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| Method name | Advantages | Disadvantages | Analysis method | Suitability |
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| Fast fourier transform | (i) Good tool for stationary signal processing
(ii) It is more appropriate for narrowband signal, such as sine wave
(iii) It has an enhanced speed over virtually all other available methods in real-time applications | (i) Weakness in analyzing nonstationary signals such as EEG
(ii) It does not have good spectral estimation and cannot be employed for analysis of short EEG signals
(iii) FFT cannot reveal the localized spikes and complexes that are typical among epileptic seizures in EEG signals
(iv) FFT suffers from large noise sensitivity, and it does not have shorter duration data record | Frequency domain | Narrowband, stationary signals |
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| Wavelet transform | (i) It has a varying window size, being broad at low frequencies and narrow at high frequencies
(ii) It is better suited for analysis of sudden and transient signal changes
(iii) Better poised to analyze irregular data patterns, that is, impulses existing at different time instances | Needs selecting a proper mother wavelet | Both time and freq. domain, and linear | Transient and stationary signal |
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| Eigenvector | Provides suitable resolution to evaluate the sinusoid from the data | Lowest eigenvalue may generate false zeros when Pisarenko’s method is employed | Frequency domain | Signal buried with noise |
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| Time frequency distribution | (i) It gives the feasibility of examining great continuous segments of EEG signal
(ii) TFD only analyses clean signal for good results | (i) The time-frequency methods are oriented to deal with the concept of stationary; as a result, windowing process is needed in the preprocessing module
(ii) It is quite slow (because of the gradient ascent computation)
(iii) Extracted features can be dependent on each other | Both time and frequency domains | Stationary signal |
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| Autoregressive | (i) AR limits the loss of spectral problems and yields improved frequency resolution
(ii) Gives good frequency resolution
(iii) Spectral analysis based on AR model is particularly advantageous when short data segments are analyzed, since the frequency resolution of an analytically derived AR spectrum is infinite and does not depend on the length of analyzed data | (i) The model order in AR spectral estimation is difficult to select
(ii) AR method will give poor spectral estimation once the estimated model is not appropriate, and model’s orders are incorrectly selected
(iii) It is readily susceptible to heavy biases and even large variability | Frequency domain | Signal with sharp spectral features |
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