Detecting Autism Spectrum Disorder in Children With Computer Vision - Adapting facial recognition models to detect Autism Spectrum Disorder

Deep neural network trained to detect eye contact from facial image

This project describes the necessary code to implement an EEG-based emotion recognition using SincNet [Ravanelli & Bengio 2018] including data from individuals diagnosed with Autism (ASD). For more details and data request send an email to the authors and contributors Juan Manuel Mayor Torres (juan.mayortorres@unitn.it) and Mirco Ravanelli (Mila)

Code for processing and managing data for EEG-based emotion recognition of individuals with and without Autism. EEG and other clinical data were collected in StonyBrook Social Competence Treatment Lab, for data request evaluation please contact professor Matthew D. Lerner matthew.lerner@stonybrook.edu

Code respository for AutSPACEs: the Autistica/Turing citizen science platform

👾 resources and experiments for autonomous agents (e.g., language models, reinforcement learning, energy based models)

Employs data science to assist therapists treat a common autism symptom that involves interpretation and correlation of facial and speech emotions

🪅Visual Learning aids for Autism spectrum disorder children. Built w/ Azure OpenAI, Azure AI Search, Azure AI services, FastAPI, Next.js

Project for detecting Autism and Dyslexia using ML. A POC for the KPMG Ideation Challenge, 2021.

👾 blockchain infrastructure projects and resources (e.g., ethereum event scanner, on chain analysis, data pipelines, etc.)

Hugging Rain Man: A Novel Facial Action Units Dataset for Analyzing Atypical Facial Expressions in Children with Autism Spectrum Disorder

Code for paper 'Obtaining leaner DNN for decoding brain functional connectome in a single shot'

Video Classification Autism Behaviour

Code implementation of SHRED variants

Human survival post-LGM relied on adaptation, cooperation, and innovation. Neanderthal and Denisovan genes shaped immunity and metabolism, but uniform modern diets clash with these traits, fueling chronic disease. Embracing diversity can drive health and resilience.

This repository contains all of code used to create the Autism Waiting Times publication.

Code of the paper "Altered EEG Variability on Different Time Scales in Participants with Autism Spectrum Disorder - An Exploratory Study" by Hecker, Wilson, Tebartz van Elst & Kornmeier (2022)

A Python based project, which involves prediction autism in children using speech data using MFCC features (Training and Testing Data is Self Collected from NGOs and used with guardian permission)

Deep Learning Autism Classification, using client-server architecture for low resource consumption & easy deployment.

This streamlit app helps you rephrase your language for effectively communicating with kids with PDA.