Low-Cost, Wireless Bioelectric Signal Acquisition and Classification Platform
Journal article, 2024

Bioelectric signal classification is a flourishing area of biomedical research, however conducting this research in a clinical setting can be difficult to achieve. The lack of inexpensive acquisition hardware can limit researchers from collecting and working with real-time data. Furthermore, hardware requiring direct connection to a computer can impose restrictions on typically mobile clinical settings for data collection. Here, we present an open-source ADS1299-based bioelectric signal acquisition system with wireless capability suitable for mobile data collection in clinical settings. This system is based on the ADS_BP and BioPatRec, both open-source bioelectric signal acquisition hardware and MATLAB-based pattern recognition software, respectively. We provide 3D-printable housing enabling the hardware to be worn by users during experiments and demonstrate the suitability of this platform for real-time signal acquisition and classification. In conjunction, these developments provide a unified hardware-software platform for a cost of around $150 USD. This device can enable researchers and clinicians to record bioelectric signals from able-bodied or motor-impaired individuals in laboratory or clinical settings, and to perform offline or real-time intent classification for the control of robotic and virtual devices.

Electrodes

Electroencephalography

open source

Performance evaluation

Microprogramming

EMG

Software

bioelectric signal

pattern recognition

Hardware

Electrocardiography

data acquisition

Author

Eric J. Earley

Center for Bionics and Pain Research

Nathaly Sanchez Chan

Center for Bionics and Pain Research

Autumn Naber

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Enzo Mastinu

Center for Bionics and Pain Research

Minh T.N. Truong

Center for Bionics and Pain Research

Max Jair Ortiz Catalan

Center for Bionics and Pain Research

IEEE Access

2169-3536 (ISSN) 21693536 (eISSN)

Vol. 12 69350-69358

Subject Categories

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1109/ACCESS.2024.3397909

More information

Latest update

6/8/2024 4