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  4. Improvement of cognitive function by virtual spatial cognitive training based on spatial cognitive scales and synchronous intensity analysis for EEG signals

Improvement of cognitive function by virtual spatial cognitive training based on spatial cognitive scales and synchronous intensity analysis for EEG signals

MMC.
2025;
Volume 12, Number 3
: pp. 882–893
Received: March 05, 2025
Revised: September 11, 2025
Accepted: September 13, 2025

Liu Y. J., Wen D., Mustafa M. S., Saripan M. I. B., Ariffin M. R. K.  Improvement of cognitive function by virtual spatial cognitive training based on spatial cognitive scales and synchronous intensity analysis for EEG signals.  Mathematical Modeling and Computing. Vol. 12, No. 3, pp. 882–893 (2025)

Authors:
  1. Y. J. Liu
  2. D. Wen
  3. M. S. Mustafa
  4. M. I. B. Saripan
  5. M. R. K. Ariffin
1
Institute for Mathematical Research, Universiti Putra Malaysia; School of Science, Yanshan University
2
School of Intelligence Science and Technology, University of Science and Technology Beijing
3
Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia
4
Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia
5
Department of Mathematics and Statistics, Faculty of Science, Universiti Putra Malaysia

Cognitive impairment is a chronic degenerative disease of the central nervous system that seriously affects the quality of life of patients and places a heavy burden on society and families.
Among various intervention methods, cognitive training has been widely applied in studies on cognitive improvement in the elderly because of its standardization, systematization, operability, and few side effects.  Computerized cognitive training, introduced in recent years, has been used in intervention studies for various cognitive disorders due to its ease of operation and high therapeutic efficiency.  Moreover, numerous studies have shown that immersive virtual reality training can improve spatial ability more effectively than traditional computer-based training.  In this study, a spatial cognitive training experiment was designed to explore the effects of immersive virtual reality and 2D computer game training on spatial cognitive ability.  Thirteen cognitively healthy elderly individuals were recruited and assigned to either the Virtual Community game experimental group or the 2D Angry Birds game control group.  Twenty-eight days of cognitive training, along with five sessions of the Virtual City Walking testing game and assessment using spatial cognitive scales, were conducted to evaluate the training effects of the VR game and the 2D game.  The experimental results indicated significant differences in spatial cognitive scales, Phase-Locking Value (PLV), and brain networks based on EEG synchronization intensity in the experimental group before and after training.  The findings also confirmed that the virtual reality training game developed in this research could effectively enhance the spatial cognitive abilities of the elderly.

spatial cognition
virtual reality
brain network
phase-locking value
  1. Li X., Feng X., Sun X., Hou N., Han F., Liu Y.  Global, regional, and national burden of Alzheimer's disease and other dementias, 1990–2019.  Frontiers in Aging Neuroscience.  14, 937486 (2022).
  2. Patnode C. D., Perdue L. A., Rossom R. C., Rushkin M. C., Redmond N., Thomas R. G., Lin J. S.  Screening for Cognitive Impairment in Older Adults: Updated Evidence Report and Systematic Review for the US Preventive Services Task Force.  Journal of the American Medical Association.  323 (8), 764–785 (2020).
  3. Matsunaga S., Fujishiro H., Takechi H.  Efficacy and Safety of Cholinesterase Inhibitors for Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.  Journal of Alzheimer's Disease.  71 (2), 513–523 (2019).
  4. Li X., Ji M., Zhang H., Liu Z., Chai Y., Cheng Q., Yang Y., Cordato D., Gao J.  Non-drug Therapies for Alzheimer's Disease: A Review.  Neurology and Therapy.  12 (1), 39–72 (2023).
  5. Harvey P. D., Zayas-Bazan M., Tibiriçá L., Kallestrup P., Czaja S. J.  Improvements in cognitive performance with computerized training in older people with and without cognitive impairment: synergistic effects of skills-focused and cognitive-focused strategies.  The American Journal of Geriatric Psychiatry.  30 (6), 717–726 (2022).
  6. Valdes E. G., Andel R., Lister J. J., Gamaldo A., Edwards J. D.  Can cognitive speed of processing training improve everyday functioning among older adults with psychometrically defined mild cognitive impairment?  Journal of Aging and Health.  31 (4), 595–610 (2019).
  7. Aarsland D., Batzu L., Halliday G. M., Geurtsen G. J., Ballard C., Chaudhuri K. R., Weintraub D.  Parkinson disease-associated cognitive impairment.  Nature Reviews Disease Primers.  7 (1), 47 (2021).
  8. Dang P., Zhu J., Qiao X., Wu J., Li W., You J., Fu L.  How does spatial cognitive style affect indoor fire evacuation wayfinding in mobile virtual reality?  Cartography and Geographic Information Science.  50 (3), 272–288 (2023).
  9. White P. J., Moussavi Z.  Neurocognitive treatment for a patient with Alzheimer's disease using a virtual reality navigational environment.  Journal of Experimental Neuroscience.  10, 129–135 (2016).
  10. Carbonell-Carrera C., Saorin J. L.  Virtual learning environments to enhance spatial orintation.  Eurasia Journal of Mathematics, Science and Technology Education.  14 (3), 709–719 (2017).
  11. Yuan J.  Research on spatial cognitive Training and Evaluation System integrating BRAIN-computer Interface and Virtual Reality.  Master Thesis, Yanshan University (2019).
  12. Bauer A. C. M., Andringa G.  The potential of immersive virtual reality for cognitive training in elderly.  Gerontology.  66 (6), 614–623 (2020).
  13. Wen D., Liang B., Li J., Wu L., Wan X., Dong X.  Feature Extraction Method of EEG Signals Evaluating Spatial Cognition of Community Elderly with Permutation Conditional Mutual Information Common Space Model.  IEEE Transactions on Neural Systems and Rehabilitation Engineering.  31, 2370–2380 (2023).
  14. Sharma G., Gramann K., Chandra S., Singh V., Mittal A. P.  Brain connectivity during encoding and retrieval of spatial information: individual differences in navigation skills.  Brain Informatics.  4 (3), 207–217 (2017).
  15. Jones H. M., Diaz G. K., Ngiam W. X., Awh E.  Electroencephalogram decoding reveals distinct processes for directing spatial attention and encoding into working memory.  Psychological Science.  35 (10), 1108–1138 (2024).
  16. Delorme A., Makeig S.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.  Journal of Neuroscience Methods.  134 (1), 9–21 (2004).
  17. Kessels R. P., Van Zandvoort M. J., Postma A., et al.  The Corsi block-tapping task: standardization and normative data.  Applied Neuropsychology.  7 (4), 252–258 (2000).
  18. Kyritsis M., Gulliver S. R.  Gilford Zimmerman orientation survey: A validation.  2009 7th International Conference on Information, Communications and Signal Processing (ICICS). 1–4 (2009).
  19. Hegarty M., Waller D.  A dissociation between mental rotation and perspective-taking spatial abilities.  Intelligence.  32 (2), 175–191 (2004).
  20. Jones K. T., Peterson D. J., Blacker K. J., Berryhill M. E.  Frontoparietal neurostimulation modulates working memory training benefits and oscillatory synchronization.  Brain Research.  1667, 28–40 (2017).
  21. Popp J. L., Thiele J. A., Faskowitz J., Seguin C., Sporns O., Hilger K.  Structural-functional brain network coupling predicts human cognitive ability.  NeuroImage.  290, 120563 (2024).
  22. Rodriguez-Bores Ramirez L., Saracco-Alvarez R., Escamilla-Orozco R., Fresan Orellana A.  Validity of the Montreal Cognitive Assessment Scale (MoCA) to detect cognitive impairment in schizophrenia.  Salud Mental.  37 (6), 517–522 (2014).
  23. Xia M., Wang J., He Y.  BrainNet Viewer: a network visualization tool for human brain connectomics.  PloS One.  8 (7), e68910 (2013).
  24. Nejati V., Khoshroo S., Mirikaram F.  Review of spatial disability in individuals with attention deficit-hyperactivity disorder: Toward spatial cognition theory.  Clinical Child Psychology and Psychiatry.  29 (1), 312–337 (2024).
  25. Xi J., Huang X.-L., Dang X.-Y., Ge B.-B., Chen Y., Ge Y.  Classification for Memory Activities: Experiments and EEG Analysis Based on Networks Constructed via Phase-Locking Value.  Computational and mathematical methods in medicine.  2022 (1), 3878771 (2022).
  26. Kunz L., Wang L., Lachner-Piza D., Zhang H., Brandt A., Dümpelmann M., Reinacher P. C. et al.  Hippocampal theta phases organize the reactivation of large-scale electrophysiological representations during goal-directed navigation.  Science Advances.  5 (7), eaav8192 (2019).
  27. Bae G. Y., Luck S. J.  Dissociable decoding of spatial attention and working memory from EEG oscillations and sustained potentials.  Journal of Neuroscience.  38 (2), 409–422 (2018).
  28. Pu Y., Cornwell B. R., Cheyne D., Johnson B. W.  High-gamma activity in the human hippocampus and parahippocampus during inter-trial rest periods of a virtual navigation task.  Neuroimage.  178, 92–103 (2018).
  29. Moulaei K., Sharifi H., Bahaadinbeigy K., Dinari F.  Efficacy of virtual reality-based training programs and games on the improvement of cognitive disorders in patients: a systematic review and meta-analysis.  BMC Psychiatry.  24 (1), 116 (2024).
  30. Catania V., Rundo F., Panerai S., Ferri R.  Virtual Reality for the Rehabilitation of Acquired Cognitive Disorders: A Narrative Review.  Bioengineering.  11 (1), 35 (2023).