SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation

被引:1
|
作者
Mikhaylets, Ekaterina [1 ]
Razorenova, Alexandra M. [1 ,2 ]
Chernyshev, Vsevolod [1 ]
Syrov, Nikolay [3 ]
Yakovlev, Lev [3 ]
Boytsova, Julia [4 ]
Kokurina, Elena [4 ]
Zhironkina, Yulia [5 ]
Medvedev, Svyatoslav [4 ]
Kaplan, Alexander [3 ,6 ]
机构
[1] HSE Univ, Fac Comp Sci, Fac Econ Sci, Moscow, Russia
[2] Moscow State Univ Psychol & Educ, Ctr Neurocognit Res, MEG Ctr, Moscow, Russia
[3] Immanuel Kant Balt Fed Univ, Balt Ctr Neurotechnol & Artificial Intelligence, Kaliningrad, Russia
[4] Academician Natalya Bekhtereva Fdn, St Petersburg, Russia
[5] Save Tibet Fdn, Moscow, Russia
[6] Lomonosov Moscow State Univ, Lab Neurophysiol & Neurocomp Interfaces, Moscow, Russia
关键词
EEG; clustering; unsupervised data annotation; information value; meditation practice; Ward's method; functional states; change point detection; DYNAMICS;
D O I
10.3389/fninf.2023.1301718
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.
引用
收藏
页数:21
相关论文
共 44 条
  • [31] Data-driven spectral decomposition of ECoG signal from an auditory oddball experiment in a marmoset monkey: Implications for EEG data in humans
    Marrouch, Natasza
    Read, Heather L.
    Slawinska, Joanna
    Giannakis, Dimitrios
    2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
  • [32] Tensor decomposition of TMS-induced EEG oscillations reveals data-driven profiles of antiepileptic drug effects
    Tangwiriyasakul, C.
    Premoli, I.
    Spyrou, L.
    Chin, R. F.
    Escudero, J.
    Richardson, M. P.
    SCIENTIFIC REPORTS, 2019, 9 (1)
  • [33] Classifying interpersonal synchronization states using a data-driven approach: implications for social interaction understanding
    Yozevitch, Roi
    Dahan, Anat
    Seada, Talia
    Appel, Daniel
    Gvirts, Hila
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [34] The impact of individual perceptual and cognitive factors on collective states in a data-driven fish school model
    Wang, Weijia L.
    Escobedo, Ramon
    Sanchez, Stephane L.
    Sire, Clement
    Han, Zhangang L.
    Theraulaz, Guy
    PLOS COMPUTATIONAL BIOLOGY, 2022, 18 (03)
  • [35] Green behavior propagation analysis based on statistical theory and intelligent algorithm in data-driven environment
    Zhu, Linhe
    Ding, Yi
    Shen, Shuling
    MATHEMATICAL BIOSCIENCES, 2025, 139
  • [36] A data-driven functional projection approach for the selection of feature ranges in spectra with ICA or cluster analysis
    Krier, C.
    Rossi, F.
    Francois, D.
    Verleysen, M.
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2008, 91 (01) : 43 - 53
  • [37] Understanding the Impacts of Public Facilities on Residential House Prices: Spatial Data-Driven Approach Applied in Hangzhou, China
    Ruan, Linlin
    Lou, Hanning
    Xiao, Wu
    Lu, Debin
    JOURNAL OF URBAN PLANNING AND DEVELOPMENT, 2022, 148 (02)
  • [38] Multivariate Fusion of EEG and Functional MRI Data Using ICA: Algorithm Choice and Performance Analysis
    Levin-Schwartz, Yuri
    Calhoun, Vince D.
    Adali, Tuelay
    LATENT VARIABLE ANALYSIS AND SIGNAL SEPARATION, LVA/ICA 2015, 2015, 9237 : 489 - 496
  • [39] EEG-Based Emotion Recognition Fusing Spacial-Frequency Domain Features and Data-Driven Spectrogram-Like Features
    Wang, Chen
    Hu, Jingzhao
    Liu, Ke
    Jia, Qiaomei
    Chen, Jiayue
    Yang, Kun
    Feng, Jun
    BIOINFORMATICS RESEARCH AND APPLICATIONS, ISBRA 2021, 2021, 13064 : 460 - 470
  • [40] Data-Driven Depot Pre-Positioning Model and Location-Routing Algorithm for Management of Disasters and Disease Outbreaks
    Akwafuo, Sampson E.
    Mikler, Armin R.
    Ihinegbu, Christopher
    37TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, 2022, : 681 - 684