## 5 Popular Datasets for ASD Detection or Classification
** The P300 speller KAU dataset is an open-source dataset by King AbdulAziz University KSA, and the dataset consists of two categories: one is the global dataset for autism disorder, and the global P300 speller dataset consists of EEG samples of individuals with ASD and people without neurological disorders. Ten boys, ages 9 to 16, who are not diagnosed with any neurological impairments make up the control group; eight boys, ages 10 to 16, represent the ASD group. The BCIAUT_P30 dataset was launched in 2020 and is freely available on Kaggle; comprising of EEG recordings developed for the Brain-Computer Interface (BCI) system based on the P300, it contains 105 individual recordings of data from 15 people diagnosed with ASD, collected throughout seven sessions. The main limitation of EEG datasets is the lack of data diversity and demographic imbalances [146] since they contain very few EEG recording samples, only from the male gender. Moreover, the EEG samples belong to a particular age group, causing bias based only on inclination to that age group. Lastly, the EEG samples are pretty challenging to extract. They are prone to making noise during recording due to interactions with other electromagnetic waves, adding distortion, which requires advanced preprocessing techniques to remove and enhance data quality [147; 148; 149]. Overall, these problems can reduce a model's performance and generalization to real-world settings. **Genetics datasets. ** The Autism WGS is a collection of genomic data where 32 ASD-affected families underwent whole-genome sequencing (WGS), revealing potentially dangerous genetic abnormalities in 31% of the families and 19% of the probands. These included new and recognized susceptible genes, such as CAPRIN1, VIP, SCN2A, and numerous others connected to disorders, including fragile X syndrome and epilepsy with de novo and hereditary mutations. Due to the rarity of certain phenotypic expressions, the dataset lacks comprehensive representation, limiting insights into specific factors contributing to ASD. Furthermore, the absence of fully developed tools for analyzing structural variants related to ASD hinders the dataset from achieving its full potential in research applications [150; 151]. **Facial images dataset. ** The Autistic Children Facial Dataset is an open-source dataset publicly available on Kaggle, consisting of 2936 facial images of normal and ASD-diagnosed children. The facial images are taken from various human races around the world. The limitations of this dataset are that the images come from various sources, so the image quality differs a lot in size, and it consists of bad image quality and noise. The major limitation is that facial images are not the best medium to detect or classify ASD based on facial images; it can be interpreted that children labeled having ASD have very normal facial features, and the children labeled normal look like having ASD. It is not easy to achieve high accuracy by training CNN-based models due to quite a similarity among facial features. In addition, the number of samples is limited as well and requires to be increased for more effective and efficient results [56]. Lastly, this dataset lacks demographic diversity and presents racial facial feature disparity and gender imbalance, which decreases generalizability and model performance. To overcome these issues, some researchers suggest building and training race-specific models to eliminate bias from other race factors on the reliability and accuracyof the models and recommending balanced demographic representation within datasets to reduce the misclassification caused by facial anthropometric disparities or differences in facial structures in ASD detection using facial images [79].