## 1 Introduction  
It is widely believed that psychological factors can affect a patient's recovery process; positive emotions, for example, affect the progression of recovery, and a patient's emotional response to his or her illness could also affect the type and amount of medication prescribed by doctors [1]. Psychological treatment is one of the most important factors in a patient's recovery and, when administered properly, might even accelerate the healing process. Although psychologists are still debating whether positive emotions could cure patients suffering from life-threatening illnesses, experts believe that experiencing positive emotions (e.g., feeling happy and stress free) would improve patients' social life, which in turn make patients more productive in their daily activities.  
The difficulty in utilizing the psychological approach in the patient recovery process stems from the patient's ability to hide emotions or the inability to express an emotional condition despite his or her desires, such as cases where patients experience facial nerve paralysis or adhere to certain cultural dynamics. When these situations occur, even social approaches through various communication techniques or observations of body language still pose a challenge, as the nurse or the patient's family members might not be able to accompany the patient at all times. A solution can therefore be found in an automatic emotion recognition tool or system.  
There are several ways to determine human emotions automatically. One popular way is by using a camera and image processing technique to recognize facial expressions; however, this method is ineffective when patients suffer from facial paralysis or when they hide their genuine emotions. Therefore, another approach should be considered, such as automatic emotion recognition that incorporates the use of physiological signals, such as the electrocardiogram (ECG) for monitoring the patient's heart rate, the multi-resonance imaging (f-MRI) for monitoring blood-oxygen-level dependence (BOLD), the eye-gaze trackers (EGT) for monitoring the width of the pupil movements or the EEG for monitoring brain signals. The EEG-based emotion recognition system is preferred due to the advantage of its temporal resolution [2] and the ability of the patient to move around while the EEG-based emotion recognition system is processing the signals.  
The goal of this research is to develop an automatic system for recognizing human emotions through EEG signals. The power spectrum analysis has previously been implemented in the developed automatic emotion recognition system [3], and in this work, the bispectrum analysis, which theoretically is better than the power spectrum analysis, was used as the feature extraction subsystem, while ANNs served as the classifiers. For the purpose of benchmarking the experiment setup and comparing the recognition capability of the developed system, we have used the EEG signals from healthy subjects as provided by the Database for Emotion Analysis Using Physiological Signals (DEAP) [4].  
The rest of the paper is organized as follows: Section 2 presents the literature review on methodologies previously proposed by other researchers; Section 3 discusses the methodology used in this research, the materials of the study, the bispectrum processing technique, including the proposed 3D filtering method for extracting the bispectrum value, and the classifiers used (i.e., the BPNN and the PNN); Section 4 describes in detail the experimental method and discusses the results; finally, Section 5 concludes the paper.