A novel statistical algorithm for multiclass EEG signal classification
Article
Article Title | A novel statistical algorithm for multiclass EEG signal classification |
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ERA Journal ID | 32032 |
Article Category | Article |
Authors | Siuly and Li, Yan (Author) |
Journal Title | Engineering Applications of Artificial Intelligence |
Journal Citation | 34, pp. 154-167 |
Number of Pages | 14 |
Year | Sep 2014 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0952-1976 |
1873-6769 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.engappai.2014.05.011 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0952197614001092 |
Abstract | This paper presents a new algorithm for the classification of multiclass EEG signals. This algorithm involves applying the optimum allocation technique to select representative samples that reflect an entire database. This research investigates whether the optimum allocation is suitable to extract representative samples depending on their variability within the groups in the input EEG data. It also assesses whether these samples are efficient for the multiclass least square support vector machine (MLS-SVM) to classify EEG signals. The performances of the MLS-SVM with four different output coding approaches: minimum output codes (MOC), error correcting output codes (ECOC), One vs One (1vs1) and One vs All (1vsA), are evaluated with a benchmark epileptic EEG database. To test the consistency, all experiments are repeated ten times with the same classifying parameters in each classification process. The results show very high classification performances for each class, and also confirm the consistency of the proposed method in each repeated experiment. In addition, the performances by the optimum allocation based MLS-SVM method are compared with the four existing reference methods using the same database. The outcomes of this research demonstrate that the optimum allocation is very effective and efficient for extracting the representative patterns from the multiclass EEG data, and the MLS-SVM is also very well fitted with the optimum allocation technique for the EEG classification. |
Keywords | Electroencephalogram (EEG); Optimum allocation; Multiclass least square support vector; machine (MLS-SVM); Multiclass classification |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400607. Signal processing |
320222. Radiology and organ imaging | |
320999. Neurosciences not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Mechanical and Electrical Engineering |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2w61/a-novel-statistical-algorithm-for-multiclass-eeg-signal-classification
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