Wavelet-based fractal features with active segment selection: Application to single-trial EEG data

Neuro-Fuzzy Prediction for Brain-Computer Interface Applications

“…In addition, using a wider frequency range from the acquired EEG signals can generally achieve higher classification accuracy in comparison with a narrower one [33]. A wide frequency range containing all mu and beta rhythm components is adopted to include all the important signal spectra for MI classification.…”

“…A signal is decomposed into numerous details in multiresolution analysis, where each scale represents a class of distinct physical characteristics within the signal. Wavelet transform is used to achieve multiresolutional representation in this study [21,33,[36][37][38][39]. The 1-s segment is decomposed into numerous non-overlapping subbands by wavelet transform.…”

“…A variety of approaches were proposed to estimate fractal dimension from signals or images [21][22][23][24]. A differential box counting (DBC) method covering a wide dynamic range with a low computational complexity is modified and used in this study [33]. A MFFV is extracted by modified fractal dimension from all the non-overlapping subbands of a 1-s segment.…”

Wavelet Transform for the Analysis of EEG Signals in Patients with Oral Communications Problems

“…This database has been used in more occasions to make studies with EEG signals and detect the P300, like in [16] or [29].…”

Sleep stage classification via ensemble and conventional machine learning methods using single channel EEG signals

“…In literature, several methods have been studied on the classification of sleep-stages. The frequency-domain analysis methods [11, 12, and 13], wavelet transform [10,14] and fuzzy logic [15] are examples of some methods with agreement rates ranging from 60% to 80%. Virkkala et al have classified the sleep stages using only EOG signals with the agreement of 72% [16].…”