Unique Covariate Identity (UCI) Detection for Emotion Recognition Through EEG Signals

Affective computing has become one of the emerging technologies in the current arena as most of the industries depend on the consumers and their feedbacks. Opinion and emotional feedbacks are playing major ways to improve the quality of services provided by the industry. Affective computing plays a vital role in analyzing emotional feedbacks. The emotions of the human being can be derived by using various ways including facial emotions and textual emotions. Recent research uses biological signals to detect the emotions of human beings. The emotions include anger, sadness, happiness, joy, disgust, surprise generating promising parameters with the biological signal from EEG. Electroencephalography (EEG) based on unique subject identification is evaluated using the presented system. Biological signals are prone to motion artifacts inside the body that distracts during the recordings. EEG signals are complex with numerous oscillating points that are unique in certain cases. The proposed system focused on capturing the impacted component in the brain wave data that produce the unique identification of subjects. The states of the brain wave data like alpha, beta, gamma, theta, and delta are keenly monitored with the help of frequency-domain analysis through discrete wavelet transforms (DWT). In this paper, the analysis of subject impacted factors is detected using a novel multi-nominal regression-based (UCI) unique covariate identity detection algorithm. The proposed system also compared with state-of-the-art approaches in terms of accuracy, precision, and error rate.