مؤتمر
Feature extraction and classification in a two-state brain-computer interface [Iki durumlu bir beyin bilgisayar arayüzünde özellik çikarimi ve siniflandirma]
| العنوان: | Feature extraction and classification in a two-state brain-computer interface [Iki durumlu bir beyin bilgisayar arayüzünde özellik çikarimi ve siniflandirma] |
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| المؤلفون: | Altindis F., Yilmaz B. |
| المصدر: | 2016 Medical Technologies National Conference, TIPTEKNO 2016 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers Inc. |
| سنة النشر: | 2017 |
| المجموعة: | Bilkent University: Institutional Repository |
| مصطلحات موضوعية: | brain-computer interfaces, classification, EEG, motor imagery, relative band power, Biomedical engineering, Brain computer interface, Classification (of information), Discriminant analysis, Electroencephalography, Feature extraction, Image retrieval, Interface states, Interfaces (computer), Medical computing, Nearest neighbor search, Neurons, Signal processing, Support vector machines, Vectors, Classification methods, Feature extraction and classification, Feature vectors, K nearest neighbor (KNN), Linear discriminant analysis, Paralyzed patients, Biomedical signal processing |
| الوصف: | Brain Computer Interface (BCI) technology is used to help patients who do not have control over motor neurons such as ALS or paralyzed patients, to communicate with outer world. This work aims to classify motor imageries using real-time EEG dataset, which was published by Graz University, Austria. The dataset consists of two-channel EEG signals of right-hand movement imagery and left-hand movement imagery of 8 subjects. There are a total of 120 motor imagery trials (60 left and 60 right) EEG signals recorded from each subject. EEG signals are filtered and feature vectors were extracted that consist of 24, 32 and 40 relative band power values (RBPV). In this work, feature vectors classified by three different methods, linear discriminant analysis (LDA), K nearest neighbor (KNN) and support vector machines (SVM). Results show that best performance was achieved by 24 RBPV feature vector and LDA classification method. © 2016 IEEE. |
| نوع الوثيقة: | conference object |
| وصف الملف: | application/pdf |
| اللغة: | Turkish |
| ردمك: | 978-1-5090-2386-8 1-5090-2386-0 |
| العلاقة: | http://dx.doi.org/10.1109/TIPTEKNO.2016.7863118Test; http://hdl.handle.net/11693/37572Test |
| DOI: | 10.1109/TIPTEKNO.2016.7863118 |
| الإتاحة: | https://doi.org/10.1109/TIPTEKNO.2016.7863118Test http://hdl.handle.net/11693/37572Test |
| رقم الانضمام: | edsbas.8E958248 |
| قاعدة البيانات: | BASE |
| ردمك: | 9781509023868 1509023860 |
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| DOI: | 10.1109/TIPTEKNO.2016.7863118 |