An Intelligent Motor Imagery Detection System Using Electroencephalography with Adaptive Wavelets

Smith Kashiram Khare, Nikhil Gaikwad, Neeraj Dhanraj Bokde*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


Classification of motor imagery (MI) tasks provides a robust solution for specially-abled people to connect with the milieu for brain-computer interface. Precise selection of uniform tuning parameters of tunable Q wavelet transform (TQWT) for electroencephalography (EEG) signals is arduous. Therefore, this paper proposes robust TQWT for automatically selecting optimum tuning parameters to decompose non-stationary EEG signals accurately. Three evolutionary optimization algorithms are explored for automating the tuning parameters of robust TQWT. The fitness function of the mean square error of decomposition is used. This paper also exploits channel selection using a Laplacian score for dominant channel selection. Important features elicited from sub-bands of robust TQWT are classified using different kernels of the least square support vector machine classifier. The radial basis function kernel has provided the highest accuracy of 99.78%, proving that the proposed method is superior to other state-of-the-art using the same database.
Original languageEnglish
Article number8128
JournalSensors (Switzerland)
Publication statusPublished - Oct 2022


  • adaptive wavelets
  • electroencephalogram signals
  • evolutionary optimization algorithms
  • support vector classifiers


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