Data Sheet 1_Discrete wavelet transform-driven optimized deep learning-based framework for dyslexia detection using EEG signals.csv

PurposeDyslexia is a prevalent neurodevelopmental disorder that impairs a children’s ability to reading, writing, and language processing despite normal cognitive skills. Early identification is vital for timely support and interventions in children with dyslexia. This study aimed to develop an efficient EEG-based pipeline for dyslexia detection using deep learning techniques, while providing a consistent evaluation protocol for fair comparison across models and prior approaches.
MethodsEEG recordings were acquired from 51 participants (26: dyslexic and 25: non-dyslexic), aged 5–10 years, during cognitive task performance. These signals were processed, segmented, and decomposed into standard frequency bands (alpha, beta, delta, and theta) using the discrete wavelet transform to capture discriminative neural patterns. Filter-based feature selection techniques were applied before classification to optimize performance and reduce redundancy to identify the most informative features. These ranked and individual band-wise features were systematically evaluated with classical machine learning baselines (Decision Trees, SVM, k-NN, and ensemble learners) alongside the proposed deep neural networks. In addition, we benchmarked end-to-end raw-EEG deep learning baselines (1D-CNN, LSTM, and EEGNet) and re-implemented representative existing pipelines, all evaluated on our dataset using the same evaluation protocol.
ResultsThe proposed compact deep neural network with four hidden layers achieved the best performance, reaching classification accuracy of 98.85%, outperforming all baseline models, raw-EEG deep learning baselines, and re-implemented approaches.
ConclusionThese findings support the feasibility of DWT-driven EEG analysis combined with deep learning for more accurate and early dyslexia detection. The proposed approach holds promise as a non-invasive screening tool to support improved educational outcomes through early diagnosis and targeted intervention.

阅读障碍（Dyslexia）是一种常见的神经发育障碍，尽管患儿认知能力正常，却会损害其阅读、书写及语言加工能力。早期识别对于患儿获得及时支持与干预至关重要。本研究旨在开发一套结合深度学习技术、基于脑电图（EEG）的高效阅读障碍检测流水线，并建立统一的评估协议以实现不同模型与已有方法间的公平比较。
方法：本研究从51名年龄介于5至10岁的受试者中采集了脑电图记录，其中阅读障碍患儿26名、非阅读障碍对照25名，受试者在完成认知任务时进行数据采集。随后对采集到的脑电信号进行预处理、分段，并通过离散小波变换（discrete wavelet transform）分解为α、β、δ、θ四个标准频带，以提取具有区分性的神经模式。在分类前，采用基于过滤法的特征选择技术以优化模型性能、降低特征冗余，从而筛选出最具信息量的特征。本研究将排序后的特征及各频带专属特征，分别与经典机器学习基线模型（决策树、支持向量机、k近邻及集成学习器）以及本文提出的深度神经网络进行系统评估。此外，我们还构建了端到端原始脑电深度学习基线模型（一维卷积神经网络（1D-CNN）、长短期记忆网络（LSTM）及EEGNet），并复现了代表性的已有流水线，所有模型均基于本数据集采用统一评估协议进行测试。
结果：本文提出的含四层隐藏层的紧凑型深度神经网络取得了最优性能，分类准确率达98.85%，优于所有基线模型、原始脑电深度学习基线模型及复现的已有方法。
结论：本研究结果证实，结合离散小波变换驱动的脑电分析与深度学习技术，可实现更精准的早期阅读障碍检测。所提方法有望作为一种非侵入性筛查工具，通过早期诊断与针对性干预助力改善患儿的教育预后。