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Algorithm of Informative Electrode Selection in Person Based Emotion Detection with Hjorth Parameters Obtained from EEG Signals

In this study, it is aimed to compose a method for the selection of electrodes that provides information in order to produce a test device that uses a small number of electrodes for emotion detection with electroencephalography (EEG) signals. In order to prevent data pollution and excessive cost in studies on the brain, as few electrodes as possible have been tried to be chosen as informative. An emotion dataset in the literature was created by recording various biomedical signals that occurred while participants were watching music videos and then voting on their triggered emotions. In the first stage of the study, the brain waves were obtained by filtering the EEG signals for the selected videos from this dataset, and then the Hjorth parameters of the brain waves were calculated and used for analysis. Among these parameters, the activity parameter was used to determine the users used in the analysis, and the mobility parameter was used to determine the informative electrodes. The EEG dataset used in the second stage was recorded for this study by asking the participants questions that triggered their emotions. Using the information obtained in the first stage, the electrode selection for the second stage dataset was tested and observed to be compatible with detecting triggered emotions. As a result, the electrode selection algorithm, which provides emotion detection on a person-based basis, is provided for the datasets used.