Kumar, Shiu and Sharma, Alokanand and Mamun, Kabir and Tsunoda, T. (2015) Application of cepstrum analysis and linear predictive coding for motor imaginary task classification. [Conference Proceedings]
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Abstract
In this paper, classification of electroencephalography (EEG) signals of motor imaginary tasks is studied using cepstrum analysis and linear predictive coding (LPC). The Brain-Computer Interface (BCI) competition III dataset IVa containing motor imaginary tasks for right hand and foot of five subjects are used. The data was preprocessed by applying whitening and then filtering the signal followed by feature extraction. A random forest classifier is then trained using the cepstrum and LPC features to classify the motor imaginary tasks. The resulting classification accuracy is found to be over 90%. This research shows that concatenating appropriate different types of features such as cepstrum and LPC features hold some promise for the classification of motor imaginary tasks, which can be helpful in the BCI context.
| Item Type: | Conference Proceedings |
|---|---|
| Uncontrolled Keywords: | brain-computer interfaces;electroencephalography;feature extraction;filtering theory;linear predictive coding;medical signal processing;signal classification;BCI competition;LPC;brain-computer interface competition III dataset IVa;cepstrum analysis;electroencephalography signal classification;feature extraction;forest classifier;linear predictive coding;motor imaginary task classification;signal filtering;whitening;Adaptive filters;Cepstrum;Electroencephalography;Feature extraction;Filter banks;Speech recognition;Support vector machines;BCI;EEG;LPC;cepstrum analysis;filetring;random forest |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics |
| Depositing User: | Kabir Mamun |
| Date Deposited: | 01 Jul 2016 01:37 |
| Last Modified: | 01 Jul 2016 01:37 |
| URI: | https://repository.usp.ac.fj/id/eprint/9015 |
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