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MEG-based Machine Learning Semantic Classification of Observed Words. / Mamaev, Anton ; Lebedkin, Dmitri ; Kupriyanov, Gavriil ; Mukha, Olga ; Soghoyan, Gurgen ; Sysoeva, Olga .

2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN). Institute of Electrical and Electronics Engineers Inc., 2022. p. 90-92.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review

Harvard

Mamaev, A, Lebedkin, D, Kupriyanov, G, Mukha, O, Soghoyan, G & Sysoeva, O 2022, MEG-based Machine Learning Semantic Classification of Observed Words. in 2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN). Institute of Electrical and Electronics Engineers Inc., pp. 90-92, 4th International Conference "Neurotechnologies and Neurointerfaces", Kaliningrad, Russian Federation, 14/09/22. https://doi.org/10.1109/CNN56452.2022.9912499

APA

Mamaev, A., Lebedkin, D., Kupriyanov, G., Mukha, O., Soghoyan, G., & Sysoeva, O. (2022). MEG-based Machine Learning Semantic Classification of Observed Words. In 2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN) (pp. 90-92). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CNN56452.2022.9912499

Vancouver

Mamaev A, Lebedkin D, Kupriyanov G, Mukha O, Soghoyan G, Sysoeva O. MEG-based Machine Learning Semantic Classification of Observed Words. In 2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN). Institute of Electrical and Electronics Engineers Inc. 2022. p. 90-92 https://doi.org/10.1109/CNN56452.2022.9912499

Author

Mamaev, Anton ; Lebedkin, Dmitri ; Kupriyanov, Gavriil ; Mukha, Olga ; Soghoyan, Gurgen ; Sysoeva, Olga . / MEG-based Machine Learning Semantic Classification of Observed Words. 2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN). Institute of Electrical and Electronics Engineers Inc., 2022. pp. 90-92

BibTeX

@inproceedings{eec0a0d2627d45609b983695ec28d585,
title = "MEG-based Machine Learning Semantic Classification of Observed Words",
abstract = "Machine learning methods are starting to be widely used in the analysis of neuroimaging data. Apart from playing a crucial part in the development of Brain-Computer Interface technologies, machine learning can be also used in academic context to link cognitive phenomena to their neurophysiological sources. In this study we attempted to use a SVM model to classify fragments of MEG recording according to the semantic categories of the words that were presented to the subject at the moment. The preprocessed data was clustered in spatial and temporal domains and the clusters were subject to the permutational F-tests. A three-dimensional epochs array was cropped to the time intervals of significant clusters from the selected channels and had its dimensionality reduced with Principal Component Analysis (PCA) or Uniform Manifold Approximation and Projection (UMAP). The resulting vector was used to fit the model to solve the binary classification problem.",
keywords = "Support vector machines, Neuroimaging, Manifolds, Semantics, Machine learning, Optimization, Neurotechnology",
author = "Anton Mamaev and Dmitri Lebedkin and Gavriil Kupriyanov and Olga Mukha and Gurgen Soghoyan and Olga Sysoeva",
note = "A. Mamaev, D. Lebedkin, G. Kupriyanov, O. Mukha, G. Soghoyan and O. Sysoeva, {"}MEG-based Machine Learning Semantic Classification of Observed Words,{"} 2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN), Kaliningrad, Russian Federation, 2022, pp. 90-92, doi: 10.1109/CNN56452.2022.9912499.; 4th International Conference {"}Neurotechnologies and Neurointerfaces{"}, CNN 2022 ; Conference date: 14-09-2022 Through 16-09-2022",
year = "2022",
month = oct,
day = "17",
doi = "10.1109/CNN56452.2022.9912499",
language = "English",
isbn = "978-1-6654-6330-0",
pages = "90--92",
booktitle = "2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

RIS

TY - GEN

T1 - MEG-based Machine Learning Semantic Classification of Observed Words

AU - Mamaev, Anton

AU - Lebedkin, Dmitri

AU - Kupriyanov, Gavriil

AU - Mukha, Olga

AU - Soghoyan, Gurgen

AU - Sysoeva, Olga

N1 - A. Mamaev, D. Lebedkin, G. Kupriyanov, O. Mukha, G. Soghoyan and O. Sysoeva, "MEG-based Machine Learning Semantic Classification of Observed Words," 2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN), Kaliningrad, Russian Federation, 2022, pp. 90-92, doi: 10.1109/CNN56452.2022.9912499.

PY - 2022/10/17

Y1 - 2022/10/17

N2 - Machine learning methods are starting to be widely used in the analysis of neuroimaging data. Apart from playing a crucial part in the development of Brain-Computer Interface technologies, machine learning can be also used in academic context to link cognitive phenomena to their neurophysiological sources. In this study we attempted to use a SVM model to classify fragments of MEG recording according to the semantic categories of the words that were presented to the subject at the moment. The preprocessed data was clustered in spatial and temporal domains and the clusters were subject to the permutational F-tests. A three-dimensional epochs array was cropped to the time intervals of significant clusters from the selected channels and had its dimensionality reduced with Principal Component Analysis (PCA) or Uniform Manifold Approximation and Projection (UMAP). The resulting vector was used to fit the model to solve the binary classification problem.

AB - Machine learning methods are starting to be widely used in the analysis of neuroimaging data. Apart from playing a crucial part in the development of Brain-Computer Interface technologies, machine learning can be also used in academic context to link cognitive phenomena to their neurophysiological sources. In this study we attempted to use a SVM model to classify fragments of MEG recording according to the semantic categories of the words that were presented to the subject at the moment. The preprocessed data was clustered in spatial and temporal domains and the clusters were subject to the permutational F-tests. A three-dimensional epochs array was cropped to the time intervals of significant clusters from the selected channels and had its dimensionality reduced with Principal Component Analysis (PCA) or Uniform Manifold Approximation and Projection (UMAP). The resulting vector was used to fit the model to solve the binary classification problem.

KW - Support vector machines

KW - Neuroimaging

KW - Manifolds

KW - Semantics

KW - Machine learning

KW - Optimization

KW - Neurotechnology

U2 - 10.1109/CNN56452.2022.9912499

DO - 10.1109/CNN56452.2022.9912499

M3 - Conference contribution

SN - 978-1-6654-6330-0

SP - 90

EP - 92

BT - 2022 Fourth International Conference Neurotechnologies and Neurointerfaces (CNN)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 4th International Conference "Neurotechnologies and Neurointerfaces"

Y2 - 14 September 2022 through 16 September 2022

ER -

ID: 102198132