Autor: |
Eric J. Earley, Nathaly Sanchez Chan, Autumn A. Naber, Enzo Mastinu, Minh Tat Nhat Truong, Max Ortiz-Catalan |
Jazyk: |
angličtina |
Rok vydání: |
2024 |
Předmět: |
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Zdroj: |
IEEE Access, Vol 12, Pp 69350-69358 (2024) |
Druh dokumentu: |
article |
ISSN: |
2169-3536 |
DOI: |
10.1109/ACCESS.2024.3397909 |
Popis: |
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 non-disabled 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. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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