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:  Visual Inspection 
1. Abstract of Cited Paper (BIB001):  2 ABSTRACT: Electroencephalogram (EEG) signal is the recording of spontaneous electrical activity of the brain over a small interval of time. Signals are produced by bombardment of neurons within the brains which are measured and evaluated by EEG.EEG signals are lowvoltage signals that are contaminated by various types of noises that are also called as artifacts.As these signals are used to diagnose various types of brain related diseases like narcolepsy,Sleep apneasyndrome,Insomnia and parasomnia it becomes necessary to make these signals free from noise for proper analysis and detection of the diseases. Variousnoise removal techniques are available and can be implemented in mat lab. The methods that are discussed in this paper are: ICA,PCA, WaveltTransform and WaveletPacketTransform. All the above methods can be implemented for EEG signal denoising.Various methods of denoising are studied and considering advantages and disadvantages of all the methods it is concluded that wavelet method of denoising and its enhancement wavelet packet is best. 
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:
Manual noise removal using visual inspection is an easy and reliable approach. However, it is hard to perform when the dataset contains long duration signals from many subjects. Visual inspection was used in BIB003 as a pre-processing step in classifying ASD. Table 2 summarizes different pre-processing techniques used to process the EEG signal. Even though the last two studies are not related to classifying ASD using machine learning algorithms, they have been included to introduce new techniques for noise removal as noise filtering is independent of the application. The "X" symbol indicates techniques used for noise filtering and the "O" symbol indicates other pre-processing techniques used for data transformations. Even though DWT can be used for removing noise, the studies have used it primarily to decompose the signal into different frequency bands. Frequencies outside the range of the frequency bands were filtered using band-pass filters in most of the researches. Band-pass filters are simple and easy to implement. I-FAST and Makoto's pre-processing pipeline combine several techniques for EEG signal preprocessing. Apart from the techniques discussed earlier, adaptive filtering, Fourier transform, source component technique, multivariate regression and empirical mode decomposition have also been used for artefact removal. The source component techniques are a combination of two approaches for artefact removal based on brain electric source analysis and principal component analysis proposed in  BIB001 . The studies done in BIB002   have presented surveys of denoising techniques. BIB002 have discussed denoising techniques based on PCA, ICA, wavelet and wavelet packet in their work. The effectiveness of these techniques was measured based on Mean Squared Error (MSE), signal to noise ratio (SNR) and peak signal to noise ratio (PSNR). High SNR and PSNR values and low MSE values are indicators for less noisy signals. They conclude that the wavelet-based method produces better results based on the MSE, SNR and PSNR values calculated in different studies. Besides the work done in  , has presented ICA and wavelet-based analysis that uses statistical analysis methods and additional artefact removal techniques. Urigüen and Garcia-Zapirain (2015) presented a detailed survey of denoising techniques in their work. Their study explores the noise removal techniques under the following major categories: linear regression methods, EOG correction methods, filtering methods, blind source separation (BSS) methods, source decomposition methods, the combination of different algorithms and other methods. ICA and PCA were categorized under BSS methods with several other techniques. Wavelets were categorized under source decomposition methods. Methods suitable for removing specific artefact types such as ocular artefacts, muscle artefacts, cardiac artefacts and mixed artefacts were also discussed. Their study concludes that the best technique for a given scenario should be chosen considering the type of EEG signal, artefacts that are present and the signal to contaminant ratio. There is no best technique which can be applied to all scenarios.