Brain2Object: Printing Your Mind from Brain Signals with Spatial Correlation Embedding

TL;DR

This paper introduces Brain2Object, an end-to-end system that decodes EEG signals to print 3D objects corresponding to what a person observes, integrating advanced neural decoding and digital fabrication.

Contribution

It presents a novel unified framework combining spatial correlation embedding and neural networks for accurate brain signal decoding to enable object printing from EEG data.

Findings

Achieved 92.58% recognition accuracy on benchmark data

Achieved 75.23% accuracy on local dataset

Outperformed existing baseline and state-of-the-art methods

Abstract

Electroencephalography (EEG) signals are known to manifest differential patterns when individuals visually concentrate on different objects. In this work, we present an end-to-end digital fabrication system, Brain2Object, to print the 3D object that an individual is observing by decoding visually-evoked brain signals. We propose a unified training framework that combines multi-class Common Spatial Pattern and Convolutional Neural Networks to support the backend computation. We learn the dynamical graph representations of brain signals to accurately capture the structural information among EEG channels. A user-friendly interface is developed as the system front end. Brain2Object presents a streamlined end-to-end workflow that can serve as a template for deeper integration of BCI technologies to assist with our routine activities. The proposed system is evaluated extensively using offline experiments and through an online demonstrator. The experimental results show that our approach can achieve the recognition accuracy of 92.58% on a benchmark dataset and 75.23% on a locally collected dataset. Moreover, our method consistently outperforms a wide range of baseline and state-of-the-art approaches. The proof-of-concept corroborates the practicality of our approach and illustrates the ease with which such a system could be deployed.