Research Progress of Content Generation Model Based on EEG Signals

Abstract

The EEG-based content generation model holds great promise in areas such as emotion recognition, thought decoding, and multimodal interaction. EEG signals can monitor the state of brain activity in real time, thereby enabling the decoding of information related to brain activity, such as emotional states or thought patterns. However, there exist problems such as noise interference, low recognition accuracy, difficulty in signal synchronization, and time delay with action signals. To address these issues, this paper proposes using Independent Component Analysis (ICA) for noise reduction, deep convolutional neural networks for spatial feature extraction, and Dynamic Time Warping (DTW) and Long Short-Term Memory (LSTM) networks for signal alignment. These methods aim to improve signal processing accuracy and alignment efficiency, thereby advancing brain-computer interface technologies.