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A Novel Approach for the Analysis of Multi-Channel EEG Signal using Advance Technique


  • School of Mechanical Engineering, Lovely Professional University, Jalandhar - Delhi G.T. Road, Phagwara, Punjab – 144411, India
  • School of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar - Delhi G.T. Road, Phagwara, Punjab – 144411, India


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.


Brain Computer Interface (BCI), Electroencephalogram (EEG), Hilbert-Huang Transform (HHT), Intrinsic Mode Function, Multivariate Empirical Mode Decomposition (MEMD).

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  • Singh M, Goyat R. Feature extraction for the analysis of multi-channel EEG signals using hilbert-huang technique. International Journal of Engineering and Technology. 2016; 8(1):17–27.
  • Diez PF, Mut V, Laciar E, Torres A, Avila E. Application of the empirical mode decomposition to extraction of features from EEG signals for mental task classification, 31st Annual international Conference of the IEEE EMBS, Minneapolis, Minnesota, USA; 2009. p. 2579–82.
  • Orosco L, Laciar E, Correa AG, Torres A, Graffigna JP. An epileptic seizures detection algorithm based on the EMD of EEG, 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA; 2009 Sep 2–6.p. 2651–4.
  • Flandrin P, Goncalves P, Rilling G. Detrending and denoising with empirical mode decompositions, 12th European Signal Processing Conference, Vienna, Austria; 2004 Sep 6–11. p. 1581–4.
  • Lin CJ, Hsieh MH. Classification of mental task from EEG data using neural networks based on particle swarm optimization. Neurocomputing. 2009; 72:1121–30.
  • Roy A, Hsien WC, Doherty JF, Mathews JD. Signal Feature Extraction from Microbarograph Observations Using the Hilbert–Huang Transform. IEEE Transactions on Geoscience and Remote Sensing. 2008 May; 46(5):1442–7.
  • Huang NE, Shen Z, Long SR, Wu MC, Shoh HH, Zhenge Q, Yen NC, Tung CC, Liu HH. The EMD and the Hilbert spectrum for non-linear random stationary time series analysis, proceeding of the royal Society of London, Series A: Mathematical, Physical and Engineering Sciences. 1998; 454(1971):903–95.
  • Fahoum ASA, Fraihat AAA. EEG signal features extraction using linear analysis in frequency and time-frequency domains. International Scholarly Research Notices (ISRN) Neuroscience. 2014; 2014:7.
  • Rutkowski TM, Mandic DP, Cichocki A, Przybyszewski AW. EMD approach to multi-channel EEG data-the amplitude and phase synchrony analysis technique. Huang DS et al. ICIC, Springer-Verlag, Berlin Heidelberg; 2008. p. 122–9.
  • Oweis RJ, Abdulhay EW. Seizure classification in EEG signals utilizing HHT. Biomedical Engineering; 2011. p. 2–15.
  • Kaleem MF, Sugavaneswaran L, Guergachi A, Krishnan S. Application of EMD and teager energy operator to EEG signals for mental task classification. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires; 2010. p. 4590–3.
  • Park C, Looney D, Rehman N, Ahrabian A, Mandic DP. Classification of motor imagery BCI using MEMD.IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2013 Jan; 21(1):10–22.
  • Yucelbas S, Ozsen S, Yucelbas C, Tezel G, Kuccukturk S, Yosunkaya S. Effect of EEG time domain features on the classification of sleep stages. Indian Journal of Science and Technology. 2016 July; 9(25):1–8. Doi no:10.17485/ ijst/2016/v9i25/96630
  • Valipour S, Ziaratban M, Shaligram AD. Improving capabilities of the adaptive recursive least-squares filter in the ocular artifact removal from EEG signal. Indian Journal of Science and Technology. 2016 Apr; 9(13):1–11. Doi no:10.17485/ijst/2016/v9i13/85908
  • Xu TK, Paulraj MP. Aggressiveness level assessment using EEG inter channel correlation coefficients. Indian Journal of Science and Technology. 2015 Sep; 8(21):1–10. Doi no:10.17485/ijst/2015/v8i21/79136.
  • Choi W, Lee S, Park J. EEG-biofeedback intervention improves balance in stroke survivor. Indian Journal of Science and Technology. 2015 Aug; 8(18):1–6. Doi no:10.17485/ijst/2015/v8i18/75926.
  • Malik MA, Touqeer M. Soft H-ideals of Soft BCI-algebras. Indian Journal of Science and Technology. 2015 Feb; 8(S3):16–23. Doi no: 10.17485/ijst/2015/v8iS3/60312.
  • Rekha et al. Investigate the features for analysis of EEG signals using MEMD. International Journal for Research in Applied Science & Engineering Technology (IJRASET). 2015; 3(IX):218–23.
  • Rehman N. MEMD. Proceeding of Royal Society
  • A Mathematical Physical and Engineering Sciences; 2009.


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