EEG-Based Autism Detection Using Multi-Input 1D Convolutional Neural Networks
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a complex condition affecting children and characterized by challenges in social interaction, communication, and behavior.Typically, evident before the age of three, ASD severity varies.Diagnosis involves a thorough assessment by a multidisciplinary team using criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), a comprehensive guide for mental health conditions, including ASD.This study focuses on employing deep learning techniques and electroencephalogram (EEG) signals for ASD detection.A unique approach is introduced, utilizing a multi-input 1D Convolutional Neural Network (CNN) framework.EEG signals undergo processing, and data augmentation using sliding windows precedes input into the multi-input 1D CNN model.This model incorporates various layers, including 1D convolutional layers, batch normalization, ReLU activation, and a fully connected layer.Experiments utilize EEG data from King Abdulaziz University Hospital, and the method's effectiveness is evaluated using diverse performance metrics.The experimental work is structured into three sections.The initial experiment focuses on specific EEG channels (FP1, FP2, F7, F3, Fz, F4, and F8), achieving a remarkable accuracy of 99.16%.Expanding the investigation to central and temporal EEG channels (C4, Cz, C3, T5, and Pz) yields an accuracy of 98.32%.In the final experiment involving occipital channels (O1, Oz, O2), an accuracy of 97.65% is achieved.Comparative analyses with existing methods consistently demonstrate the superior performance of our proposed approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".