The purpose of this study is to examine whether the neural correlates of lexical expectations could be used to predict speech in noise perception. We analyse mag-netoencephalography (MEG) data from 20 normal hearing participants, who read a set of couplets (a pair of phrases with rhyming end words) prior to the experiment. During the experiment, the participants are asked to listen to the couplets, whose intelligibility is set to 80%. However, the last word is pronounced with a delay of 1600 ms (i.e. expectation gap) and is masked at 50% of intelligibility. At the end of each couplet, the participants are asked to indicate if the last word was cor-rect, i.e. corresponding to the expected word. Given the oscillatory characteristics of neural patterns of lexical expectations during the expectation gap, can we predict the participant’s actual perception of the last word? In order to approach this re-search question, we aim to identify the correlation patterns between the instances of neural pre-activation, occurring during the interval of the expectation gap and the type of the given answer. According to the sequential design of the experiment, the expectation gap is placed 4400 ms prior to the time interval dedicated to the participant’s answer. Machine Learning approach has been chosen as the main tool for the pattern recognition.