EEG Based Emotion Sensing using convolutional neural networks

TL;DR

This paper explores the use of convolutional neural networks for emotion detection from EEG signals, highlighting their effectiveness in feature extraction and generalization over large datasets.

Contribution

It demonstrates the application of CNNs to EEG-based emotion sensing, emphasizing improved feature engineering and data interpretation capabilities.

Findings

CNNs effectively extract features from EEG signals.

CNN-based models outperform traditional methods in emotion recognition.

The approach generalizes well to large EEG datasets.

Abstract

Deep Learning has impacted various fields especially in bio-medical applications. Deep learning algorithms work well with both structured and unstructured data. Especially, convolutional neural network work well with signal-based data like EEG data. These types of data may or may not follow a pattern in their data. Algorithms like CNN help feature engineering and simplistic interpretation of the data. These algorithms are also better in comparison to other algorithms when generalised to a data belonging to larger data set.