## 1 Introduction
### Background
In this paper, we have not only devised methods that outperform many existing techniques in epileptic seizure detection from EEG signal analysis but also implemented the ideas as a mobile development application and a functional framework. The computer-based monitoring systems can be effectively enhanced by utilizing the application of Internet of Things (IoT) technology [14]. [PERSON] et al. developed a technique in 2019 in the field of BCI. They were able to develop and control a wireless robot utilizing an EEG signal that was recorded directly from the brain via a MindWave EEG headset in relation to attention. The EEG went through pre-processing, feature extraction and classification, which was then communicated to the App using TCP/IP. Feedback was provided from the robot to the BCI App [15]. In 2020, [PERSON] developed a BCI method proving that a relationship can be found between problem-solving requiring algorithmic thinking and executive function. The Hanoi-tower test and Flowchart-based debugging test were used, respectively. Based on the method results, a positive correlation was indicated between the level of Executive Functions and Algorithmic Problem-solving [16]. [PERSON] et al. utilized a GP3 eye tracker to conduct research in the field of BCI in 2019 on school-age children to assess visual attention using the OpenGazeAndMouseAnalyzer (OGAMA) application. They proposed a technology-based paradigm to accurately measure psychological outcomes [17]. [PERSON] et al. studied and tested eye-hand coordination in relation to the movement of a computer mouse cursor on the screen. The research was tested using an eye tracker and a leap motion controller in 2021 [18]. Their results are useful for developing future HCI research. In 2020, [PERSON] et al. were able to test an HCI method based on eye-tracking that involved the determination of the outcome of two different queries where various programming syntaxes were compared by eye parameters [19]. Due to the large amount of data recorded by EEGs, visual analysis of the data has become challenging. Moreover, epilepsy diagnosis by EEG signals requires a human evaluator and is laborious and expensive, as it requires long hours of expert analysis, which means it can be prone to human error. Therefore, many studies have focused on developing an automated, computerized model for the analysis of EEG signals [1,13]. Previously proposed methods typically perform feature extraction of the EEG signal for analysis. These features include time, frequency, and the combination of both time and frequency. Classification can then be performed utilizing a variety of widely used machine-learning algorithms such as artificial neural networks (ANNs), K-nearest neighbor, and Support-vector machines (SVMs) to distinguish between epileptic and non-epileptic EEG signals. Despite the many epileptic seizure detection techniques, achieving high accuracy, specificity, and sensitivity has always been a challenge. The complexity of most theoretical techniques has resulted in poor practical, real-time implementation. In addition, developing a method of epileptic seizure detection that can be used daily has also been a challenge. The development and implementation of a wearable device for detecting epileptic seizures based on EEG signals would be a valuable contribution to the research community [20].