonstrated the capability to help HI to overcome these challenges. The objective of this study was to investigate the effect of NR processing (inactive, where the NR feature was switched off, vs. active, where the NR feature was switched on) on correlates of listening effort across two different background noise levels [+3 dB signal-to-noise ratio (SNR) and +8 dB SNR] by using a phase synchrony analysis of electroencephalogram (EEG) signals.DesignThe EEG was recorded while 22 HI participants fitted with HAs performed a continuous speech in noise (SiN) task in the presence of background noise and a competing talker. The phase synchrony within eight regions of interest (ROIs) and four conventional EEG bands was computed by using a multivariate phase synchrony measure.ResultsThe results demonstrated that the activation of NR in HAs affects the EEG phase synchrony in the parietal ROI at low SNR differently than that at high SNR. The relationship between conditions of the listening task and phase synchrony in the parietal ROI was nonlinear.ConclusionWe showed that the activation of NR schemes in HAs can non-linearly reduce correlates of listening effort as estimated by EEG-based phase synchrony. We contend that investigation of the phase synchrony within ROIs can reflect the effects of HAs in HI individuals in ecological listening conditions.

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“…Recently, the brain state under the naturalistic stimuli including music and movie has been investigated through functional magnetic resonance imaging (fMRI) (Alluri et al 2012a, b;Alluri et al 2013;Burunat et al 2014Burunat et al , 2016aLiu et al 2017;Toiviainen et al 2014), MEG (Koskinen et al 2013;Lankinen et al 2014) and EEG (Cong et al 2013a, b;Daly et al 2014Daly et al , 2015Schaefer et al 2013;Sturm et al 2015;Zhu et al 2019Zhu et al , 2020. Alluri et al explored the neural correlates of music feature processing as it occurs in a realistic or naturalistic environment, where eleven participants attentively listened to the whole piece of music (Alluri et al 2012a, b;Burunat et al 2016b, a).…”

Section: Introductionmentioning

confidence: 99%

Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening

et al. 2020

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Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro-or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during freely listening to music. We used a data-driven method that combined music information retrieval with spatial Fourier Independent Components Analysis (spatial Fourier-ICA) to probe the interplay between the spatial profiles and the spectral patterns of the brain network emerging from music listening. Correlation analysis was performed between time courses of brain networks extracted from EEG data and musical feature time series extracted from music stimuli to derive the musical feature related oscillatory patterns in the listening brain. We found brain networks of musical feature processing were frequency-dependent. Musical feature time series, especially fluctuation centroid and key feature, were associated with an increased beta activation in the bilateral superior temporal gyrus. An increased alpha oscillation in the bilateral occipital cortex emerged during music listening, which was consistent with alpha functional suppression hypothesis in task-irrelevant regions. We also observed an increased delta-beta oscillatory activity in the prefrontal cortex associated with musical feature processing. In addition to these findings, the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

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Distinct Patterns of Functional Connectivity During the Comprehension of Natural, Narrative Speech

Cited by 12 publications

References 75 publications

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Frontal EEG-Based Multi-Level Attention States Recognition Using Dynamical Complexity and Extreme Gradient Boosting

Speech to noise ratio improvement induces nonlinear parietal phase synchrony in hearing aid users

Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening

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