WeDea A New EEG-based Framework for Emotion Recognition


Electroencephalography (EEG) is a technique that has been actively developed and put to use in a variety of fields, including automobiles, robotics, healthcare, and customer support services. This technique was made possible by the advancement of sensing technologies and Machine Learning. EEG is a technique that can determine the emotions and inner states of a human being based on the physiological signals that they emit. As a consequence of this, there is a growing demand for the acquisition and analysis of EEG signals in real time. We aimed to acquire a new EEG dataset based on the discrete emotion theory, which we referred to as WeDea (W ireless-based e eg D ata for e motion a nalysis), and we proposed a new combination for WeDea analysis. Both of these goals were accomplished in this paper. For the WeDea dataset that was collected, we used 15 different volunteers' choices of video clips as emotional stimulants. These clips were selected from online videos. As a result, WeDea is a multi-way dataset that was measured while 30 subjects watched 79 video clips while in one of five distinct emotional states while using a convenient portable headset device. Using this new database, we also developed a system for recognizing the emotional state of human beings. WeDea is a promising resource for emotion analysis, and its findings have demonstrated that it has the potential to be applied to the study of neuroscience. These findings were obtained through practical testing on a variety of emotional states.

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