Trademark Electronic Search System Tess
Electroencephalogram signals are one such source to capture and study human emotions. In this Letter, a novel time-order representation based on the S-transform and convolutional neural network is proposed for the identification of human emotions. EEG signals are transformed into time-order representation based on the S-transform. This TOR is given as an input to CNN to automatically extract and classify the deep features. Emotional states of happiness, fear, sadness, and relax are classified with an accuracy of 94.58%. The superiority of the method is judged by evaluating four performance parameters and comparing it with existing state-of-the-art on the same dataset. One of the issues is the difference between emotional expression amongst various individuals, [...] Read more. Sensor–artery alignment has always been a significant problem in arterial tonometry devices and prevents their application to wearable continuous blood pressure monitoring. Traditional solutions are to use a c