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EEG Signal Classification using Linear Predictive Cepstral Coefficient Features
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 73 - Number 1 |
| Year of Publication: 2013 |
| Authors: S. Pazhanirajan, P. Dhanalakshmi |
10.5120/12707-9508
|
S. Pazhanirajan, P. Dhanalakshmi . EEG Signal Classification using Linear Predictive Cepstral Coefficient Features. International Journal of Computer Applications. 73, 1 ( July 2013), 28-31. DOI=10.5120/12707-9508
Abstract
An electroencephalogram (EEG) is a procedure that records brain wave patterns, which are used to identify abnormalities related to the electrical activities of the brain. In this study an effective algorithm is proposed to automatically classify EEG clips into two different classes: normal and abnormal. For categorizing the EEG data, feature extraction techniques such as linear predictive coefficients (LPC) and linear predictive cepstral coefficients (LPCC) are used. Support vector machines (SVM) is used to classify the EEG clip into their respective classes by learning from training data.
References
- Golnaz Baghdadi, Ali Motie Nasrabadi (2012). Comparison of different EEG Features in Estimation of Hypnosis Susceptibility Level. Elsevier(pp. 590-597).
- Shang-Ming Zhou, John Q. Gan, Francisco Sepulveda (2007). Classifying mental tasks based on features of higher-orderstatistics from EEG signals in brain–computer interface. Elsevier(pp. 1629-1640).
- Ricardo Aler, Inés M. Galván, José M. Valls (2012). Applying evolution strategies to preprocessing EEG signals for brain–computer interfaces. Elsevier(pp. 53-66).
- Elif Derya Ubeyli, Inan Culer (2009). Measuring saliency of features representing EEG signals using signal-to-noise ratios. Elsevier(pp. 501-509).
- Elif Derya Ubeyli, Inan Culer (2006). Features Extracted by Eigenvector Methods for Detecting Variability of EEG Signals. Elsevier(pp. 592-603).
- Y. Tang, D. M. Durand (2012). A Tunable Support Vector Machine Assembly Classifier for Epileptic Seizure Detection. Elsevier(pp. 3925-3938).
- Z. Khalili1, M. H. Moradi (2008). Emotion Detection Using Brain and Peripheral Signals. Biomedical engineering faculty. IEEE Transactions on Proceedings of the CIBEC.
- Ashish Panat, Anita Patil (2012). Analysis of Emotion Disorders based on EEG signals of Human Brain. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol. 2, No. 4.
- Abdulhamit Subasi (2007). EEG Signal Classification Using Wavelet Feature Extraction and a Mixture of Expert Model. Elsevier(pp. 1084 -1093).
- Duda, R. O. , Hart, P. E. , & Strok, D. G. (2001). Pattern Classification. New York: John Wiley-Interscience.
- P. Dhanalakshmi, S. Palanivel, V. Ramalingam (2009). Classification of Audio Signals Using SVM and RBFNN. Elsevier(pp. 6069-6075).
- R. Courant, H. Hilbert-methods of Mathematical Physics-vol. 1- Interscience Publishers Inc. 1953.
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