UNSUPERVISED FUZZY C-MEANS CLUSTERING FOR MOTOR IMAGERY EEG RECOGNITION

被引：0

作者：

Hsu, Wei-Yen ^{[1
]}

Lin, Chi-Yuan ^{[2
]}

Kuo, Wen-Feng ^{[3
]}

Liou, Michelle ^{[1
]}

Sun, Yung-Nien ^{[4
]}

Tsai, Arthur Chih-Hsin ^{[1
]}

Hsu, Hsien-Jen ^{[5
]}

Chen, Po-Hsun ^{[6
]}

Chen, I-Ru ^{[7
]}

机构：

[1] Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan

[2] Natl Chin Yi Univ Technol, Dept Comp Sci & Informat Engn, Taiping City 411, Taichung, Taiwan

[3] Natl Cheng Kung Univ Hosp, Dept Med Informat, Tainan 701, Taiwan

[4] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan 701, Taiwan

[5] Natl Cheng Kung Univ, Inst Mfg Informat & Syst, Tainan 701, Taiwan

[6] Natl Cheng Kung Univ, Dept Elect Engn, Tainan 701, Taiwan

[7] Fu Jen Catholic High Sch, Dept Math, Chia I, Taiwan

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2011年 / 7卷 / 08期

关键词：

Brain-computer interface (BCI); Electroencephalogram (EEG); Motor imagery (MI); Fractal dimension (FD); Fuzzy c-means (FCM); ABNORMAL SIGNAL-DETECTION; FRACTAL FEATURES; WAVELET; CLASSIFICATION; ALGORITHM; INTERFACE; MACHINE;

D O I：

暂无

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 study, an electroencephalogram (EEG) recognition system is proposed on single-trial motor imagery (MI) data. Fuzzy c-means (FCM) clustering is used for the unsupervised recognition of left and right MI data by combining with selected active segments and multiresolution fractal features. Active segment selection is used to detect active segments situated at most discriminable areas in the time-frequency domain. The multiresolution fractal features are then extracted by using modified fractal dimension from wavelet data. Finally, FCM clustering is used as the discriminant of MI features. The FCM clustering is an adaptive approach suitable for the clustering of non-stationary biomedical signals. Compared with several popular supervised classifiers, FCM clustering provides a potential for BCI application.

引用

页码：4965 / 4976

页数：12

共 50 条

[1] Unsupervised Multiview Fuzzy C-Means Clustering Algorithm
Hussain, Ishtiaq
Sinaga, Kristina P.
Yang, Miin-Shen
[J]. ELECTRONICS, 2023, 12 (21)
[2] Unsupervised Subpixel Mapping of Remotely Sensed Imagery Based on Fuzzy C-Means Clustering Approach
Zhang, Yihang
Du, Yun
Li, Xiaodong
Fang, Shiming
Ling, Feng
[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2014, 11 (05) : 1024 - 1028
[3] Fuzzy c-means for fuzzy hierarchical clustering
Vicenc, T
[J]. FUZZ-IEEE 2005: PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS: BIGGEST LITTLE CONFERENCE IN THE WORLD, 2005, : 646 - 651
[4] On Tolerant Fuzzy c-Means Clustering
Hamasuna, Yukihiro
Endo, Yasunori
Miyamoto, Sadaaki
[J]. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2009, 13 (04) : 421 - 428
[5] Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering
Krishna, P. Gopala
Bhaskari, D. Lalitha
[J]. PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATION TECHNOLOGIES, IC3T 2015, VOL 1, 2016, 379 : 479 - 486
[6] CLUSTERING OF DETECTED CHANGES IN SATELLITE IMAGERY USING FUZZY C-MEANS ALGORITHM
Sjahputera, O.
Scott, G. S.
Klaric, M. K.
Claywell, B. C.
Hudson, N. J.
Keller, J. M.
Davis, C. H.
[J]. 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, : 468 - 471
[7] Drilling Wear Recognition based on Fuzzy C-means Clustering Algorithm
Yan, Mingxia
[J]. MATERIALS PROCESSING TECHNOLOGY II, PTS 1-4, 2012, 538-541 : 1408 - 1412
[8] Noise subspace fuzzy C-means clustering for robust speech recognition
Gorriz, J. M.
Ramirez, J.
Segura, J. C.
Puntonet, C. G.
Gonzalez, J. J.
[J]. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2006, PT 5, 2006, 3984 : 772 - 779
[9] A Fuzzy Kohonen Local Information C-Means Clustering for Remote Sensing Imagery
Singh, Krishna Kant
Nigam, M. J.
Pal, Kirat
Mehrotra, Akansha
[J]. IETE TECHNICAL REVIEW, 2014, 31 (01) : 75 - 81
[10] Kernelized Fuzzy C-Means Method and Gaussian Mixture Model in Unsupervised Cascade Clustering
Czajkowska, Joanna
Bugdol, Monika
Pietka, Ewa
[J]. INFORMATION TECHNOLOGIES IN BIOMEDICINE, ITIB 2012, 2012, 7339 : 58 - 66

← 1 2 3 4 5 →