Subject-independent classification on brain-computer interface using autonomous deep learning for finger movement recognition
| Autor: | Mahardhika Pratama, Khairul Anam, Saiful Bukhori, Faruq Sandi Hanggara |
|---|---|
| Přispěvatelé: | Anam, Khairul, Bukhori, Saiful, Hanggara, Faruq Sandi, Pratama, Mahardhika, 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 Montreal, Canada 20-24 July 2020 |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Interface (computing) Movement Electroencephalography autonomous deep learning (ADL) brain computer interface Convolutional neural network 03 medical and health sciences 0302 clinical medicine Deep Learning medicine Humans 030304 developmental biology Brain–computer interface 0303 health sciences medicine.diagnostic_test business.industry electroencephalogram (EEG) Deep learning SIGNAL (programming language) Pattern recognition Construct (python library) Random forest Brain-Computer Interfaces Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery |
| Zdroj: | EMBC |
| Popis: | usc Refereed/Peer-reviewed The degradation of the subject-independent classification on a brain-computer interface is a challenging issue. One method mostly taken to overcome this problem is by collecting as many subjects as possible and then training the system across all subjects. This article introduces streaming online learning called autonomous deep learning (ADL) to classify five individual fingers based on electroencephalography (EEG) signals to overcome the issue above. ADL is a deep learning architecture that can construct its structure by itself through streaming learning and adapt its structure to the changes occurring in the input. In this article, the input of ADL is a common spatial pattern (CSP) extracted from the EEG signal of healthy subjects. The experimental results on the subject-dependence classification across four subjects using 5fold cross-validation show that that ADL achieved the classification accuracy of around 77%. This performance was excellent compared to a random forest (RF) and a convolutional neural network (CNN). They achieved accuracies of about 53% and 72%, respectively. On the subject-independent classification, ADL outperforms CNN by resulting stable accuracies for both training and testing, different from CNN that experience accuracy degradation to approximately 50%. These results imply that ADL is a promising machine learning in dealing with the issue in the subject-independent classification. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|