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Qiao R, Zhang H, Tian Y. EEG cortical network reveals the temporo-spatial mechanism of visual search. Brain Res Bull 2023; 203:110758. [PMID: 37704055 DOI: 10.1016/j.brainresbull.2023.110758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/06/2023] [Accepted: 09/11/2023] [Indexed: 09/15/2023]
Abstract
This study aims to explore a method based on brain networks for implicit attention by using wavelet coherence as feature to identify individual targets in the visual field, find the optimal classification rhythm and time window, and investigate the relationship between the optimal rhythm and N2pc event-related potential. The study uses a weighted minimum norm estimate to locate the sources of the scalp EEG and reconstructs the source time series. The functional connectivity between brain areas during the visual search process is evaluated using wavelet coherence analysis, and a lateral difference network is constructed based on the difference in coherence values between the left and right visual fields. A support vector machine classifier is trained based on the wavelet coherence network features to identify the target in the left or right visual field. We also extract N2pc from the source activity data of the parieto-occipital brain region and record the time period in which N2pc occurred. The study finds that the best classification performance is achieved in the theta rhythm from 200 to 400 ms and achieved an average classification accuracy of 87% (chance level: 51.07%) in a serial search task. And this time window corresponds to the time period when N2pc appeared. The results show that the use of wavelet coherence analysis to evaluate the functional connectivity between brain areas during the visual search process provides a new approach for analyzing brain activity. The study's findings regarding the relationship between the N2pc and theta rhythm and the effectiveness of using wavelet coherence network features based on the theta rhythm for visual search classification contribute to the understanding of the neural mechanisms underlying visual search.
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Affiliation(s)
- Rui Qiao
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Haiyong Zhang
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Yin Tian
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Institute for Advanced Sciences,Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing 400064, China.
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Jiang Y, Qiao R, Shi Y, Tang Y, Hou Z, Tian Y. The effects of attention in auditory-visual integration revealed by time-varying networks. Front Neurosci 2023; 17:1235480. [PMID: 37600005 PMCID: PMC10434229 DOI: 10.3389/fnins.2023.1235480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Attention and audiovisual integration are crucial subjects in the field of brain information processing. A large number of previous studies have sought to determine the relationship between them through specific experiments, but failed to reach a unified conclusion. The reported studies explored the relationship through the frameworks of early, late, and parallel integration, though network analysis has been employed sparingly. In this study, we employed time-varying network analysis, which offers a comprehensive and dynamic insight into cognitive processing, to explore the relationship between attention and auditory-visual integration. The combination of high spatial resolution functional magnetic resonance imaging (fMRI) and high temporal resolution electroencephalography (EEG) was used. Firstly, a generalized linear model (GLM) was employed to find the task-related fMRI activations, which was selected as regions of interesting (ROIs) for nodes of time-varying network. Then the electrical activity of the auditory-visual cortex was estimated via the normalized minimum norm estimation (MNE) source localization method. Finally, the time-varying network was constructed using the adaptive directed transfer function (ADTF) technology. Notably, Task-related fMRI activations were mainly observed in the bilateral temporoparietal junction (TPJ), superior temporal gyrus (STG), primary visual and auditory areas. And the time-varying network analysis revealed that V1/A1↔STG occurred before TPJ↔STG. Therefore, the results supported the theory that auditory-visual integration occurred before attention, aligning with the early integration framework.
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Affiliation(s)
- Yuhao Jiang
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, China
| | - Rui Qiao
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
| | - Yupan Shi
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
| | - Yi Tang
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
| | - Zhengjun Hou
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
| | - Yin Tian
- Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing, China
- Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing, China
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Li P, Li C, Bore JC, Si Y, Li F, Cao Z, Zhang Y, Wang G, Zhang Z, Yao D, Xu P. L1-norm based time-varying brain neural network and its application to dynamic analysis for motor imagery. J Neural Eng 2022; 19. [PMID: 35234668 DOI: 10.1088/1741-2552/ac59a4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 03/01/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE EEG-based motor imagery (MI) brain-computer interface offers a promising way to improve the efficiency of motor rehabilitation and motor skill learning. In recent years, the power of dynamic network analysis for MI classification has been proved. In fact, its usability mainly depends on the accurate estimation of brain connection. However, traditional dynamic network estimation strategies such as adaptive directed transfer function (ADTF) are designed in the L2-norm. Usually, they estimate a series of pseudo connections caused by outliers, which results in biased features and further limits its online application. Thus, how to accurately infer dynamic causal relationship under outlier influence is urgent. APPROACH In this work, we proposed a novel ADTF, which solves the dynamic system in the L1-norm space (L1-ADTF), so as to restrict the outlier influence. To enhance its convergence, we designed an iteration strategy with the alternating direction method of multipliers (ADMM), which could be used for the solution of the dynamic state-space model restricted in the L1-norm space. Furthermore, we compared L1-ADTF to traditional ADTF and its dual extension across both simulation and real EEG experiments. MAIN RESULTS A quantitative comparison between L1-ADTF and other ADTFs in simulation studies demonstrates that fewer bias errors and more desirable dynamic state transformation patterns can be captured by the L1-ADTF. Application to real MI EEG datasets seriously noised by ocular artifacts also reveals the efficiency of the proposed L1-ADTF approach to extract the time-varying brain neural network patterns, even when more complex noises are involved. SIGNIFICANCE The L1-ADTF may not only be capable of tracking time-varying brain network state drifts robustly but may also be useful in solving a wide range of dynamic systems such as trajectory tracking problems and dynamic neural networks.
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Affiliation(s)
- Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, NO.2,Chongwen Road,Nan'an District, Chongqing, China, Chongqing, 400065, CHINA
| | - Cunbo Li
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 611731, CHINA
| | - Joyce Chelangat Bore
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 611731, CHINA
| | - Yajing Si
- Department of Psychology, Xinxiang Medical University, No. 601, Jinsui Avenue, Hongqi District, Xinxiang, Henan, 453003, CHINA
| | - Fali Li
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 610054, CHINA
| | - Zehong Cao
- University of South Australia, Adelaide, SA 5095, Australia, Adelaide, South Australia, 5001, AUSTRALIA
| | - Yangsong Zhang
- Southwest University of Science and Technology, 59 Qinglong Road, Mianyang,Sichuan, P.R.China, Mianyang, 621010, CHINA
| | - Gang Wang
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 610054, CHINA
| | - Zhijun Zhang
- South China University of Technology, 777 Xingye Avenue East, Panyu District, Guangzhou, Guangzhou, 510640, CHINA
| | - Dezhong Yao
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 611731, CHINA
| | - Peng Xu
- University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, China, Chengdu, 611731, CHINA
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Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers. SENSORS 2020; 20:s20205881. [PMID: 33080866 PMCID: PMC7589097 DOI: 10.3390/s20205881] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/09/2020] [Accepted: 10/13/2020] [Indexed: 11/17/2022]
Abstract
In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.
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Velasquez-Martinez L, Caicedo-Acosta J, Castellanos-Dominguez G. Entropy-Based Estimation of Event-Related De/Synchronization in Motor Imagery Using Vector-Quantized Patterns. ENTROPY 2020; 22:e22060703. [PMID: 33286475 PMCID: PMC7517241 DOI: 10.3390/e22060703] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 05/23/2020] [Accepted: 05/26/2020] [Indexed: 02/07/2023]
Abstract
Assessment of brain dynamics elicited by motor imagery (MI) tasks contributes to clinical and learning applications. In this regard, Event-Related Desynchronization/Synchronization (ERD/S) is computed from Electroencephalographic signals, which show considerable variations in complexity. We present an Entropy-based method, termed VQEnt, for estimation of ERD/S using quantized stochastic patterns as a symbolic space, aiming to improve their discriminability and physiological interpretability. The proposed method builds the probabilistic priors by assessing the Gaussian similarity between the input measured data and their reduced vector-quantized representation. The validating results of a bi-class imagine task database (left and right hand) prove that VQEnt holds symbols that encode several neighboring samples, providing similar or even better accuracy than the other baseline sample-based algorithms of Entropy estimation. Besides, the performed ERD/S time-series are close enough to the trajectories extracted by the variational percentage of EEG signal power and fulfill the physiological MI paradigm. In BCI literate individuals, the VQEnt estimator presents the most accurate outcomes at a lower amount of electrodes placed in the sensorimotor cortex so that reduced channel set directly involved with the MI paradigm is enough to discriminate between tasks, providing an accuracy similar to the performed by the whole electrode set.
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Tian Y, Ma L. Auditory attention tracking states in a cocktail party environment can be decoded by deep convolutional neural networks. J Neural Eng 2020; 17:036013. [DOI: 10.1088/1741-2552/ab92b2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Tian Y, Ma L, Xu W, Chen S. The Influence of Listening to Music on Adults with Left-behind Experience Revealed by EEG-based Connectivity. Sci Rep 2020; 10:7575. [PMID: 32372046 PMCID: PMC7200695 DOI: 10.1038/s41598-020-64381-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 04/16/2020] [Indexed: 11/12/2022] Open
Abstract
The human brain has a close relationship with music. Music-induced structural and functional brain changes have been demonstrated in the healthy adult. In the present study, adults with left-behind experience (ALB) were divided into two groups. The experimental group (ALB-E) took part in the music therapy experiment with three stages, including before listening to music (pre-stage), initially listening to music (mid-stage) and after listening to music (post-stage). The control group (ALB-C) did not participate in music therapy. Scalp resting-state EEGs of ALB were recorded during the three stages. We found no significant frequency change in the ALB-C group. In the ALB-E group, only the theta power spectrum was significantly different at all stages. The topographical distributions of the theta power spectrum represented change in trends from the frontal regions to the occipital regions. The result of Granger causal analysis (GCA), based on theta frequency, showed a stronger information flow from the middle frontal gyrus to the middle temporal gyrus (MFG → MTG) in the left hemisphere at the pre-stage compared to the post-stage. Additionally, the experimental group showed a weaker information flow from inferior gyrus to superior temporal gyrus (IFG → STG) in the right hemisphere at post-test stage compared to the ALB-C group. Our results demonstrate that listening to music can play a positive role on improving negative feelings for individuals with left behind experience.
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Affiliation(s)
- Yin Tian
- Bio-information College, ChongQing University of Posts and Telecommunications, ChongQing, 400065, China.
| | - Liang Ma
- Bio-information College, ChongQing University of Posts and Telecommunications, ChongQing, 400065, China
| | - Wei Xu
- Bio-information College, ChongQing University of Posts and Telecommunications, ChongQing, 400065, China
| | - Sifan Chen
- Sichuan Heguang Clinical Psychology Institute, ChengDu, 610074, China
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Bosker HR, Sjerps MJ, Reinisch E. Temporal contrast effects in human speech perception are immune to selective attention. Sci Rep 2020; 10:5607. [PMID: 32221376 PMCID: PMC7101381 DOI: 10.1038/s41598-020-62613-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Accepted: 03/16/2020] [Indexed: 11/09/2022] Open
Abstract
Two fundamental properties of perception are selective attention and perceptual contrast, but how these two processes interact remains unknown. Does an attended stimulus history exert a larger contrastive influence on the perception of a following target than unattended stimuli? Dutch listeners categorized target sounds with a reduced prefix "ge-" marking tense (e.g., ambiguous between gegaan-gaan "gone-go"). In 'single talker' Experiments 1-2, participants perceived the reduced syllable (reporting gegaan) when the target was heard after a fast sentence, but not after a slow sentence (reporting gaan). In 'selective attention' Experiments 3-5, participants listened to two simultaneous sentences from two different talkers, followed by the same target sounds, with instructions to attend only one of the two talkers. Critically, the speech rates of attended and unattended talkers were found to equally influence target perception - even when participants could watch the attended talker speak. In fact, participants' target perception in 'selective attention' Experiments 3-5 did not differ from participants who were explicitly instructed to divide their attention equally across the two talkers (Experiment 6). This suggests that contrast effects of speech rate are immune to selective attention, largely operating prior to attentional stream segregation in the auditory processing hierarchy.
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Affiliation(s)
- Hans Rutger Bosker
- Max Planck Institute for Psycholinguistics, PO Box 310, 6500 AH, Nijmegen, The Netherlands.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
| | - Matthias J Sjerps
- Max Planck Institute for Psycholinguistics, PO Box 310, 6500 AH, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Eva Reinisch
- Institute of Phonetics and Speech Processing, Ludwig Maximilian University Munich, Munich, Germany
- Acoustics Research Institute, Austrian Academy of Sciences, Vienna, Austria
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Tian Y, Zhang H, Jiang Y, Li P, Li Y. A Fusion Feature for Enhancing the Performance of Classification in Working Memory Load With Single-Trial Detection. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1985-1993. [DOI: 10.1109/tnsre.2019.2936997] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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