# Deep Learning Applications for Dyslexia Prediction
## 4 Research Results
### Data Acquisition  
The first step in predicting dyslexia disorders using DL methods is the acquisition of datasets related to dyslexia. As mentioned above, brain imaging and eye-tracking data in addition to traditional data (e.g., test scores and time consumption when performing specific tasks) can be used to diagnose dyslexia. In [19], the dyslexia datasets were divided into three classes. The first category was related to behavioral symptoms in dyslexics, the second category was related to brain imaging, and the last was related to eye movements during cognitive tasks. The behavioral symptoms of dyslexics manifest through reading, writing, working memory, phonological awareness, and facial reactions throughout the learning process. Reading and writing are inextricably linked, and children who struggle to read also struggle to write. Hence, dyslexics have problems when writing letters. For example, instead of starting from the top, they start from the bottom when writing letters, inverting the letters, skewing some, and erasing frequently [24]. [PERSON] et al. [25] were the first to use handwriting datasets to predict dyslexia. They compiled a handwritten dataset on 88 subjects (11 from dyslexics and 77 from normal subjects), demonstrating the possibility of using these types of data for predicting dyslexia. In another study, 100 handwriting samples were collected (22 dyslexia and 78 non-dyslexia) from children in grades K-6 [26]. [PERSON] et al. [27] collected 30 handwriting samples from the ADM. The dataset included eight selected numbers and small letters (6, 7, 2, 5, c, b, p, and f). The main reason for selecting these numbers and letters is that they are the most commonly associated with writing errors among dyslexics. In India, [PERSON] and [PERSON] collected 54 handwriting samples from the notebooks of children (18 females, 36 males) from first to fifth grade [28]. Fourteen Hindi words were selected from the children's notebooks, representing varying degrees of difficulty. Five words consisted of two letters, six words had vowel signs, and three were conjoined consonants words. [PERSON] et al. [4; 29] used a dataset collected from three sources: uppercase letters were taken from NIST Special Database 19, lowercase letters were taken from the Kaggle dataset, and testing data were collected on Seberang Jaya Primary School's dyslexic students. These two studies used data augmentation techniques to generate the dyslexic datasets, using noise injection and rotation techniques to generate reversal handwriting datasets. [PERSON] and [PERSON] [30] collected a dataset from the test scores of dyslexic students on reading, spelling, speech deficits, deficits in writing, and mathematical abilities as well as motor skills. The second category of dyslexia datasets is related to brain imaging, where the modalities of brain imaging identify special behaviors and activations of the brains of participants in the studies [19]. EEG is a brain imaging modality that can be utilized to observe the brain's function through electrodes placed on the scalp. In the studies, the researchers examined \"neurological\" aspects to identify unique patterns in dyslexia [31]. For example, [PERSON] et al. [32] collected EEG signal data from six participants between four and seven years of age (three dyslexic and three non-dyslexic). The normal children were selected randomly from various schools, while the dyslexic children were from the Titiwangsa and Ampang Hilir branches of the Dyslexia Association of Malaysia. In the KSA, [PERSON] and [PERSON] [33] compiled a dataset on 80 children between 7 and 13 years of age with the help of the \"Brain training and consultation center\". Normal children were randomly selected from the cities of Jeddah and Makah. In another study [34], a dataset was compiled on 32 children who were native Hebrew speakers in grades 6-7 (17 dyslexics and 15 skilled readers). In Spain, [PERSON] et al. [35] collected a dataset on 48 native Spanish-speaking participants (32 skilled readers and 16 dyslexics).