# A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

arXiv: https://arxiv.org/abs/1803.09861 · HTML: https://mlsanity.com/paper/1803.09861/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Mar 2018.

## Built on by (most cited first, 7 of 7)

- [Identification of Adaptive Driving Style Preference through Implicit Inputs in SAE L2 Vehicles](https://mlsanity.com/paper/2209.10536/) (2022)
- [Beyond Empirical Windowing: An Attention-Based Approach for Trust Prediction in Autonomous Vehicles](https://mlsanity.com/paper/2312.10209/) (2023)
- [Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning](https://mlsanity.com/paper/2409.08980/) (2024)
- [Moral-Trust Violation vs Performance-Trust Violation by a Robot: Which Hurts More?](https://mlsanity.com/paper/2110.04418/) (2021)
- [Trust in Language Grounding: a new AI challenge for human-robot teams](https://mlsanity.com/paper/2209.02066/) (2022)
- [The Response Shift Paradigm to Quantify Human Trust in AI Recommendations](https://mlsanity.com/paper/2202.08979/) (2022)
- [Adaptive XAI in High Stakes Environments: Modeling Swift Trust with Multimodal Feedback in Human AI Teams](https://mlsanity.com/paper/2507.21158/) (2025)

## Abstract (arXiv)

Today, intelligent machines interact and collaborate with humans in a way that demands a greater level of trust between human and machine. A first step towards building intelligent machines that are capable of building and maintaining trust with humans is the design of a sensor that will enable machines to estimate human trust level in real-time. In this paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) measurements. Human subject data collected from 45 participants is used for feature extraction, feature selection, classifier training, and model validation. The first approach considers a general set of psychophysiological features across all participants as the input variables and trains a classifier-based model for each participant, resulting in a trust sensor model based on the general feature set (i.e., a "general trust sensor model"). The second approach considers a customized feature set for each individual and trains a classifier-based model using that feature set, resulting in improved mean accuracy but at the expense of an increase in training time. This work represents the first use of real-time psychophysiological measurements for the development of a human trust sensor. Implications of the work, in the context of trust management algorithm design for intelligent machines, are also discussed.