Subject independent emotion recognition using EEG signals employing attention driven neural networks. (May 2022) - Arjun,; Rajpoot, Aniket Singh; Panicker, Mahesh Raveendranatha
Graphical abstract: Highlights: Focus on subject independent tasks to perform better considering inter-subject variability. Proposed a novel LSTM with channel attention autoencoder to obtain a subject-invariant latent representation of EEG data. CNN with attention framework for performing classification task based on latent vectors from autoencoder. Investigation of temporal localization of emotional outbursts. Achieved state-of-the-art results for epileptic seizure detection, showing the task agnostic nature of the proposed architecture.. Abstract: Electroencephalogram (EEG) based emotional analysis has been employed in medical science, security and human–computer interaction with good success. 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. With the proposed methodology, average subject independent accuracies of 65.9%, 69.5% for valence and arousal classification in the DEAP dataset and 76.7% for positive–negative classification in SEED dataset are obtained. Further for the CHB-MIT dataset, average subject independent accuracies of 69.1%, 67.6%, 72.3% for Pre-Ictal Vs Ictal, Inter-Ictal Vs Ictal, Pre-Ictal Vs Inter-Ictal classification are obtained, which are state-of-the-art to the best of our knowledge. 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.