Extraction and Localization of Non-contaminated Alpha and Gamma Oscillations from EEG Signal Using Finite Impulse Response, Stationary Wavelet Transform, and Custom FIR

The alpha and gamma oscillations derived from EEG signal are useful tools in recognizing a cognitive state and several cerebral disorders. However, there are undesirable artifacts that exist among the electrophysiological signals which lead to unreliable results in the extraction and localization of these accurate oscillations. We introduced, three filtering techniques based on Finite Impulse Response filters FIR, Stationary Wavelet transform SWT method and custom FIR filter to extract the non-contaminated (pure) oscillations and localize their responsible sources using the Independent Component Analysis ICA technique. In our obtained results, we compared the effectiveness of these filtering techniques in extracting and localizing of non-contaminated alpha and gamma oscillations. We proposed here the accurate technique for the extraction of pure alpha and oscillations. We also presented the accurate cortical region responsible of the generation of these oscillations.

[1]  Silviu-Ioan Filip,et al.  A Robust and Scalable Implementation of the Parks-McClellan Algorithm for Designing FIR Filters , 2016, ACM Trans. Math. Softw..

[2]  C. Bénar,et al.  Separation between spikes and oscillation by stationary wavelet transform implemented on an embedded architecture , 2017, Journal of the Neurological Sciences.

[3]  Fabrice Bartolomei,et al.  Despikifying SEEG signals using a temporal basis set , 2015, 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA).

[4]  S. Moshé,et al.  Scalp EEG Ictal gamma and beta activity during infantile spasms: Evidence of focality , 2017, Epilepsia.

[5]  Martine Gavaret,et al.  Despiking SEEG signals reveals dynamics of gamma band preictal activity , 2017, Physiological measurement.

[6]  Tarek Frikha,et al.  Adaptive architecture for medical application case study: Evoked Potential detection using matching poursuit consensus , 2015, 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA).

[7]  C. Ben Amar,et al.  Integration of stationary wavelet transform on a dynamic partial reconfiguration for recognition of pre-ictal gamma oscillations , 2018, Heliyon.

[8]  J. McClellan,et al.  Chebyshev Approximation for Nonrecursive Digital Filters with Linear Phase , 1972 .

[9]  Saeid Sanei,et al.  Adaptive Processing of Brain Signals , 2013 .

[10]  Abdennaceur Kachouri,et al.  A comparison of methods for separation of transient and oscillatory signals in EEG , 2011, Journal of Neuroscience Methods.

[11]  Abdennaceur Kachouri,et al.  The detection of Evoked Potential with variable latency and multiple trial using Consensus matching pursuit , 2014, 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[12]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[13]  Reecha Sharma,et al.  Comparative study of FIR and IIR filters for the removal of 50 Hz noise from EEG signal , 2016 .