Indian Journal of Science and Technology
DOI: 10.17485/ijst/2016/v9i45/98350
Year: 2016, Volume: 9, Issue: 45, Pages: 1-7
Original Article
Mahipal Singh1* and Rekha2
1School of Mechanical Engineering, Lovely Professional University, Jalandhar - Delhi G.T. Road, Phagwara, Punjab – 144411, India; [email protected], 2School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar - Delhi G.T. Road, Phagwara, Punjab – 144411, India; [email protected]
*Author for correspondence
Mahipal Singh School of Mechanical Engineering, Lovely Professional University, Jalandhar - Delhi G.T. Road, Phagwara, Punjab – 144411, India; [email protected]
Objectives: In this research paper, Electroencephalogram (EEG) is recorded by placing electrodes on the scalp for different mental task. The significant features are extracted for three different metal tasks as mental arithmetic, baseline and letter composing. Methods/Statistical analysis: In the EEG signals, there are many features which having some significant information and some having false. The significant features are extracted by using advance techniques as Multivariate Empirical Mode Decomposition (MEMD) and Hilbert-Huang Transform (HHT). The t-paired test is used for determining the discrimination power of extracted features. Findings: After applying MEMD techniques we have achieved twelve multivariate Intrinsic Mode Functions (IMFs) and one residue. Most sensitive IMFs are selected by calculating Power Spectral Density (PSD) of each IMFs functions by Welch method. The Instantaneous Amplitude (IA) and Instantaneous Phase (IP) from most sensitive IMF are investigated by using Hilbert Huang transform (HHT) and features such as min., max., Skewness and kurtosis are extracted from IA and IP. The feature values are tested for their class discrimination power (p< .05) using paired t test. The results of paired t test support their applicability to be used as feature vector for any classification application. Accuracy nearby 80% to 90% is procured for different mental task EEG signals by using these extracted features. Application/Improvements: The investigated results are applicable for Brain Computer Interface. If a handicapped person (his hands and legs are not working/living) wants to write some letters on the screen of a desktop, so by putting the electrode on scalp and by measuring the electrical signals of his/her brain through EEG, we can apply these significant features for converting the electrical signals in to letters with the help of computer. In this research, we have investigated only linear feature, so further research area is open for investigating of Non-linear features of EEG signals.
Keywords: Brain Computer Interface (BCI), Electroencephalogram (EEG), Hilbert-Huang Transform (HHT), Intrinsic Mode Function, Multivariate Empirical Mode Decomposition (MEMD)
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