Machine learning and deep learning applied to EEG and fNIRS for early autism spectrum disorder diagnosis: a systematic review
The results demonstrated the potential of DL and ML algorithms applied to EEG and fNIRS signals for early ASD assessment, supporting the development of personalized intervention strategies grounded in robust neurophysiological evidence.
Authors
- Ilaria Pizzolorusso
- Arcangelo Merla (ORCID: https://orcid.org/0000-0003-1111-6255)
- Roberta Palmieri
- David Perpetuini (ORCID: https://orcid.org/0000-0003-1903-0501)
- Daniela Cardone (ORCID: https://orcid.org/0000-0002-1506-1995)
- Emanuele Francesco Russo (ORCID: https://orcid.org/0009-0005-2958-602X)
- Michele Giuseppe Di Cesare
- Sara Quattrocelli
- Roberta Brandi
- Francesca Magistro
- Andrea De Giacomo
Institutions
- Local Government Association (GB)
- University of Chieti-Pescara (IT)
- PADI Foundation (US)
Publication Details
- Journal
- Frontiers in Psychiatry
- Published
- 2026-02-03
- DOI
- https://doi.org/10.3389/fpsyt.2026.1668914
- Primary Topic
- Autism Spectrum Disorder Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00