Emotion Recognition based on EEG Using Fuzzy Deep Learning

Abstract

In recent years, research utilizing EEG for emotion recognition has garnered significant attention. Previous studies have shown that deep learning methods can recognize pleasant and unpleasant emotions triggered by memory with relatively high accuracy. However, the methods currently considered to have high recognition accuracy relay on multi-channel brainwave measurement, big data, which are costly. Therefore, research that ensures accuracy depending on a small amount of learning data is crucial for technological advancement in this field. In this study, we propose an emotion recognition using “fuzzy deep learning.” His method involves classifying converted fuzzy feature images through transfer learning with a pre-trained VGG16 image classification model. We integrated the features of small-channel brainwave data in a fuzzy inference framework and converts them into images through fuzzification using Learning-type Fuzzy Template Matching (L-FTM). The generation mechanism of the fuzzy feature image provides an interface that integrates brainwaves with different characteristics such as signal/noise ratio, average amplitude, and time series changes, without preprocessing. This approach allows the use of individual biometric data for learning.