Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by restricted, repetitive behaviors and impaired social interaction.Currently, the identification of individuals with ASD largely relies on subjective assessments, presenting a challenge for researchers to distinguish between Typically Developing (TD) children and those with ASD.This study analyzes EEG data from 10 children with ASD and 10 TD children in response to an audio-video stimulus.Two separate analyses were performed on EEG frequency bands within the range of 0-70 Hz and specific frequency bands of 8-30 Hz, aiming to identify the brain lobe region that yields the most significant discrimination between ASD and TD.Parameters such as Linear Frequency Cepstral Coefficients (LFCC), Cepstral energy, signal energy, delta, and delta-delta derivatives were utilized for the analysis.The study deployed classification techniques including K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Decision Tree, Support Vector Machine (SVM), Bagging KNN, and Random Forest (RF).The results indicated that KNN surpassed all other classification models for frequency bands within the range of 0-70Hz, achieving a discriminating accuracy of 98.3% for ASD and TD in the central lobe region (C3, C4, Cz).However, KNN did not yield a significant level of accuracy when applied to a specific frequency band; it was improved by employing Bagging KNN, reaching 93.8% in the central lobe region (C3, C4, Cz).The electrode combination in the central lobe (C3, C4, and Cz) demonstrated superior discrimination between TD and ASD compared to other brain lobes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".