Early detection of dyslexia based on EEG with novel predictor extraction and selection

Parmar, Shankar; Paunwala, Chirag

doi:10.1007/s44163-023-00082-4

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…In addition, the proposed model utilized the EEG data to identify dyslexic individuals. Parmar & Paunwala (2023) used a predictor extraction and selection methodology to predict dyslexia using EEG data. The shortcomings of their feature extraction reduced the performance of dyslexia identification.…”

Section: Discussionmentioning

confidence: 99%

“…Traditional dyslexia diagnosis techniques may be insensitive and less specific ( Kheyrkhah Shali & Setarehdan, 2020 ; Parmar & Paunwala, 2023 ; Yan, Zhou & Wong, 2022 ). Standardized testing can ignore specific dyslexia symptoms, resulting in false-negative or positive findings.…”

Section: Introductionmentioning

confidence: 99%

“… Zainuddin et al (2018) proposed a model using a K-nearest neighbor algorithm based on DDM. Similarly, studies ( Guhan Seshadri et al, 2023 ; Parmar & Paunwala, 2023 ; Yan, Zhou & Wong, 2022 ) employed EEG signals for predicting dyslexia.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning-driven dyslexia detection model using multi-modality data

Alkhurayyif,

Wahab Sait

2024

PeerJ Computer Science

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Background Dyslexia is a neurological disorder that affects an individual’s language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs. Methods In this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM. Results The findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning-driven dyslexia detection model using multi-modality data

Alkhurayyif,

Wahab Sait

2024

PeerJ Computer Science

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“…More generally, ML techniques can be used in various critical applications involving decision-making, which include, amongst others, applications in the medical/health sector (see, e.g., [26][27][28]).…”

Section: Automated Decision-making Systems and Relevant Risks For Fun...mentioning

confidence: 99%

Data Protection Issues in Automated Decision-Making Systems Based on Machine Learning: Research Challenges

Christodoulou,

Limniotis

2024

Network

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Data protection issues stemming from the use of machine learning algorithms that are used in automated decision-making systems are discussed in this paper. More precisely, the main challenges in this area are presented, putting emphasis on how important it is to simultaneously ensure the accuracy of the algorithms as well as privacy and personal data protection for the individuals whose data are used for training the corresponding models. In this respect, we also discuss how specific well-known data protection attacks that can be mounted in processes based on such algorithms are associated with a lack of specific legal safeguards; to this end, the General Data Protection Regulation (GDPR) is used as the basis for our evaluation. In relation to these attacks, some important privacy-enhancing techniques in this field are also surveyed. Moreover, focusing explicitly on deep learning algorithms as a type of machine learning algorithm, we further elaborate on one such privacy-enhancing technique, namely, the application of differential privacy to the training dataset. In this respect, we present, through an extensive set of experiments, the main difficulties that occur if one needs to demonstrate that such a privacy-enhancing technique is, indeed, sufficient to mitigate all the risks for the fundamental rights of individuals. More precisely, although we manage—by the proper configuration of several algorithms’ parameters—to achieve accuracy at about 90% for specific privacy thresholds, it becomes evident that even these values for accuracy and privacy may be unacceptable if a deep learning algorithm is to be used for making decisions concerning individuals. The paper concludes with a discussion of the current challenges and future steps, both from a legal as well as from a technical perspective.

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“…They have also explained it by reviewing the clinical observations and research data perspectives in Mather and Schneider (2023) and Wagner et al (2023), evaluated with the help of Bayesian identification model and included set of predictors. In Alqahtani et al (2023), Ahire et al (2023), Jan and Khan (2023), and Parmar and Paunwala (2023), the authors have worked on detection and categorization of dyslexia with a strong focus on implementing ML methods, like deep learning and electroencephalogram data analysis, in the detection and categorization of dyslexia.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

Aldehim,

Rashid,

Alluhaidan

et al. 2024

Journal of Disability Research

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Dyslexia is a complex learning disorder that affects neurological nerves in the brain and makes reading and writing difficult; therefore, early diagnosis for effective interventions becomes important. This study demonstrates how quickly dyslexia can be identified by introducing an advanced convolutional neural network model developed for detecting dyslexia through image-based handwriting analysis. The need for early identification is informed by the fact that dyslexia may, in certain cases, lead to poor academic performance and emotional imbalance among learners. This method of using deep learning outperforms all other established conventional methods due to inherent sensitivity in classifying handwritings of dyslexics from those of normal individuals. The artificial intelligence (AI)-supported technology has the highest training accuracy of 99.5% proving its ability to capture subtle features related to the presence of dyslexic tendencies. Consequently, it records a testing accuracy of 96.4%, thereby confirming its efficacy under practical circumstances. In addition, the model also shows a good F1-score of 96 which indicates that it can achieve a balanced precision versus recall trade-off unlike other state-of-the-art approaches. The obtained results of the proposed methodology were compared with those of previous state–of-the-art approaches, and it has been observed that the proposed study provides better outcomes. These detailed performance indicators point toward the potential usefulness of AI-based methods in identifying dyslexia thus informing appropriate interventions on time and targeted assistance to the patients suffering from this disease.

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Early detection of dyslexia based on EEG with novel predictor extraction and selection

Cited by 7 publications

References 27 publications

Deep learning-driven dyslexia detection model using multi-modality data

Deep learning-driven dyslexia detection model using multi-modality data

Data Protection Issues in Automated Decision-Making Systems Based on Machine Learning: Research Challenges

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

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