EEG (Electroencephalography) allows to elicit the mental state of the user, which in turn reveals the user emotion, which is an important factor in HMI (Human Machine Interaction). Researchers across the globe are developing new techniques to increase the EEG accuracy by using different signal processing, statistics, and machine learning techniques in this work we will discuss the most common techniques that can yield to better results, along with discussing the common experiment steps to classify the emotion, starting from collecting the signal, and extracting the features and select the best feature to classify the emotions. Along with highlighting some standing problems in field and potential growth areas.
|Title of host publication||2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)|
|Publication status||Published - 2018|