# Hierarchical Graphical Models for Context-Aware Hybrid Brain-Machine Interfaces

arXiv: https://arxiv.org/abs/1809.05635 · HTML: https://mlsanity.com/paper/1809.05635/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders](https://mlsanity.com/paper/2207.08002/) (2022)
- [HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Hands](https://mlsanity.com/paper/2103.04845/) (2021)
- [From Hand-Perspective Visual Information to Grasp Type Probabilities: Deep Learning via Ranking Labels](https://mlsanity.com/paper/2103.04863/) (2021)

## Abstract (arXiv)

We present a novel hierarchical graphical model based context-aware hybrid brain-machine interface (hBMI) using probabilistic fusion of electroencephalographic (EEG) and electromyographic (EMG) activities. Based on experimental data collected during stationary executions and subsequent imageries of five different hand gestures with both limbs, we demonstrate feasibility of the proposed hBMI system through within session and online across sessions classification analyses. Furthermore, we investigate the context-aware extent of the model by a simulated probabilistic approach and highlight potential implications of our work in the field of neurophysiologically-driven robotic hand prosthetics.