Stress-Nets- A Novel LSTM Ensembled Single Feed Forward Layers for Stress Classification with EEG Signals

Abstract

Mental instability and emotional imbalance   of the individual can be reflected in the form of stress which results  in an inappropriate work ethics.  There are various methods for the Stress creation. Moreover , several bio-signal sources such as Electroencephalograph(EEG), Electrocardiography(ECG) and Electromyography(EMG) are considered to be the important catalyst for developing the stress detection systems(SDS). Recently, machine and deep learning algorithms has gained more popularity in designing the SDS using the bio-signals. Further, EEG based SDS with ML and DL algorithms plays an important role for its high classification accuracy and performance. However, these EEG based SDS  systems needs the better lime light of improvisation in terms of  performance and computational overhead to deal with multiple datasets.  In this context, this manuscript proposes new  Long Short term Memory recurrent units(LSTM) for extracting temporal features while enhanced extreme learning machines are employed for better classification with less computational complexity. The different data sources are used to collect the EEG signals in which the collected  signals are preprocessed for evaluating the proposed model. Additionally, the experiments are performed by DEAP  and Kaggle datasets as well as performance parameters and  compared by conventional  Fused  Support vector machines (F-SVM),BI-Long Short Term Memory(BILSTM), Random Forest(RF) and  Deep Convolutional Neural network(DCNN).  Results shows which proposed EEG based SDS has better performance than other conventional  ones  of high accuracy in stress detection and for diagnosing classify the stress-levels .