The activity of neurons inside the human brain produces electrical signals that contain frequencies. An electroencephalogram (EEG) system with a noninvasive device can record brain signals directly from the scalp, these signals are called EEG signals. In motor imaging (MI) task the human brain imagines moving a part of the body without any physical movement. Speech imagery (SI) is also a type of MI task in which the subject imagines speaking without moving the vocal organ or any other articulations. In the last two decades, Brain Computer Interface (BCI) system has been developed to analyze SI and MI tasks of human brain aiding in overcoming critical motor non-functionalities. A BCI system involves the collection, pre-processing, selection, extraction of features, and classification of EEG signals. This systematic literature review (SLR) aims to assist researchers in knowing EEG signals, non-invasive EEG devices and analyzing EEG signals by making use of ML models. This survey is divided into four subsections which explain analysis of SI task for imaging of digits, alphabets or word, MI task for visualization of a picture or a video and left-hand right-hand movement. Based on utilizations of number of channels of EEG device, accuracy of classification models is compared.
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