Real-Time On-Chip Machine-Learning-Based Wearable Behind-The-Ear Electroencephalogram Device for Emotion Recognition

In this study, we propose an end-to-end emotion recognition system using an ear-electroencephalogram (EEG)-based on-chip device that is enabled using the machine-learning model. The system has an integrated device that gathers EEG signals from electrodes positioned behind the ear; it is more practical than the conventional scalp-EEG method. The relative power spectral density (PSD), which is the feature used in this study, is derived using the fast Fourier transform over five frequency bands. Directly on the embedded device, data preprocessing and feature extraction were carried out. Three standard machine learning models, namely, support vector machine (SVM), multilayer perceptron (MLP), and one-dimensional convolutional neural network (1D-CNN), were trained on these rich emotion classification features. The traditional approach, which integrates a model into the application software on a personal computer (PC), is cumbersome and lacks mobility, which makes it challenging to use in real-life applications. Besides, the PC-based system is not sufficiently real-time because of the connection latency from the EEG data acquisition device. To overcome these limitations, we propose a wearable device capable of performing on-chip machine learning and signal processing on the EEG data immediately after the acquisition task for the real-time result. In order to perform on-chip machine learning for the real-time prediction of emotions, 1D-CNN was chosen as a pre-trained model using the relative PSD characteristics as input based on the evaluation of the set results. Additionally, we developed a smartphone application that alerted the user whenever a negative emotional state was identified and displayed the information in real life. Our test results demonstrated the feasibility and practicability of our embedded system for real-time emotion recognition.