Multimodal Deep Sparse Subspace Clustering for Multiple Stimuli-based Cognitive task

Cognitive state assessment can be effectively performed using Electroencephalogram (EEG). However, due to the curse of dimensionality issues of EEG, most of the clustering methods often lead to poor performance. Deep neural network-based representation learning transforms high-dimensional data into lower-dimensional feature space, increasing the clustering performance. This paper proposes an efficient multimodal (spectral-temporal) deep clustering model to evaluate workload levels from multiple stimuli (visual and auditory)-based n-back task. The proposed model extracts the temporal and spectral EEG features from sequence-wise EEG signal and spectral power. The combined spectral-temporal low-dimensional latent feature is passed to the sparse subspace clustering (SSC) model to estimate different workload levels. The temporal and spectral latent features are learned using the Long short-term memory (LSTM) and Convolutional Neural Network (CNN)-based variational autoencoder (VAE) model. In the SSC method, the collection of data points that lies in the union of low-dimensional subspaces forms a cluster. Here, each cluster overcomes the effect of the outliers from subspace, improving the cluster quality. The proposed model achieves the best clustering accuracy of 98.2% in the subject-independent test and a mean clustering accuracy of 95.2%. The proposed model achieves a significant improvement over the state-of-the-art studies. The effectiveness of the model is also evaluated on two other publicly available n-back datasets. The proposed model enhances the future scope of the deep representation learning-based clustering approach for other cognitive tasks.