Support vector EEG classification in the Fourier and time-frequency correlation domains

被引：42

作者：

Garcia, GN ^{[1
]}

Ebrahimi, T ^{[1
]}

Vesin, JM ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, EPFL, CH-1015 Lausanne, Switzerland

来源：

1ST INTERNATIONAL IEEE EMBS CONFERENCE ON NEURAL ENGINEERING 2003, CONFERENCE PROCEEDINGS | 2003年

关键词：

direct brain-computer communication; EEG classification; SVM; optimal SVM parameter choice; time-frequency correlation;

D O I：

10.1109/CNE.2003.1196897

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we use support vector machines (SVM) for classifying EEG signals corresponding to imagined motor movements. The parameters of an SVM Kernel are optimized for minimizing a theoretical error bound. Fourier features and correlative time-frequency based features are extracted from EEG signals and compared with respect to their discriminatory power.

引用

页码：591 / 594

页数：4

共 50 条

[1] A combined Fourier analysis and support vector machine for EEG classification
de Carvalho, Jhonnata B.
Silva, Murilo C.
von Borries, George F.
de Pinho, Andre L. S.
von Borries, Ricardo F.
CHILEAN JOURNAL OF STATISTICS, 2019, 10 (01): : 3 - 20
[2] Time-Frequency Representations for EEG Artifact Classification with CNNs
Tiwary, Hrishikesh
Bhavsar, Arnav
2021 IEEE APPLIED IMAGERY PATTERN RECOGNITION WORKSHOP (AIPR), 2021,
[3] Time-frequency analysis of EEG for improved classification of emotion
Vanitha V.
Krishnan P.
Vanitha, V. (vanikkdi@gmail.com), 2017, Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland (23) : 191 - 212
[4] Time-frequency distributions in the classification of epilepsy from EEG signals
Musselman, Marcus
Djurdjanovic, Dragan
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (13) : 11413 - 11422
[5] CLASSIFICATION OF EEG SIGNALS USING ADAPTIVE TIME-FREQUENCY DISTRIBUTIONS
Khan, Nabeel A.
Ali, Sadiq
METROLOGY AND MEASUREMENT SYSTEMS, 2016, 23 (02) : 251 - 260
[6] Time Series Pattern Discovery and Classification with Variable Scales in Time-frequency Domains
Wei C.-X.
Wang Z.-H.
Yuan J.-D.
Lin Q.-H.
Ruan Jian Xue Bao/Journal of Software, 2022, 33 (12): : 4411 - 4428
[7] Comparison of Time-Frequency Feature Extraction Methods for EEG Signals Classification
Rutkowski, Grzegorz
Patan, Krzysztof
Lesniak, Pawel
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT II, 2013, 7895 : 320 - +
[8] Epileptic EEG Classification by Using Time-Frequency Images for Deep Learning
Ozdemir, Mehmet Akif
Cura, Ozlem Karabiber
Akan, Aydin
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2021, 31 (08)
[9] Neural network classification of EEG signals using time-frequency representation
Gope, C
Kehtarnavaz, N
Nair, D
PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5, 2005, : 2502 - 2507
[10] A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals
Durongbhan, Pholpat
Zhao, Yifan
Chen, Liangyu
Zis, Panagiotis
De Marco, Matteo
Unwin, Zoe C.
Venneri, Annalena
He, Xiongxiong
Li, Sheng
Zhao, Yitian
Blackburn, Daniel J.
Sarrigiannis, Ptolemaios G.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2019, 27 (05) : 826 - 835

← 1 2 3 4 5 →