# Mental State Recognition via Wearable EEG

arXiv: https://arxiv.org/abs/1602.00985 · HTML: https://mlsanity.com/paper/1602.00985/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2016.

## Built on by (most cited first, 8 of 8)

- [Time Majority Voting, a PC-based EEG Classifier for Non-expert Users](https://mlsanity.com/paper/2207.12662/) (2022)
- [A Deep Learning Based Ternary Task Classification System Using Gramian Angular Summation Field in fNIRS Neuroimaging Data](https://mlsanity.com/paper/2101.05891/) (2021)
- [Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift](https://mlsanity.com/paper/2208.00465/) (2022)
- [Too Fine or Too Coarse? The Goldilocks Composition of Data Complexity for Robust Left-Right Eye-Tracking Classifiers](https://mlsanity.com/paper/2209.03761/) (2022)
- [Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning](https://mlsanity.com/paper/2209.10992/) (2022)
- [EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis](https://mlsanity.com/paper/2208.11743/) (2022)
- [Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns](https://mlsanity.com/paper/2401.03250/) (2024)
- [Review of algorithms for predicting fatigue using EEG](https://mlsanity.com/paper/2402.09443/) (2024)

## Abstract (arXiv)

The increasing quality and affordability of consumer electroencephalogram (EEG) headsets make them attractive for situations where medical grade devices are impractical. Predicting and tracking cognitive states is possible for tasks that were previously not conducive to EEG monitoring. For instance, monitoring operators for states inappropriate to the task (e.g. drowsy drivers), tracking mental health (e.g. anxiety) and productivity (e.g. tiredness) are among possible applications for the technology. Consumer grade EEG headsets are affordable and relatively easy to use, but they lack the resolution and quality of signal that can be achieved using medical grade EEG devices. Thus, the key questions remain: to what extent are wearable EEG devices capable of mental state recognition, and what kind of mental states can be accurately recognized with these devices? In this work, we examined responses to two different types of input: instructional (logical) versus recreational (emotional) videos, using a range of machine-learning methods. We tried SVMs, sparse logistic regression, and Deep Belief Networks, to discriminate between the states of mind induced by different types of video input, that can be roughly labeled as logical vs. emotional. Our results demonstrate a significant potential of wearable EEG devices in differentiating cognitive states between situations with large contextual but subtle apparent differences.