基于持续同调性的人脑认知过程格式塔模式识别拓扑分析

doi:10.11959/j.issn.1000-0801.2021124

Abstract

Abstract:

The integrated development of information communication technology and neuroscience heralds the possibility and great potential of brain-to-brain wireless communication(B2BC).The physiologically meaningful features of brain responses were extracted to different contour and shape in images in Gestalt cognitive tests by combining persistent homology analysis with electroencephalogram (EEG).The experimental results show that more brain regions in the frontal lobe were involved when the subject perceives the random and disordered combination of images compared to the ordered Gestalt images.Meanwhile, the persistence entropy of EEG data evoked by random sequence diagram (RSD) was observed to be significantly different from that by the ordered Gestalt images(GST) in several frequency bands, which indicated that human cognition of shapes and contours, such as a preliminary advanced cognition process, could be separated to some extent through topological analysis.This method can digitize the neural signals while preserving the whole and local features of the original signals.In general, the cognitively related neural correlates by persistent homology features of EEG signal are revaluated and quantified, which provides an approach to realize the digitization of neural signals in brain-to-brain wireless communication.

Key words: 6G, brain-to-brain communication, Gestalt, electroencephalogram, persistent entropy

CLC Number:

TP393

Zaisheng LIU, Fei NI, Rongpeng LI, Honggang ZHANG, Chang LIU, Jiefang ZHANG, Songyun XIE. Persistent homology based topological analysis on the gestalt patterns during human brain cognition process[J]. Telecommunications Science, 2021, 37(7): 77-85.

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[1]	MELGAREJO D C , MOIOLI R , NARDELLI P . Brain-to-brain communication based on wireless technologies:actual and future perspectives[EB]. 2019.
[2]	SIULY S , KHARE S K , BAJAJ V ,et al. A computerized method for automatic detection of schizophrenia using EEG signals[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020,28(11): 2390-2400.
[3]	WANG Y , OMBAO H , CHUNG M K . Topological data analysis of single-trial electroencephalographic signals[J]. The Annals of Applied Statistics, 2018,12(3): 1506-1534.
[4]	SCHOLLER S , BOSSE S , TREDER M S ,et al. Toward a direct measure of video quality perception using EEG[J]. IEEE Transactions on Image Processing, 2012,21(5): 2619-2629.
[5]	KOFFKA K . Perception:an introduction to the gestalt-theorie[J]. Psychological Bulletin, 1922,19(10): 531-585.
[6]	GEORGE D , LáZARO-GREDILLA M ,, GUNTUPALLI J S . From CAPTCHA to commonsense:how brain can teach us about artificial intelligence[J]. Frontiers in Computational Neuroscience, 2020(14): 554097.
[7]	LAVIN A , SWAROOP GUNTUPALLI J , LáZARO-GREDILLA M , ,et al. Explaining visual cortex phenomena using recursive cortical network[Z]. 2018.
[8]	SANTOS F A N , RAPOSO E P , COUTINHO-FILHO M D , ,et al. Topological phase transitions in functional brain networks[J]. Physical Review E, 2019(100): 32414.
[9]	SUBASI A , ER?ELEBI E , . Classification of EEG signals using neural network and logistic regression[J]. Computer Methods and Programs in Biomedicine, 2005,78(2): 87-99.
[10]	SRINIVASAN V , ESWARAN C , SRIRAAM N . Approximate entropy-based epileptic EEG detection using artificial neural networks[J]. IEEE Transactions on Information Technology in Biomedicine, 2007,11(3): 288-295.
[11]	FORNITO A , ZALESKY A , BULLMORE E T . Fundamentals of brain network analysis[EB]. 2016
[12]	CHEN A C N , RAPPELSBERGER P , FILZ O . Topology of EEG coherence changes may reflect differential neural network activation in cold and pain perception[J]. Brain Topography, 1998,11(2): 125-132.
[13]	PIANGERELLI M , RUCCO M , TESEI L ,et al. Topological classifier for detecting the emergence of epileptic seizures[J]. BMC Research Notes, 2018,11(1): 1-7.
[14]	BAKER N , ERLIKHMAN G , KELLMAN P J ,et al. Deep convolutional networks do not perceive illusory contours[Z]. 2018.
[15]	KIM B , REIF E , WATTENBERG M ,et al. Neural networks trained on natural scenes exhibit gestalt closure[J]. Computational Brain ＆ Behavior, 2021: 1-13.
[16]	GONZALEZ-DIAZ R , PALUZO-HIDALGO E ,, QUESADA J F . Towards emotion recognition:a persistent entropy application[C]// Proceedings of Computational Topology in Image Context.[S.l.:s.n.], 2021.
[17]	ZHANG D L , YAO L N , CHEN K X ,et al. A graph-based hierarchical attention model for movement intention detection from EEG signals[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019,27(11): 2247-2253.
[18]	ZOMORODIAN A , CARLSSON G . Computing persistent homology[J]. Discrete ＆ Computational Geometry, 2005,33(2): 249-274.
[19]	EDELSBRUNNER H , HARER J . Computational topology[M]. Rhode Island: American Mathematical Society, 2009.
[20]	HARTSHORNE R . Algebraic geometry[M]. New York: Springer Press, 1977.
[21]	PETTI M , TOPPI J , BABILONI F ,et al. EEG resting-state brain topological reorganization as a function of age[J]. Computational Intelligence and Neuroscience,2016, 2016:6243694.
[22]	RUCCO M , GONZALEZ-DIAZ R ,, JIMENEZ M J ,et al. A new topological entropy-based approach for measuring similarities among piecewise linear functions[J]. Signal Processing, 2017,134: 130-138.
[23]	KHALID A , KIM B S , CHUNG M K ,et al. Tracing the evolution of multi-scale functional networks in a mouse model of depression using persistent brain network homology[J]. NeuroImage, 2014(101): 351-363.
[24]	JOHANSSON M. The hilbert transform[Z]. 1999.
[25]	ADAMS H , CHEPUSHTANOVAS , EMERSONT ,et al. Persistence images:a stable vector representation of persistent homology[EB]. 2015.
[26]	WANG Y , OMBAO H , CHUNG M K . Statistical persistent homology of brain signals[C]// Proceedings of ICASSP 2019 2019 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP). Piscataway:IEEE Press, 2019: 1125-1129.

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计算方式	区分度范围	均值	最大值
C-matrix	65%～90%	73.33%	90%
D-matrix	55%～85%	71.67%	85%

Persistent homology based topological analysis on the gestalt patterns during human brain cognition process

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