A Deep CNN System for Classification of Emotions Using EEG Signals

Emotion classification has many applications in human-computer interaction, and is a necessary mode of communication for many different tasks where humans and robots must work together or in close quarters. When working with people who have trouble using verbal communication, or when it is unrealistic to expect verbal communication, robots must still be capable of taking the person's emotions into account, whether through facial cues, body language, or other signals. Electroencephalograms are capable of capturing the signals of the brain, which can be processed and classified using various artificial intelligence architectures. In this paper, a deep convolutional neural network is applied to an emotion classification task, where it successfully learns to identify six second windows as one of four emotions: boredom, relaxation, horror, and humour. The neural network is applied to 14 individuals and a high accuracy of nearly 100% is achieved when the test data is chosen randomly from the dataset. A study is performed to find what conditions in the data are necessary for high classification accuracy. The emotion data was collected from subjects as they played four games of different genres, designed to evoke one emotion out of boredom, relaxation, humour, or fear, as assessed by the professional game critic services.