Adaptive Tunable Q Wavelet Transform-Based Emotion Identification

Emotion is a neuronic transient that drives a person to a certain action. Emotion recognition from electroencephalogram (EEG) signals plays a vital role in the development of a brain-computer interface (BCI). Extracting the important information from raw EEG signals is difficult due to its nonstationary nature. Fixing a factual predefined basis function for efficient decomposition using a tunable Q wavelet transform is an arduous task. In this article, an adaptive tunable Q wavelet transform is proposed for the automatic selection of tuning parameters. Optimum tuning parameters are obtained using gray wolf optimization (GWO). Tuning parameters obtained by GWO are used to decompose the EEG signals into subbands (SBs). The set of time-domain features elicited from the SBs are used as an input to multiclass least-squares support vector machine. Classification accuracy of four basic emotions, namely, happy, fear, sad, and relax, is tested and compared with existing methods. An accuracy of 95.70% is achieved with a radial basis function kernel that is about 5% more than the existing methods using the same data set. This article proposes the development of a nonparameterized decomposition method for efficient decomposition of EEG signals. This method can be used with machine learning algorithms to take a step forward in the development of BCI systems.