###### Abstract  
Confusion emotion in a learning environment can motivate the learner, but prolonged confusion hinders the learning process. Recognizing confused learners is possible; nevertheless, finding them requires a lot of time and effort. Due to certain restrictions imposed by the settings of an online learning environment, the recognition of confused students is a big challenge for educators. Therefore, novel technologies are necessary to handle such crucial difficulties. Lately, Electroencephalography (EEG)-based emotion recognition systems have been rising in popularity in the domain of Education Technology. Such systems have been utilized to recognize the confusion emotion of learners. Numerous studies have been conducted to recognize confusion emotion through this system since 2013, and because of this, a systematic review of the methodologies, feature sets, and utilized classifiers is a timely necessity. This article presents the findings of the review conducted to achieve this requirement. We summarized the published literature in terms of the utilized datasets, feature preprocessing, feature types for model training, and deployed classifiers in terms of shallow machine learning and deep learning-based algorithms. Moreover, the article presents a comparison of the prediction accuracies of the classifiers and illustrates the existing research gaps in confusion emotion recognition systems. Future study directions for potential research are also suggested to overcome existing gaps. EEG signals; emotion recognition; confusion; learning activities; machine learning; deep learning +
Footnote †: journal:  
brain. This emotion is often utilized in the learning environment as a motivator; however, prolonged confusion levels among learners lead to frustration and boredom [1; 2; 3]. Due to this, educators need to thoroughly monitor learners' confusion levels during their learning process. Various technologies that detect confusion emotion within a digital learning environment, such as learner analytics and sentiment analysis, are presently being utilized [3]. Bio-physiological signal approaches such as using Electroencephalography (EEG), Electrocardiogram (ECG), skin conductance, etc., are now becoming a popular technology over the aforementioned ones, as the latter can offer more accurate and reliable emotion dynamics over the former, which relies on indirect cues. As emotions are considered the source of most physiological responses, the validity and reliability of emotion recognition through these signals are enhanced [4; 5; 6; 7]. Moreover, physiological signals can provide real-time emotional detection, which allows for immediate feedback in human-computer applications. Hence, many researchers in the emotion recognition domain prefer this approach. Out of the physiological signals, many researchers prefer EEG as it captures emotions from the source of generation, that is, the brain [2; 3; 4]. Confusion emotion detection among learners using EEG is still an emerging research domain, where the first publication was reported in 2013 [4]. However, when observing the trend of publications in this domain (Figure 1), this domain did not show intense popularity when compared to research conducted on other emotion recognition systems (Figure 2). This lesser popularity of the confusion emotion recognition domain was the problem that drove the authors to conduct this review with the intention of finding the root causes of this unwavering popularity. Hence, this systematic review was conducted to (i) have a state-of-the-art understanding of the current context; (ii) identify present research gaps and their plausible root causes; and (iii) explore future research directions to encourage potential research. Figure 1: PRISMA workflow diagram of this study. * Indicates that the manuscript reports the number of records identified from each database which is listed in Table 1. ** refers to the number of articles excluded through title/abstract screening.