The comparison of automatic artifact removal methods with robust classification strategies in terms of EEG classification accuracy
- Resource Type
- Conference
- Authors
- Merinov, Pavel; Belyaev, Mikhail; Krivov, Egor
- Source
- 2015 International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON) Biomedical Engineering and Computational Technologies (SIBIRCON), 2015 International Conference on. :221-224 Oct, 2015
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Electroencephalography
Robustness
Band-pass filters
Benchmark testing
Covariance matrices
Noise measurement
Prediction algorithms
- Language
One of the key objectives of brain-computer interface (BCI) design is to construct accurate electroencephalogram (EEG) based classifier. But out of laboratory all EEG signals are contaminated with artifacts, which hamper algorithmic processing and EEG analysis, i.e. classifier ought to get a prediction for noisy data. Real-time BCI system rely on relatively clean EEG signals. Therefore, the exclusion of artifacts is of special interest for BCI applications in everyday life. There are two main approaches to this objective: automatic EEG artifact rejection methods (subtract the noisy component) and robust classification methods (replace sensitive to outliers estimates with robust counterparts). The goal of this work is to quantitatively compare popular automatic EEG artifact rejection approaches with robust classification methods in terms of motor imagery (MI) classification paradigm.