Subject Independent Emotion Recognition using EEG Signals Employing Attention Driven Neural Networks

TL;DR

This paper introduces a novel deep learning framework that achieves subject-independent emotion recognition from EEG signals by combining an autoencoder with attention mechanisms and a CNN, validated on multiple datasets.

Contribution

A new deep learning approach that effectively generalizes emotion recognition across subjects without hand-engineered features, using attention-driven autoencoders and CNNs.

Findings

Effective subject-independent emotion recognition demonstrated on multiple datasets.

Attention mechanisms highlight significant EEG segments related to emotions.

The framework operates end-to-end without manual feature extraction.

Abstract

In the recent past, deep learning-based approaches have significantly improved the classification accuracy when compared to classical signal processing and machine learning based frameworks. But most of them were subject-dependent studies which were not able to generalize on the subject-independent tasks due to the inter-subject variability present in EEG data. In this work, a novel deep learning framework capable of doing subject-independent emotion recognition is presented, consisting of two parts. First, an unsupervised Long Short-Term Memory (LSTM) with channel-attention autoencoder is proposed for getting a subject-invariant latent vector subspace i.e., intrinsic variables present in the EEG data of each individual. Secondly, a convolutional neural network (CNN) with attention framework is presented for performing the task of subject-independent emotion recognition on the encoded lower dimensional latent space representations obtained from the proposed LSTM with channel-attention autoencoder. With the attention mechanism, the proposed approach could highlight the significant time-segments of the EEG signal, which contributes to the emotion under consideration as validated by the results. The proposed approach has been validated using publicly available datasets for EEG signals such as DEAP dataset, SEED dataset and CHB-MIT dataset. The proposed end-to-end deep learning framework removes the requirement of different hand engineered features and provides a single comprehensive task agnostic EEG analysis tool capable of performing various kinds of EEG analysis on subject independent data.