Application of identity vectors for EEG classification
| Autor: | Christian R. Ward, Iyad Obeid |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Biometrics Computer science Gaussian Feature selection Pattern Recognition Automated 03 medical and health sciences symbols.namesake 0302 clinical medicine Humans Mahalanobis distance business.industry General Neuroscience Brain Electroencephalography Signal Processing Computer-Assisted Pattern recognition Mixture model Brain Waves ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Data Interpretation Statistical symbols Unsupervised learning Mel-frequency cepstrum Artificial intelligence business Classifier (UML) 030217 neurology & neurosurgery Unsupervised Machine Learning |
| Zdroj: | Journal of Neuroscience Methods. 311:338-350 |
| ISSN: | 0165-0270 |
| DOI: | 10.1016/j.jneumeth.2018.09.015 |
| Popis: | Background Finding an optimal EEG subject verification algorithm is a long standing goal within the EEG community. For every advancement made, another feature set, classifier, or dataset is often introduced; tracking improvements in classification without a consistent benchmark, such as a classifier-feature pairing tested on a publicly available dataset, makes it difficult to understand how and why these improvements occur. New Method Following on previous biometric experiments, I-Vectors and Gaussian Mixture Model-Universal Background Models are compared to an established Mahalanobis classifier. A second experiment then addresses the impact of epoch duration as a function of classification performance across all three classifiers. Results The experimental classification results indicate that I-Vectors are more robust than the other classifiers displaying less sensitivity to epoch duration, data composition, and feature selection. Comparison with Existing Methods This I-Vector based approach is compared against commonly used EEG classifiers, such as Mahalanobis and Gaussian mixture models. These classifiers are benchmarked using the publicly available PhysioNet database converted into three feature sets, spectral coherence, power spectral density, and cepstral coefficients. Conclusions The experimental results suggests I-Vectors provide reliable baseline performance by leveling the field between feature set and datasets making them well suited for EEG signal processing tasks. |
| Databáze: | OpenAIRE |
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