Tackling EEG signal classification with least squares support vector machines: a sensitivity analysis study

Computers in Biology and Medicine
Clodoaldo A M LimaMarcio Eisencraft

Abstract

The electroencephalogram (EEG) signal captures the electrical activity of the brain and is an important source of information for studying neurological disorders. The proper analysis of this biological signal plays an important role in the domain of brain-computer interface, which aims at the construction of communication channels between human brain and computers. In this paper, we investigate the application of least squares support vector machines (LS-SVM) to the task of epilepsy diagnosis through automatic EEG signal classification. More specifically, we present a sensitivity analysis study by means of which the performance levels exhibited by standard and least squares SVM classifiers are contrasted, taking into account the setting of the kernel function and of its parameter value. Results of experiments conducted over different types of features extracted from a benchmark EEG signal dataset evidence that the sensitivity profiles of the kernel machines are qualitatively similar, both showing notable performance in terms of accuracy and generalization. In addition, the performance accomplished by optimally configured LS-SVM models is also quantitatively contrasted with that obtained by related approaches for the same dataset.

References

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Citations

Sep 13, 2014·Computers in Biology and Medicine·Pierre E ChauvetAlain Clément
Apr 9, 2016·Journal of Neuroscience Methods·Emigdio Z-FloresLuis N Coria
Mar 25, 2015·Seizure : the Journal of the British Epilepsy Association·Oliver FaustAmir Adeli
Nov 10, 2017·Journal of Medical Engineering & Technology·A Sharmila, P Mahalakshmi
Jan 30, 2018·IEEE Transactions on Biomedical Circuits and Systems·Lichen FengYuanfa Wang
Nov 23, 2018·Journal of Medical Engineering & Technology·A Sharmila
Mar 16, 2019·Journal of Medical Engineering & Technology·A Sharmila, P Geethanjali
Apr 27, 2019·Biomedizinische Technik. Biomedical Engineering·Ashok Sharmila, Purusothaman Geethanjali
May 24, 2021·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Catarina da Silva LourençoMichel J A M van Putten

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