Table 1.

Wavelet transforms used in the selected papers reviewed.

Paper	Wavelet Transform Involved	Characteristics
Bhattacharyya A, Pachori R [1]	Littlewood–Paley and Meyer wavelet	Filters based on these wavelets are adaptive in the sense that they have a compact frequency support and are centered around a specific frequency.
Jacobs D., Hilton T., Del Campo M., et al. [5]	Complex Morlet wavelet	Complex wavelet transform is less oscillatory and is advantageous in detecting and tracking instantaneous frequencies.
Shivnarayan Patidar and Trilochan Panigrahi [6]	Daubechies filter with two vanishing moments	Filters with lower vanishing moments can be used if the filters are purposely limited in their ability to decompose signal information adequately without using many resources.
Wang D, Ren D, Li K, et al. [8]	Daubechies order 4 wavelet Decomposition used up to fifth level	Fifth level decomposition ensures adequate signal decomposition if the user needs an output of five sub-bands with good resource trade-offs.
Hashem Kalbkhani and Mahrokh G. Shayesteh [9]	N-point discrete Fourier transform derivative	This derivative is the basis of the Stockwell transform used by the author. It provides good resolution of time and frequency.
Muhd Kaleem, Aziz Guergachi, and Sridhar Krishnan [10]	Level 5 Daubechies db6 wavelet is used as the mother wavelet with six vanishing moments	The higher number of vanishing moments is used here since it shows more similarity with the recorded EEG signals.
Mingyang Li, Wanzhong Chen, and Tao Zhang [13]	Dual-tree complex wavelet transform (DT-CWT)	Compared to Discrete Wavelet Transform (DWT), the dual-tree types have approximate shift-invariance and preferable anti-aliasing.