Recognizing emotions using biological brain signals requires accurate and efficient signal processing and feature extraction methods. Existing methods use several techniques to extract useful features from a fixed number of electroencephalography (EEG) channels. The primary objective of this study was to improve the performance of emotion recognition using brain signals by applying a novel and adaptive channel selection method that acknowledges that brain activity has a unique behavior that differs from one person to another and one emotional state to another. Moreover, we propose identifying epochs, which are the instants at which excitation is maximum, during the emotion to improve the system’s accuracy. We used the zero-time windowing method to extract instantaneous spectral information using the numerator group-delay function to accurately detect the epochs in each emotional state. Different classification scheme were defined using QDC and RNN and evaluated using the DEAP database. The experimental results showed that the proposed method is highly competitive compared with existing studies of multi-class emotion recognition. The average accuracy rate exceeded 89%. Compared with existing algorithms dealing with 9 emotions, the proposed method enhanced the accuracy rate by 8%. Moreover, experiment shows that the proposed system outperforms similar approaches discriminating between 3 and 4 emotions only. We also found that the proposed method works well, even when applying conventional classification algorithms.

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Contrary to popular belief that only the frontal lobe is concerned with emotions, recent neuroscience experiments show that in each emotional state, some of the brain lobes fired clearly whereas some did not. Unlike most of the previous works, which focused on choosing a fixed set of electrodes to detect emotions, this work presents a novel approach that consists of investigating the cerebral activity while experiencing emotions to identify the brain lobes that are showing significant and relevant changes. The identification process of relevant brain lobes is adaptive as the brain activity varies from one person to another and differs from one lobe to another during the same emotional state. The electrodes of the selected brain lobes will be tuned before being used as resources to extract the features that are required for the proposed three phases classification process. Using a clustering technique, the tuning process excludes every electrode that hardly separates between relevant and irrelevant rhythmic brain changes. The results show that the proposed method outperforms all previous approaches. Compared to the best performance obtained by previous studies, the proposed method enhanced the accuracy of both valence and arousal dimensions by 5%. Besides, the accuracy of the dominance dimension was improved by 2%.

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It is becoming increasingly attractive to detect human emotions using electroencephalography (EEG) brain signals. EEG is a reliable and cost-effective technology used to measure brain activities. This paper proposes an original framework for usability testing based on emotion detection using EEG signals, which can significantly affect software production and user satisfaction. This approach can provide an in-depth understanding of user satisfaction accurately and precisely, making it a valuable tool in software development. The proposed framework includes a recurrent neural network algorithm as a classifier, a feature extraction algorithm based on event-related desynchronization and event-related synchronization analysis, and a new method for selecting EEG sources adaptively for emotion recognition. The framework results are promising, achieving 92.13%, 92.67%, and 92.24% for the valence–arousal–dominance dimensions, respectively.

Abstract. Dyslexia Explorer is a screening program for dyslexia that focuses on mapping visual patterns of reading Arabic scripts to reading difficulties. Dyslexia Explorer is designed to process the eye gaze patterns exhibited by readers with Specific Learning Difficulties (SpLDs) in screening sessions with Arabic stimuli. The screening is based on gaze measures of eye fixation duration for the Area Of Interest (AOI), mean fixation duration, fixation count for the AOI, total fixations count, backward patterns (within words, lines and paragraph). The system is a novel contribution in screening for reading difficulties in the Arabic language. It helps in diagnosing dyslexia by specifying reading deficits, providing objective gaze metrics and linking them to phonological processing difficulties of readers.

The analysis of emotions has utility in several applications that cross multiple fields, including education, medicine, psychology, software engineering, accessibility in-habitation studies, healthcare, robotics, marketing, and business. Studying emotions can play an essential role in software engineering, particularly in the domain of usability testing. For example, emotions can be used to determine whether a specific software application achieves acceptable levels of user satisfaction. Furthermore, emotions can be used to test product usability and all its aspects. Emotion detection in usability testing is a first-of-its-kind tool that has the potential to improve software production (designing and interaction), thus enabling the ongoing revolution in software development to continue onwards. This work aims to build an original framework for emotion detection using electroencephalography (EEG) brain signals, which is then applied in usability testing as a case study. This will create opportunities to gain an in-depth understanding of user satisfaction in a precise and accurate way, especially when compared to traditional approaches.

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