Work

MEGnifying Emotion: Sentiment Analysis from Annotated Brain Data

Year: 2026

Type: preprint

Abstract: Decoding emotion from brain activity could unlock a deeper understanding of the human experience. While a number of existing datasets align brain data with speech and with speech transcripts, no datasets have annotated brain data with sentiment. To bridge this gap, we explore the use of pre-trained Text-to-Sentiment models to annotate non invasive brain recordings, acquired using magnetoencephalography (MEG), while participants listened to audiobooks. Having annotated the text, we employ force-alignment of the text and audio to align our sentiment labels with the brain recordings. It is straightforward then to train Brainto-Sentiment models on these data. Experimental results show an improvement in balanced accuracy for Brain-to-Sentiment compared to baseline, supporting the proposed approach as a proof-of-concept for leveraging existing MEG datasets and learning to decode sentiment directly from the brain.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2601.18792

Authors: Liu, Brian, Jones, Oiwi Parker

Institutions: Bowdoin College, University of Oxford, Precision Polyolefins (United States)

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funders: National Science Foundation, University of Oxford, Social Sciences and Humanities Research Council of Canada, Medical Research Council

Awards: Brain Commands and Beyond: Decoding Inner Speech for Neural Prosthetics, Hermann Hesse and Pietism

OpenAlex ID: W7125815051

Cites:

Cited by:

Related to: