Feature Extraction Using Independent Component Analysis Method from Non-Invasive Recordings of Electroencephalography (EEG) Brain Signals Pages 259-267

Feature Extraction Using Independent Component Analysis Method from Non-Invasive Recordings of Electroencephalography (EEG) Brain Signals
Pages 259-267Creative Commons License

Muhammad Azhar, Ishfaque Ahmed, Syed Tanveer Iqbal, Muhammad Jahangir, Rizwan-ur-Rehman, Nasir Ali Shah and Imran Siddiqui

DOI: https://doi.org/10.6000/1927-5129.2017.13.43
Published: 12 May 2017

Abstract: Electroencephalography (EEG) is a well known procedure in neuroscience, performed to extract brain signal activity associated with voluntary and involuntary tasks. Scientists and researchers working in neuroscience are involved in the research of brain computer interfacing (BCI) and in improving the existing BCI systems. In BCI, it is possible for a person to control the external devices remotely using brain signals without neurophysical intervention. In the proposed work the new algorithm is introduced to extract the feature from EEG based recorded brain signals. The features are extracted for a specific motoryaction that is raising the right hand. The proposed algorithm is also verified from EEGLAB routines also based on Independent Component Analysis (ICA) method written in MATLAB platform.

Keywords: BCI, Electroencephalography, ERP, ICA, Infomax, SOBI, FastICA.