Standard
MEG-based Machine Learning Semantic Classification of Observed Words. / Mamaev, Anton; Lebedkin, Dmitri; Kupriyanov, Gavriil et al.
Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 90-92 (Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022).
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
Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022. Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022, Institute of Electrical and Electronics Engineers Inc., pp. 90-92, 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022, Kaliningrad, Russian Federation,
14.09.2022.
https://doi.org/10.1109/CNN56452.2022.9912499APA
Mamaev, A., Lebedkin, D., Kupriyanov, G., Mukha, O., Soghoyan, G., & Sysoeva, O. (2022).
MEG-based Machine Learning Semantic Classification of Observed Words. In
Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022 (pp. 90-92). (Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022). Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/CNN56452.2022.9912499Vancouver
Mamaev A, Lebedkin D, Kupriyanov G, Mukha O, Soghoyan G, Sysoeva O.
MEG-based Machine Learning Semantic Classification of Observed Words. In Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 90-92. (Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022). doi: 10.1109/CNN56452.2022.9912499
Author
Mamaev, Anton ; Lebedkin, Dmitri ; Kupriyanov, Gavriil et al. /
MEG-based Machine Learning Semantic Classification of Observed Words. Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 90-92 (Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022).
BibTeX
@inproceedings{e3785e1910b044aea2420ac5b8115fed,
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 = "decoding, language, machine learning, magnetoencephalography, support vector machines",
author = "Anton Mamaev and Dmitri Lebedkin and Gavriil Kupriyanov and Olga Mukha and Gurgen Soghoyan and Olga Sysoeva",
note = "Funding Information: This work is an output of a research project implemented as part of the Basic Research Program HSE and was carried out using HSE unique equipment (Reg. num 354937). We are thankful to Ksenia Gromova for the MEG data recording. The data analysis was done as a part of the educational program of Center for Cognitive Sciences, Sirius University of Science and Technology. Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Conference {"}Neurotechnologies and Neurointerfaces{"}, CNN 2022 ; Conference date: 14-09-2022 Through 16-09-2022",
year = "2022",
doi = "10.1109/CNN56452.2022.9912499",
language = "English",
isbn = "9781665463294",
series = "Proceedings - 4th International Conference {"}Neurotechnologies and Neurointerfaces{"}, CNN 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "90--92",
booktitle = "Proceedings - 4th International Conference {"}Neurotechnologies and Neurointerfaces{"}, CNN 2022",
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 - Funding Information:
This work is an output of a research project implemented as part of the Basic Research Program HSE and was carried out using HSE unique equipment (Reg. num 354937). We are thankful to Ksenia Gromova for the MEG data recording. The data analysis was done as a part of the educational program of Center for Cognitive Sciences, Sirius University of Science and Technology.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
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 - decoding
KW - language
KW - machine learning
KW - magnetoencephalography
KW - support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85141418260&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/51bd6d7d-1d87-3ac6-8898-7892bba525f0/
U2 - 10.1109/CNN56452.2022.9912499
DO - 10.1109/CNN56452.2022.9912499
M3 - Conference contribution
AN - SCOPUS:85141418260
SN - 9781665463294
T3 - Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022
SP - 90
EP - 92
BT - Proceedings - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference "Neurotechnologies and Neurointerfaces", CNN 2022
Y2 - 14 September 2022 through 16 September 2022
ER -
