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Qiao Y, Mu J, Xie J, Hu B, Liu G. Music emotion recognition based on temporal convolutional attention network using EEG. Front Hum Neurosci 2024; 18:1324897. [PMID: 38617132 PMCID: PMC11010638 DOI: 10.3389/fnhum.2024.1324897] [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: 10/20/2023] [Accepted: 03/08/2024] [Indexed: 04/16/2024] Open
Abstract
Music is one of the primary ways to evoke human emotions. However, the feeling of music is subjective, making it difficult to determine which emotions music triggers in a given individual. In order to correctly identify emotional problems caused by different types of music, we first created an electroencephalogram (EEG) data set stimulated by four different types of music (fear, happiness, calm, and sadness). Secondly, the differential entropy features of EEG were extracted, and then the emotion recognition model CNN-SA-BiLSTM was established to extract the temporal features of EEG, and the recognition performance of the model was improved by using the global perception ability of the self-attention mechanism. The effectiveness of the model was further verified by the ablation experiment. The classification accuracy of this method in the valence and arousal dimensions is 93.45% and 96.36%, respectively. By applying our method to a publicly available EEG dataset DEAP, we evaluated the generalization and reliability of our method. In addition, we further investigate the effects of different EEG bands and multi-band combinations on music emotion recognition, and the results confirm relevant neuroscience studies. Compared with other representative music emotion recognition works, this method has better classification performance, and provides a promising framework for the future research of emotion recognition system based on brain computer interface.
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Affiliation(s)
- Yinghao Qiao
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Institute of Affective Computing and Information Processing, Southwest University, Chongqing, China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China
| | - Jiajia Mu
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Institute of Affective Computing and Information Processing, Southwest University, Chongqing, China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China
| | - Jialan Xie
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Institute of Affective Computing and Information Processing, Southwest University, Chongqing, China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China
| | - Binghui Hu
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Institute of Affective Computing and Information Processing, Southwest University, Chongqing, China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China
| | - Guangyuan Liu
- School of Electronic and Information Engineering, Southwest University, Chongqing, China
- Institute of Affective Computing and Information Processing, Southwest University, Chongqing, China
- Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, Southwest University, Chongqing, China
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Nam KH, Kim P, Wood DK, Kwon S, Provenzano PP, Kim DH. Multiscale Cues Drive Collective Cell Migration. Sci Rep 2016; 6:29749. [PMID: 27460294 PMCID: PMC4962098 DOI: 10.1038/srep29749] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2016] [Accepted: 06/23/2016] [Indexed: 02/07/2023] Open
Abstract
To investigate complex biophysical relationships driving directed cell migration, we developed a biomimetic platform that allows perturbation of microscale geometric constraints with concomitant nanoscale contact guidance architectures. This permits us to elucidate the influence, and parse out the relative contribution, of multiscale features, and define how these physical inputs are jointly processed with oncogenic signaling. We demonstrate that collective cell migration is profoundly enhanced by the addition of contract guidance cues when not otherwise constrained. However, while nanoscale cues promoted migration in all cases, microscale directed migration cues are dominant as the geometric constraint narrows, a behavior that is well explained by stochastic diffusion anisotropy modeling. Further, oncogene activation (i.e. mutant PIK3CA) resulted in profoundly increased migration where extracellular multiscale directed migration cues and intrinsic signaling synergistically conspire to greatly outperform normal cells or any extracellular guidance cues in isolation.
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Affiliation(s)
- Ki-Hwan Nam
- Department of Bioengineering, Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98195, USA
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 151-742, Korea
- Division of Scientific Instrumentation, Optical Instrumentation Development Team, The Korea Basic Science Institute, Daejeon 34133, Korea
| | - Peter Kim
- Department of Bioengineering, Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98195, USA
| | - David K. Wood
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 151-742, Korea
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul 151-744, South Korea
- Seoul National University Hospital Biomedical Research Institute, Seoul National University hospital, Seoul 110-744, South Korea
| | - Paolo P. Provenzano
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
- Masonic Cancer Center, and Stem Cell Institute, University of Minnesota, Minneapolis, MN 55455, USA
| | - Deok-Ho Kim
- Department of Bioengineering, Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98195, USA
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Zhu G, Du L, Jin L, Offenhäusser A. Effects of Morphology Constraint on Electrophysiological Properties of Cortical Neurons. Sci Rep 2016; 6:23086. [PMID: 27052791 PMCID: PMC4823731 DOI: 10.1038/srep23086] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 02/26/2016] [Indexed: 11/08/2022] Open
Abstract
There is growing interest in engineering nerve cells in vitro to control architecture and connectivity of cultured neuronal networks or to build neuronal networks with predictable computational function. Pattern technologies, such as micro-contact printing, have been developed to design ordered neuronal networks. However, electrophysiological characteristics of the single patterned neuron haven't been reported. Here, micro-contact printing, using polyolefine polymer (POP) stamps with high resolution, was employed to grow cortical neurons in a designed structure. The results demonstrated that the morphology of patterned neurons was well constrained, and the number of dendrites was decreased to be about 2. Our electrophysiological results showed that alterations of dendritic morphology affected firing patterns of neurons and neural excitability. When stimulated by current, though both patterned and un-patterned neurons presented regular spiking, the dynamics and strength of the response were different. The un-patterned neurons exhibited a monotonically increasing firing frequency in response to injected current, while the patterned neurons first exhibited frequency increase and then a slow decrease. Our findings indicate that the decrease in dendritic complexity of cortical neurons will influence their electrophysiological characteristics and alter their information processing activity, which could be considered when designing neuronal circuitries.
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Affiliation(s)
- Geng Zhu
- Institute of Complex Systems, Bioelectronics (PGI-8/ICS-8), Forschungszentrum Jülich, JARA – FIT, Jülich D-52425, Germany
- Bio-X Institutes, Key Laboratory for the Genetics of Development and Neuropsychiatric Disorders (Ministry of Education), and Shanghai Key Laboratory of Psychotic Disorders, Brain Science and Technology Research Center, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Liping Du
- Institute of Complex Systems, Bioelectronics (PGI-8/ICS-8), Forschungszentrum Jülich, JARA – FIT, Jülich D-52425, Germany
- Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
| | - Lei Jin
- Institute of Complex Systems, Bioelectronics (PGI-8/ICS-8), Forschungszentrum Jülich, JARA – FIT, Jülich D-52425, Germany
| | - Andreas Offenhäusser
- Institute of Complex Systems, Bioelectronics (PGI-8/ICS-8), Forschungszentrum Jülich, JARA – FIT, Jülich D-52425, Germany
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Poli D, Pastore VP, Martinoia S, Massobrio P. From functional to structural connectivity using partial correlation in neuronal assemblies. J Neural Eng 2016; 13:026023. [PMID: 26912115 DOI: 10.1088/1741-2560/13/2/026023] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Our goal is to re-introduce an optimized version of the partial correlation to infer structural connections from functional-effective ones in dissociated neuronal cultures coupled to microelectrode arrays. APPROACH We first validate our partialization procedure on in silico networks, mimicking different experimental conditions (i.e., different connectivity degrees and number of nodes) and comparing the partial correlation's performance with two gold-standard methods: cross-correlation and transfer entropy. Afterwards, to infer the structural connections in in vitro neuronal networks where the ground truth is unknown, we propose a thresholding heuristic approach. Then, to validate whether the partialization process correctly reconstructs macroscopic features of the network structure, we extract a modularity index from segregated in silico and in vitro models. Finally, as a case study, we apply our partialization procedure to analyze connectivity and topology on spontaneous developing and electrically stimulated in vitro cultures. MAIN RESULTS In simulated networks, partial correlation outperforms cross-correlation and transfer entropy at low and medium connectivity degrees, not only in relatively small (60 nodes) but also in larger (120-240 nodes) assemblies. Furthermore, partial correlation correctly identifies interconnected neuronal sub-populations and allows one to derive network topology in in vitro cortical networks. SIGNIFICANCE Our results support the idea that partial correlation is a good method for connectivity studies and can be applied to derive topological and structural features of neuronal assemblies.
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Affiliation(s)
- Daniele Poli
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genova, Genova, Italy
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Massobrio P, Pasquale V, Martinoia S. Emergence of critical dynamics in large-scale in vitro cortical networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:4737-40. [PMID: 26737352 DOI: 10.1109/embc.2015.7319452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
In vitro neuronal networks coupled to Micro-Electrode Arrays (MEAs) represent a valid experimental framework to study neuronal dynamics. This preparation is free of chemical or physical constraints and allows neurons to self-organize during development, creating networks that exhibit complex spatio-temporal patterns of activity. Starting from this experimental evidence, here we address the question whether a particular network architecture can drive the network dynamics towards a sub-, super-, or critical state.
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Poli D, Pastore VP, Massobrio P. Functional connectivity in in vitro neuronal assemblies. Front Neural Circuits 2015; 9:57. [PMID: 26500505 PMCID: PMC4595785 DOI: 10.3389/fncir.2015.00057] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 09/22/2015] [Indexed: 01/21/2023] Open
Abstract
Complex network topologies represent the necessary substrate to support complex brain functions. In this work, we reviewed in vitro neuronal networks coupled to Micro-Electrode Arrays (MEAs) as biological substrate. Networks of dissociated neurons developing in vitro and coupled to MEAs, represent a valid experimental model for studying the mechanisms governing the formation, organization and conservation of neuronal cell assemblies. In this review, we present some examples of the use of statistical Cluster Coefficients and Small World indices to infer topological rules underlying the dynamics exhibited by homogeneous and engineered neuronal networks.
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Affiliation(s)
- Daniele Poli
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova Genova, Italy
| | - Vito P Pastore
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova Genova, Italy
| | - Paolo Massobrio
- Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova Genova, Italy
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Pirino V, Riccomagno E, Martinoia S, Massobrio P. A topological study of repetitive co-activation networks in in vitro cortical assemblies. Phys Biol 2015; 12:016007. [PMID: 25559130 DOI: 10.1088/1478-3975/12/1/016007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
To address the issue of extracting useful information from large data-set of large scale networks of neurons, we propose an algorithm that involves both algebraic-statistical and topological tools. We investigate the electrical behavior of in vitro cortical assemblies both during spontaneous and stimulus-evoked activity coupled to Micro-Electrode Arrays (MEAs). Our goal is to identify core sub-networks of repetitive and synchronous patterns of activity and to characterize them. The analysis is performed at different resolution levels using a clustering algorithm that reduces the network dimensionality. To better visualize the results, we provide a graphical representation of the detected sub-networks and characterize them with a topological invariant, i.e. the sequence of Betti numbers computed on the associated simplicial complexes. The results show that the extracted sub-populations of neurons have a more heterogeneous firing rate with respect to the entire network. Furthermore, the comparison of spontaneous and stimulus-evoked behavior reveals similarities in the identified clusters of neurons, indicating that in both conditions similar activation patterns drive the global network activity.
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Affiliation(s)
- Virginia Pirino
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS),University of Genova, Genova, Italy
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NeuVision: A novel simulation environment to model spontaneous and stimulus-evoked activity of large-scale neuronal networks. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2013.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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