## 4 Applications on Electroencephalogram (EEG) Prediction
### Chaotic EEG Time Series Prediction with the ANN-ALO System  
The electroencephalogram (EEG) is a typical monitoring approach for electrical activities that occur in our brains. Since the brain is a complex component of the body that is associated with many vital functions, the electrical activity in it can be observed in chaotic forms. Due to this, a general analysis of brain activity requires the employment of advanced approaches to understand the chaotic flows in time series that are derived from electrical activities in them. It is possible to consider this to be a vital issue because of the following key points: In terms of the healthcare and medical perspective, the early diagnosis of some diseases (like depression, autism, epilepsy, and even Alzheimer's disease) related to the brain is very important. Additionally, the evaluation of interactions of the brain with  
Figure 3: Brief scheme of the artificial neural network (ANN) and ant-lion optimizer (ALO) hybrid system (ANN–ALO) for chaotic electroencephalogram (EEG) prediction. other environmental factors (e.g., triggering factors around living organisms, psychological aspects, living standards, and health state affected by problems in other organs) are also critical. Considering the mentioned key points, predicting the future of such data flow is also a remarkable research approach and is followed in this study. In order to perform predictions over chaotic EEG time series, data were recorded from individuals coming to the Isparta State Hospital located in the city center of Isparta, Turkey. At this point, a total of 10 EEG data gathered from 10 (five males and five females) individuals (from whom necessary ethical permission was obtained) were considered for the prediction approach. In this context, a total of 10 different prediction applications were determined to achieve the prediction approach with the ANN-ALO hybrid system and also to evaluate it. A typical EEG recording is done by locating electrodes on certain points over the individual's head (Figure 4a [102]). At this point, an internationally used 10-20 system is followed to place the electrodes on points from which it is possible to gather the desired electrical data easily (Figure 4b [103]). In this study, the sampling frequency for the EEG recording was 250 Hz, and in order to make the related data ready for the prediction tasks, it was preprocessed against noises, eye blinks, and muscular artifacts. For the noise artifact removal, FastICA, which is an automated, simple technique introduced by [PERSON] et al. [104] (which benefits generally from wavelet transform) was used in this study. Each chaotic EEG time series is called as 'Application' and named with the letter followed by application number (A1,..., A10). Figure 5 briefly presents the chaotic EEG time series considered in the sense of this research study. Figure 4: (a) Electroencephalogram (EEG) recording approach [102]; (b) placement of the electrodes according to the 10–20 system while recording EEG [103] (the figures were sourced from a public domain). Figure 5: The chaotic electroencephalogram (EEG) time series that was included in the study for prediction applications. Since it is important to observe chaotic behaviors within each EEG time series (for the scope of the research), it is possible to evaluate their phase-space state portraits. In this context, Figure 6 represents the phase-space state portrait for each chaotic EEG time series within the related applications. It can be seen from Figure 6 that for each piece of EEG data, the phase-spaces are not periodic and do not extend to infinity, which is a sign of chaos. In order to validate that the considered EEG time series are chaotic, a formal approach--calculation of the largest Lyapunov exponent--was used for each EEG time series.