Classification of multichannel EEG signal by single layer perceptron learning algorithm

• Main Authors: Hasan, Mohammad Rubaiyat, Ibrahimy, Muhammad Ibn, Motakabber, S. M. A., Shahid, Shahjahan
• Format: Conference or Workshop Item
• Language: English; English
• Published: 2014
• Subjects: T Technology (General)
• Online Access: http://irep.iium.edu.my/39001/1/39001_edited.pdf; http://irep.iium.edu.my/39001/4/39001_Classification%20of%20multichannel%20EEG%20signal_Scopus.pdf; http://irep.iium.edu.my/39001/; http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7031650

Motor imagery (MI) related Electroencephalogram (EEG) signal classification is one of the main challenge in designing a brain computer interface (BCI) system. Single Layer Perceptron Learning (SLPL) algorithm has a very low computational requirement which makes it suitable for online BCI system. This paper proposes an advanced and simple classification technique for MI related BCI system. Initially the signal is extracted for different features. The SLPL classifier has been used to propose technique to design an MI based BCI. For contrastive comparison with other classification techniques have been evaluated by classification accuracy, mutual information and Cohen’s kappa.