You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: EEG-Based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review 
Chapter Title:  Input Layer 
1. Abstract of Cited Paper (BIB001):  Feature extraction is a process to extract information from the electroencephalogram (EEG) signal to represent the large dataset before performing classification. This paper is intended to study the use of discrete wavelet transform (DWT) in extracting feature from EEG signal obtained by sensory response from autism children. In this study, DWT is used to decompose a filtered EEG signal into its frequency components and the statistical feature of the DWT coefficient are computed in time domain. The features are used to train a multilayer perceptron (MLP) neural network to classify the signals into three classes of autism severity (mild, moderate and severe). The training results in classification accuracy achieved up to 92.3% with MSE of 0.0362. Testing on the trained neural network shows that all samples used for testing is being classified correctlyARPN Journal of Engineering and Applied Sciences 
2. Abstract of Cited Paper (BIB002):  Autism Spectrum Disorder is a developmental disorder that often impairs a child’s normal development of the brain. Early Diagnosis is crucial in the long term treatment of ASD, but this is challenging due to the lack of a proper objective measures. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. This paper presents EEG and Eye movement data for the diagnosis of ASD using machine learning algorithms. There are number of studies on classification of ASD using EEG or Eye tracking data. However, all of them simply use either Eye movements or EEG data for the classification. In our study we combine Eye movements and EEG data to develop an efficient methodology for diagnosis. This paper presents several models based on EEG, and eye movements for the diagnosis of ASD.

Literature Review:
Hidden Layers Output Layer Output ' ,,e - In order to add non-linearity, activation functions are introduced into the nodes. The weighted sums are fed as parameters for the activation functions. The activation function decides the output of a node. Some of the common activation functions are ReLU (Rectified Linear Unit), sigmoid and linear functions. Given enough amount of training samples, neural networks can classify most of the complex relationships. However, it requires a considerably large amount of training data for learning. Majority of the proposed approaches use a neural network. Some of them are BIB003 BIB001 BIB002 and  . Different algorithms used for classification in the related studies are summarized in Table 4 . As the table illustrates, the neural network has been most frequently used for classification. Next to neural networks, SVM is the most common algorithm. Compared to other techniques discriminant analysis, sequential minimal optimization and k-contractive map have been seldom used. However, we cannot define one algorithm as the best since it depends on several factors. ASD classification being a medical application, interpretability of the decision is important. Algorithms such as decision trees generate classification models with better interpretability. Models generated by algorithms similar to SVM and neural network are black boxes which are difficult to interpret. However, they can model complex relationships unlike simpler methods such as decision trees and Naïve Bayes. Further, if sufficient data is not available neural networks will not produce satisfactory results since it requires a large amount of data to train the model. Similarly, not all algorithms can handle noisy data. It is a standard practice to start with simpler models and if the results are not satisfactory then move on to more complex models to avoid overfitting. If many samples are available choosing neural networks has a high probability for producing more accurate results.