← Back to all news
arXiv Machine Learning By Hubert Huang, Michelle McCleod, Brendan Ames, Evie Malaia

Contrastive Neural Embeddings Reveal Individual Traits Beyond Conversational Role

Read the original on arXiv Machine Learning →

The study applies contrastive representation learning (CEBRA) to EEG data collected from conversational dyads, embedding the recordings onto a 2‑dimensional sphere. The resulting manifold captures individual identity features, as evidenced by high decoding accuracy for dyad labels and significant differences when labels are permuted only after retraining the encoder. However, the embedding shows little sensitivity to conversational role (speaker vs. listener) and to autism‑quotient differences, suggesting that the geometry is dominated by individual traits rather than task‑specific dynamics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Aug 21

Decoding silent reading from non-invasive EEG

arXiv:2608. 20186v1 Announce Type: new Abstract: Non-invasive decoding of inner speech faces a fundamental data problem: a corpus pairing brain activity with a person's spontaneous inner monologue cannot be collected, and the available proxy paradigms (cued repetitive and retrospectively reported generative inner speech) are slow to acquire, poorly time-locked, and subject compliance is unverifiable.

By Ingo Marquardt, Anthilia Alchanat, Priyanka Jain
llmsrag
More like this →
arXiv Machine Learning
Oct 16

Cross-subject decoding of imagined speech from scalp EEG: a leakage-controlled evaluation on three public corpora

The study evaluated whether imagined speech decoded from scalp EEG can transfer across individuals using three public datasets. Across 482 participant pairs, cross‑subject decoding accuracy was essentially at chance, while within‑subject decoding only surpassed chance in one dataset. The results suggest that imagined‑speech content does not generalize between people and that larger single‑condition datasets are more beneficial than further model development.

By Blessing Isoyiza Adeika, Onyema Osuagwu
safety
More like this →