Vijayan K Asari
University of Dayton, USA
Title: Brain signal analysis for emotion recognition and brain machine interface
Biography
Biography: Vijayan K Asari
Abstract
Emotion recognition by analyzing electroencephalographic (EEG) recordings is a growing area of research. EEG can detect neurological activities and collect data representing brain signals without the need for any invasive technology or procedures. EEG recordings are found useful for the detection of emotions through monitoring the characteristics of spatiotemporal variations of activations inside the brain. Specific spectral descriptors as features are extracted from EEG data to quantify the spatiotemporal variations to distinguish different emotions. Several features representing different brain activities are estimated for the classification of emotions. A brain machine interface using EEG data facilitates the control of machines through the analysis and classification of signals directly from the human brain. The collected EEG data is analyzed by an independent component analysis based feature extraction methodology and classified using a multilayer neural network classifier into several control signals for controlling a robot. The system also collects the data of electromyography signals indicative of movement of the facial muscles. Research work is progressing to extend the range of controls beyond a set of discrete actions by refining the algorithmic steps and procedures.