Device JNEEG to convert Jetson Nano to brain-Computer interfaces. Short report

TL;DR

This paper introduces a device that transforms the Jetson Nano into a brain-computer interface, enabling real-time EEG data processing with embedded machine learning capabilities, reducing reliance on external processing units.

Contribution

The novel shield allows Jetson Nano to directly process EEG signals and run machine learning models on-device, simplifying BCI systems and reducing costs.

Findings

Successful implementation of the shield on Jetson Nano

Real-time EEG data processing demonstrated

Machine learning integration on-device achieved

Abstract

Artificial intelligence has made significant advances in recent years and this has had an impact on the field of neuroscience. As a result, different architectures have been implemented to extract features from EEG signals in real time. However, the use of such architectures requires a lot of computing power. As a result, EEG devices typically act only as transmitters of EEG data, with the actual data processing taking place in a third-party device. That's expensive and not compact. In this paper, we present a shield that allows a single-board computer, the Jetson Nano from Nvidia, to be converted into a brain-computer interface and, most importantly, the Jetson Nano's capabilities allow machine learning tools to be used directly on the data collection device. Here we present the test results of the developed device. https://github.com/HackerBCI/EEG-with-JetsonNano