Unveiling Emotions from EEG: A GRU-Based Approach

One of the most important study areas in affective computing is emotion identification using EEG data. In this study, the Gated Recurrent Unit (GRU) algorithm, which is a type of Recurrent Neural Networks (RNNs), is tested to see if it can use EEG signals to predict emotional states. Our publicly accessible dataset consists of resting neutral data as well as EEG recordings from people who were exposed to stimuli evoking happy, neutral, and negative emotions. For the best feature extraction, we pre-process the EEG data using artifact removal, bandpass filters, and normalization methods. With 100% accuracy on the validation set, our model produced outstanding results by utilizing the GRU's capacity to capture temporal dependencies. When compared to other machine learning techniques, our GRU model's Extreme Gradient Boosting Classifier had the highest accuracy. Our investigation of the confusion matrix revealed insightful information about the performance of the model, enabling precise emotion classification. This study emphasizes the potential of deep learning models like GRUs for emotion recognition and advances in affective computing. Our findings open up new possibilities for interacting with computers and comprehending how emotions are expressed through brainwave activity.

Noise removal methods on ambulatory EEG: A Survey

Over many decades, research is being attempted for the removal of noise in the ambulatory EEG. In this respect, an enormous number of research papers is published for identification of noise removal, It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection and removal of an noise. More than 100 research papers have been discussed to discern the techniques for detecting and removal the ambulatory EEG. Further, the literature survey shows that the pattern recognition required to detect ambulatory method, eye open and close, varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG noise data from a various condition of EEG data.