EEG Signal Classification Using Neural Network and Support Vector Machine in Brain Computer Interface
| Autor: | Shawky Ibrahim, Wael A. Mohamed, M. M. El Bahy, M. Hosny |
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| Rok vydání: | 2016 |
| Předmět: |
Quantitative Biology::Neurons and Cognition
medicine.diagnostic_test Artificial neural network business.industry Computer science Speech recognition Feature extraction Fast Fourier transform Wavelet transform Pattern recognition Computer Science::Human-Computer Interaction 02 engineering and technology Electroencephalography Support vector machine InformationSystems_MODELSANDPRINCIPLES ComputingMethodologies_PATTERNRECOGNITION 020204 information systems Principal component analysis 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Artificial intelligence business Brain–computer interface |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319483078 AISI |
| DOI: | 10.1007/978-3-319-48308-5_24 |
| Popis: | Classification of EEG signals is one of the biggest problems in Brain Computer Interface (BCI) systems. This paper presents a BCI system based on using the EEG signals associated with five mental tasks (baseline, math, mental letter composing, geometric figure rotation and visual counting). EEG data for these five cognitive tasks from one subject were taken from the Colorado University database. Wavelet Transform (WT), Fast Fourier Transform (FFT) and Principal Component Analysis (PCA) were used for features extraction. Artificial Neural Network (ANN) trained by a standard back propagation algorithm and Support Vector Machines (SVMs) were used for classifying different combinations mental tasks. Experimental results show the classification accuracies achieved with the three used feature extraction techniques and the two classification techniques. |
| Databáze: | OpenAIRE |
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