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DOI: 10.14489/vkit.2024.01.рр.038-045
Асадуллаев Р. Г., Ситникова М. А.
ИНТЕЛЛЕКТУАЛЬНАЯ МОДЕЛЬ КЛАССИФИКАЦИИ ГЕМОДИНАМИЧЕСКИХ ПАТТЕРНОВ МОЗГОВОЙ АКТИВАЦИИ ДЛЯ ВЫЯВЛЕНИЯ НЕЙРОКОГНИТИВНЫХ МЕХАНИЗМОВ ПРОСТРАНСТВЕННО-ЧИСЛОВЫХ АССОЦИАЦИЙ
(с. 38-45)
Аннотация. Представлены результаты разработки и апробации архитектур нейронных сетей глубокого обучения, которые демонстрируют высокую точность при решении задачи классификации паттернов гемодинамической мозговой активации, полученных с помощью функциональной спектроскопии в ближнем инфракрасном диапазоне, в процессе решения математических задач на пространственно-числовые ассоциации. Проведен сравнительный анализ семи архитектур нейронных сетей. Обученные архитектуры рекуррентных нейронных сетей с длинной краткосрочной памятью уступили в точности архитектурам сверточных нейронных сетей с 1D-свертками. Нейронная сеть model_Resnet продемонстрировала показатели точности выше 88 % по трем экспериментальным условиям при определении возрастных различий в мозговой активации в процессе решения заданий на пространственно-числовые ассоциации.
Ключевые слова: сверточная нейронная сеть; нейронная сеть с длинной краткосрочной памятью; остаточная нейронная сеть; глубокое обучение; машинное обучение; функциональная спектроскопия в ближнем инфракрасном диапазоне.
Asadullaev R. G., Sitnikova M. A.
INTELLIGENT MODEL FOR CLASSIFYING HEMODYNAMIC PATTERNS OF BRAIN ACTIVATION TO IDENTIFY NEUROCOGNITIVE MECHANISMS OF SPATIAL-NUMERICAL ASSOCIATIONS
(рр. 38-45)
Abstract. The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.
Keywords: Convolutional neural network; Long short-term memory neural network; Residual neural net-work; Deep learning; Machine learning; Functional near-infrared spectroscopy.
Р. Г. Асадуллаев (Белгородский государственный национальный исследовательский университет, Белгород, Россия) E-mail:
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М. А. Ситникова (Федеральный научный центр психологических и междисциплинарных исследований РАО, Москва, Россия)
R. G. Asadullaev (Belgorod National Research University, Belgorod, Russia) E-mail:
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M. A. Sitnikova (Federal scientific center of psychological and multidisciplinary researches RAE, Moscow, Russia)
1. Dehaene S., Cohen L. Towards an Anatomical and Functional Model of Number Processing // Mathematical Cognition. 1995. Т. 1, № 1. С. 83–120.
2. Fischer M. H., Shaki S. Two Steps to Space for Numbers // Frontiers in Psychology. 2015. Т. 6. С. 612.
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1. Dehaene S., Cohen L. (1995). Towards an Anatomical and Functional Model of Number Processing. Mathematical Cognition, 1(1), 83 – 120.
2. Fischer M. H., Shaki S. (2015). Two Steps to Space for Numbers. Frontiers in Psychology, 6.
3. Schneider M., Merz S., Stricker J. et al. (2018). Associations of Number Line Estimation with Mathematical Competence: A Meta‐Analysis. Child Development, 89(5), 1467 – 1484.
4. Siegler R. S., Opfer J. E. (2003). The Development of Numerical Estimation: Evidence for Multiple Representations of Numerical Quantity. Psychological Science, 14(3), 237 – 250.
5. De Hevia M. D., Girelli L., Macchi Cassia V. (2012). Minds Without Language Represent Number Through Space: Origins of the Mental Number Line. Frontiers in Psychology, 3.
6. Booth J. L., Siegler R. S. (2008). Numerical Magnitude Representations Influence Arithmetic Learning. Child Development, 79(4), 1016 –1031.
7. Quaresima V., Ferrari M. (2019). Functional Near-Infrared Spectroscopy (FNIRS) for Assessing Cerebral Cortex Function During Human Behavior in Natural. Social Situations: A Concise Review. Organizational Research Methods, 22(1), 46 – 68.
8. Cutini S., Moro S. B., Bisconti S. (2012). Functional Near Infrared Optical Imaging in Cognitive Neuroscience: An Introductory Review. Journal of Near Infrared Spectroscopy, 20(1), 75 – 92.
9. Chen W. L., Wagner J., Heugel N. et al. (2020). Functional Near-Infrared Spectroscopy and its Clinical Application in the Field of Neuroscience: Advances and Future Directions. Frontiers in neuroscience, 14.
10. Eastmond C., Subedi A., De S. et al. (2022). Deep Learning in fNIRS: A Review. Neurophotonics, 9(4), 041411 – 041411
11. Hamid H., Nasser N., Nazeer H. et al. (2022). Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks. Sensors, 22(5).
12. Janani J., Sasikala M., Harlen C. et. al. (2020). Investigation of Deep Convolutional Neural Network for Classification of Motor Imagery fNIRS Signals for BCI Applications. Biomedical Signal Processing and Control, 62.
13. Wang R., Hao Y., Yu Q. et al. (2021). Depression Analysis and Recognition Based on Functional Near-Infrared Spectroscopy. IEEE Journal of Biomedical and Health Informatics, 25(12), 4289 – 4299.
14. Gramfort A., Luessi M., Larson E. et al. (2013). MEG and EEG Data Analysis with MNE-Python. Frontiers in Neuroscience.
15. Luke R., Larson E., Shader M. J. et al. (2021). Analysis Methods for Measuring Passive Auditory FNIRS Responses Generated by a Block-Design Para-digm. Neurophotonics, 8(2), 025008 – 025008.
16. Sitnikova M. A., Maraksina J. A., Adamovich T. V. et al. (2023). The Neural Correlates of Exact Calculation in Word and Numerical Formats in Low and High Math Performers: A FNIRS Study. International Journal of Cognitive Research in Science, Engineering & Education (IJCRSEE), 11(1), 93 – 114.
17. Hochreiter S., Schmidhuber J. (1997). Long Short-term Memory. Neural Computation, 9(8), 1735 – 1780.
18. He K., Zhang X., Ren S. et al. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770 – 778.
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