Standard

Software Framework for EEG Signals Analysis Using Machine Learning Methods. / Shanarova, N.; Lipkovich, M.; Pronina, M.; Knyazeva, V.; Sagatdinov, A.; Aleksandrov, A.; Kropotov, J.; Ponomarev, V.

Intelligent Systems . 2026. p. 41-52 (Communications in Computer and Information Science; Vol. 2605 CCIS).

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

Harvard

Shanarova, N, Lipkovich, M, Pronina, M, Knyazeva, V, Sagatdinov, A, Aleksandrov, A, Kropotov, J & Ponomarev, V 2026, Software Framework for EEG Signals Analysis Using Machine Learning Methods. in Intelligent Systems . Communications in Computer and Information Science, vol. 2605 CCIS, pp. 41-52, 16th International Conference on Intelligent Systems, Москва, Russian Federation, 2/12/24. https://doi.org/10.1007/978-3-032-04764-9_4

APA

Shanarova, N., Lipkovich, M., Pronina, M., Knyazeva, V., Sagatdinov, A., Aleksandrov, A., Kropotov, J., & Ponomarev, V. (2026). Software Framework for EEG Signals Analysis Using Machine Learning Methods. In Intelligent Systems (pp. 41-52). (Communications in Computer and Information Science; Vol. 2605 CCIS). https://doi.org/10.1007/978-3-032-04764-9_4

Vancouver

Shanarova N, Lipkovich M, Pronina M, Knyazeva V, Sagatdinov A, Aleksandrov A et al. Software Framework for EEG Signals Analysis Using Machine Learning Methods. In Intelligent Systems . 2026. p. 41-52. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-032-04764-9_4

Author

Shanarova, N. ; Lipkovich, M. ; Pronina, M. ; Knyazeva, V. ; Sagatdinov, A. ; Aleksandrov, A. ; Kropotov, J. ; Ponomarev, V. / Software Framework for EEG Signals Analysis Using Machine Learning Methods. Intelligent Systems . 2026. pp. 41-52 (Communications in Computer and Information Science).

BibTeX

@inproceedings{ae75266150a6408fa8a7a0aac8d50d12,
title = "Software Framework for EEG Signals Analysis Using Machine Learning Methods",
abstract = "This paper introduces a software framework developed for analyzing EEG signal using machine learning methods. The framework consists of several independent and customizable modules for signal acquisition and preprocessing, feature extraction, model training, evaluation, and interpretation. A unique aspect is the flexibility to tune hyperparameters across all stages of preprocessing and feature extraction. The framework was applied to two tasks: diagnosis of mental disorders and detection of intention to perform a hand movement. The results demonstrate balanced accuracy rates of 91% for schizophrenia diagnosis, 88% for obsessive-compulsive disorder diagnosis and 77% for movement intention detection. The methodologies employed for both tasks are detailed in the study. {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.",
keywords = "electroencephalogram, event-related potentials, machine learning, psychiatric diagnosis, software framework, Biomedical signal processing, Computer aided diagnosis, Diseases, Extraction, Feature extraction, Learning systems, Signal analysis, Customizable, EEG signals, EEG signals analysis, Event related potentials, Features extraction, Machine learning methods, Machine-learning, Psychiatric diagnose, Signal acquisitions, Software frameworks, Electroencephalography",
author = "N. Shanarova and M. Lipkovich and M. Pronina and V. Knyazeva and A. Sagatdinov and A. Aleksandrov and J. Kropotov and V. Ponomarev",
note = "Export Date: 29 March 2026; Cited By: 0; Correspondence Address: N. Shanarova; Institute of Problems in Mechanical Engineering, Saint Petersburg, Russian Federation; email: nadya.shanarova@gmail.com; Conference name: 16th International Conference on Intelligent Systems, INTELS 2024; Conference date: 2 December 2024 through 4 December 2024; Conference code: 342679; null ; Conference date: 02-12-2024 Through 04-12-2024",
year = "2026",
doi = "10.1007/978-3-032-04764-9_4",
language = "Английский",
isbn = "9783032047632",
series = "Communications in Computer and Information Science",
pages = "41--52",
booktitle = "Intelligent Systems",

}

RIS

TY - GEN

T1 - Software Framework for EEG Signals Analysis Using Machine Learning Methods

AU - Shanarova, N.

AU - Lipkovich, M.

AU - Pronina, M.

AU - Knyazeva, V.

AU - Sagatdinov, A.

AU - Aleksandrov, A.

AU - Kropotov, J.

AU - Ponomarev, V.

N1 - Export Date: 29 March 2026; Cited By: 0; Correspondence Address: N. Shanarova; Institute of Problems in Mechanical Engineering, Saint Petersburg, Russian Federation; email: nadya.shanarova@gmail.com; Conference name: 16th International Conference on Intelligent Systems, INTELS 2024; Conference date: 2 December 2024 through 4 December 2024; Conference code: 342679

PY - 2026

Y1 - 2026

N2 - This paper introduces a software framework developed for analyzing EEG signal using machine learning methods. The framework consists of several independent and customizable modules for signal acquisition and preprocessing, feature extraction, model training, evaluation, and interpretation. A unique aspect is the flexibility to tune hyperparameters across all stages of preprocessing and feature extraction. The framework was applied to two tasks: diagnosis of mental disorders and detection of intention to perform a hand movement. The results demonstrate balanced accuracy rates of 91% for schizophrenia diagnosis, 88% for obsessive-compulsive disorder diagnosis and 77% for movement intention detection. The methodologies employed for both tasks are detailed in the study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

AB - This paper introduces a software framework developed for analyzing EEG signal using machine learning methods. The framework consists of several independent and customizable modules for signal acquisition and preprocessing, feature extraction, model training, evaluation, and interpretation. A unique aspect is the flexibility to tune hyperparameters across all stages of preprocessing and feature extraction. The framework was applied to two tasks: diagnosis of mental disorders and detection of intention to perform a hand movement. The results demonstrate balanced accuracy rates of 91% for schizophrenia diagnosis, 88% for obsessive-compulsive disorder diagnosis and 77% for movement intention detection. The methodologies employed for both tasks are detailed in the study. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

KW - electroencephalogram

KW - event-related potentials

KW - machine learning

KW - psychiatric diagnosis

KW - software framework

KW - Biomedical signal processing

KW - Computer aided diagnosis

KW - Diseases

KW - Extraction

KW - Feature extraction

KW - Learning systems

KW - Signal analysis

KW - Customizable

KW - EEG signals

KW - EEG signals analysis

KW - Event related potentials

KW - Features extraction

KW - Machine learning methods

KW - Machine-learning

KW - Psychiatric diagnose

KW - Signal acquisitions

KW - Software frameworks

KW - Electroencephalography

UR - https://www.mendeley.com/catalogue/ffdd6697-7eec-3a19-ba55-3b07f6ec2057/

U2 - 10.1007/978-3-032-04764-9_4

DO - 10.1007/978-3-032-04764-9_4

M3 - статья в сборнике материалов конференции

SN - 9783032047632

T3 - Communications in Computer and Information Science

SP - 41

EP - 52

BT - Intelligent Systems

Y2 - 2 December 2024 through 4 December 2024

ER -

ID: 151442974