From 69aa93d6549ebcdadfa0f680d73839b19c2effdf Mon Sep 17 00:00:00 2001 From: Isha Telikicherla Date: Tue, 31 Mar 2026 09:32:49 +0530 Subject: [PATCH] Fix spelling and formatting in NeuroDyad proposal Corrected spelling of 'Google' in the proposal and updated formatting. --- _gsocproposals/2026/proposal_NeuroDyad1.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_gsocproposals/2026/proposal_NeuroDyad1.md b/_gsocproposals/2026/proposal_NeuroDyad1.md index b1c97162..dc2ed30a 100644 --- a/_gsocproposals/2026/proposal_NeuroDyad1.md +++ b/_gsocproposals/2026/proposal_NeuroDyad1.md @@ -11,7 +11,7 @@ organization: When two people talk, their brains synchronize in complex ways. But can we actually decode anything about conversation participants from brain activity? This project uses machine learning on simultaneous brain recordings (hyperscanning EEG) from conversing pairs to rigorously test whether neural patterns generalize across different conversational partners. ## The Challenge -Previous pilot work (Googel Summer of Code 2025, N=8 pairs) achieved 94% accuracy in decoding both conversational roles and participant gender using CEBRA (Contrastive Embedding for Behavioral and Neural Analysis). With our expanded dataset of 40+ pairs, we are looking to build mathematical understanding of which features of neural data inform successful brain-to-brain mapping (explainable AI), and to compare/contrast validation models for topological mapping. +Previous pilot work (Google Summer of Code 2025, N=8 pairs) achieved 94% accuracy in decoding both conversational roles and participant gender using CEBRA (Contrastive Embedding for Behavioral and Neural Analysis). With our expanded dataset of 40+ pairs, we are looking to build mathematical understanding of which features of neural data inform successful brain-to-brain mapping (explainable AI), and to compare/contrast validation models for topological mapping. ## Why This Matters Recent research challenges the "deficit model" of social communication differences. Crompton et al. (2025, Nature Human Behaviour) showed that communication breakdowns depend more on neurotype mismatch than individual deficits; but these studies used only behavioral measures. Your work establishes the neural foundation for understanding how brain-to-brain coordination enables (or impairs) communication, with implications for designing assistive technologies.