journal article·2024Open access

Exploring Motor Imagery EEG: Enhanced EEG Microstate Analysis with GMD-Driven Density Canopy Method

Xin Xiong, Jing Zhang, Sanli Yi, Chunwu Wang, Ruixiang Liu, Jianfeng He

Computers, Materials & Continua· Volume 79· Issue 3

Abstract

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: EEG and Brain-Computer Interfaces · Brain Tumor Detection and Classification · Functional Brain Connectivity Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (46)

Show source-provided references
1. EEG-based functional connectivity to analyze motor recovery after stroke: A pilot study10.1016/j.bspc.2018.12.022 ↗
2. EEG feature pre-processing for neonatal epileptic seizure detection10.1007/s10439-014-1089-2 ↗
3. Perception-related EEG is more sensitive to Alzheimer’s disease effects than resting EEG10.1016/j.neurobiolaging.2016.03.032 ↗
4. Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface10.1371/journal.pone.0026322 ↗
5. Brain-controlled wheelchairs: A robotic architecture10.1109/MRA.2012.2229936 ↗
6. EEG classification of imaginary lower limb stepping movements based on fuzzy support vector machine with kernel-induced membership function10.1007/s40815-016-0259-9 ↗
7. Towards efficient decoding of multiple classes of motor imagery limb movements based on EEG spectral and time domain descriptors10.1007/s10916-017-0843-z ↗
8. Classification scheme for arm motor imagery10.1007/s40846-016-0102-7 ↗
9. K. K. Ang, Z. Y. Chin, H. Zhang, and C. Guan, “Filter bank common spatial pattern (FBCSP) in brain-computer interface,” presented at the 2018 IEEE World Conf. Comput. Intell., Hong Kong, China, Nov. 6–1, 2008. doi: 10.1109/IJCNN.2008.4634130.10.1109/IJCNN.2008.4634130 ↗
10. A new method to generate artificial frames using the empirical mode decomposition for an EEG-based motor imagery BCI10.3389/fnins.2018.00308 ↗
11. EEG source imaging enhances the decoding of complex right-hand motor imagery tasks10.1109/TBME.2015.2467312 ↗
12. Decoding multiclass motor imagery EEG from the same upper limb by combining Riemannian geometry features and partial least squares regression10.1088/1741-2552/aba7cd ↗
13. Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review10.1007/s00521-021-06352-5 ↗
14. Lehmann · Brain Electric Microstates and Cognition: The Atoms of Thought · 1990
15. Microstate dynamics and spectral components as markers of persistent and remittent attention-deficit/hyperactivity disorder10.1016/j.clinph.2024.02.027 ↗
16. Temporal and spatial variability of dynamic microstate brain network in early Parkinson’s disease10.1038/s41531-023-00498-w ↗
17. Biomarkers for prediction of schizophrenia: Insights from resting-state EEG microstates10.1109/ACCESS.2020.3037658 ↗
18. EEG microstate sequences from different clustering algorithms are information-theoretically invariant10.3389/fncom.2018.00070 ↗
19. A componential analysis of the ERP elicited by novel events using a dense electrode array10.1017/S0048577299981180 ↗
20. EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease10.1038/s41598-020-74790-7 ↗
21. Aberrant intrinsic brain activity in patients with autism spectrum disorder: Insights from EEG microstates10.1007/s10548-018-0685-0 ↗
22. On the reliability of the EEG microstate approach10.1007/s10548-023-00982-9 ↗
23. EEG microstate features for schizophrenia classification10.1371/journal.pone.0251842 ↗
24. Research of clustering algorithm based on K-means
25. A novel text clustering algorithm based on feature weighting distance and soft subspace learning
26. An efficient global K-means clustering algorithm10.4304/jcp.6.2.271-279 ↗
27. Method for determining optimal number of clusters in K-means clustering algorithm
28. Improved canopy-ameans algorithm based on MapReduce
29. Improved K-means algorithm based on density canopy10.1016/j.knosys.2018.01.031 ↗
30. Prediction of depression severity scores based on functional connectivity and complexity of the EEG signal10.1177/1550059420965431 ↗
31. Multiple characteristics analysis of Alzheimer’s electroencephalogram by power spectral density and Lempel-Ziv complexity10.1007/s11571-015-9367-8 ↗
32. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review10.1016/j.neuroimage.2017.11.062 ↗
33. Reliability and individual specificity of EEG microstate characteristics10.1007/s10548-020-00777-2 ↗
34. Functional brain microstate predicts the outcome in a visuospatial working memory task10.1016/j.bbr.2016.08.020 ↗
35. Hyperactivation of left inferior parietal lobule and left temporal gyri shortens resting EEG microstate in schizophrenia10.1016/j.schres.2018.06.020 ↗
36. Dual-threshold-based microstate analysis on characterizing temporal dynamics of affective process and emotion recognition from EEG signals10.3389/fnins.2021.689791 ↗
37. EEG based dynamic functional connectivity analysis in mental workload tasks with different types of information10.1109/TNSRE.2022.3156546 ↗
38. Auditory and visual tasks influence the temporal dynamics of EEG microstates during post-encoding rest10.1007/s10548-020-00802-4 ↗
39. Mapping relational links between motor imagery, action observation, action-related language, and action execution10.3389/fnhum.2022.984053 ↗
40. Activation of the brain during motor imagination task with auditory stimulation10.3389/fnins.2023.1130685 ↗
41. Functional properties of brain areas associated with motor execution and imagery10.1152/jn.00132.2002 ↗
42. Motor imagery after subcortical stroke: A functional magnetic resonance imaging study10.1161/STROKEAHA.108.525766 ↗
43. Brain activity during visual versus kinesthetic imagery: An fMRI study10.1002/hbm.20658 ↗
44. Exploring differences between left and right hand motor imagery via spatio-temporal EEG microstate10.1080/24699322.2017.1389404 ↗
45. Exploring differences for motor imagery using Teager energy operator-based EEG microstate analyses10.31083/j.jin2002042 ↗
46. Dynamic convolution with multilevel attention for EEG-based motor imagery decoding10.1109/JIOT.2023.3281911 ↗