Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals
| العنوان: | Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals |
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| المؤلفون: | Stefan Haufe, Michael Tangermann, Irene Winkler |
| المصدر: | Behavioral and Brain Functions : BBF Behavioral and Brain Functions, Vol 7, Iss 1, p 30 (2011) |
| بيانات النشر: | Springer Science and Business Media LLC, 2011. |
| سنة النشر: | 2011 |
| مصطلحات موضوعية: | Adult, Male, Mean squared error, Computer science, Auditory event, Cognitive Neuroscience, Linear classifier, Electroencephalography, lcsh:RC346-429, 050105 experimental psychology, User-Computer Interface, Young Adult, 03 medical and health sciences, Behavioral Neuroscience, 0302 clinical medicine, Motor imagery, Reaction Time, medicine, Humans, 0501 psychology and cognitive sciences, lcsh:Neurology. Diseases of the nervous system, Biological Psychiatry, Aged, Brain–computer interface, Communication, medicine.diagnostic_test, business.industry, 05 social sciences, Methodology, Signal Processing, Computer-Assisted, Pattern recognition, General Medicine, Middle Aged, Independent component analysis, Evoked Potentials, Auditory, Artificial intelligence, Artifacts, business, Classifier (UML), 030217 neurology & neurosurgery |
| الوصف: | Background Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. Methods We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. Results Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. Conclusions We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies. |
| تدمد: | 1744-9081 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e9c49244de2726db9d438334ef7f96c3Test https://doi.org/10.1186/1744-9081-7-30Test |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....e9c49244de2726db9d438334ef7f96c3 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17449081 |
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