Detection of Event Related Patterns using Hilbert Transform in Brain Computer Interface

A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications

“…In the next layer, the low-frequency part is decomposed further until desired features are acquired. WT was utilized for extracting EEG features in [30,37,41,43,46,[54][55][56][57][58][59][60][61][62][63][64][65][66]. The original EEG signal was reduced into detail and approximate frequency coefficients.…”

“…This Bayesian-SSO approach provided improved features for MI categorization and largely helped in attaining >95% accuracy. The authors in [64] exploited the Hilbert transform method for extracting signal features. The proposed Hilbert transform scheme effectively extracted the ERP and band power features for classifying MI tasks.…”

“…. , z n ] through estimating the highest probability using Equation (16): P(C i /Z)= P(C i ) ∏ j P(z j /C j )/P(Z), (16) The naive Bayes technique was employed in [46,47,56,59] for categorizing EEG signals and in [64,65] for MI categorization. The authors in [65], classified EEG-MI signals by assigning a weight for every selected CSP attribute using a weighted naive Bayes method.…”

“…It suggested SVM as the efficient classifier for categorizing EEG signals. Distinct ML classifiers such as SVM, naive Bayes, and LDA were exploited in [64] for classifying both right-and left-hand MI functions. In classifying MI functions, different classifiers such as SVM, naïve Bayes, and LDA achieved 82.22%, 71.64%, and 75.52% accuracy.…”