Enabling Model-Based Design for Real-Time Spike Detection
<italic>Goal</italic>: This study addresses the inherent difficulties in the creation of neuroengineering devices for real-time neural signal processing, a task typically characterized by intricate and technically demanding processes. Beneath the substantial hardware advancements in neur...
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/10870096/ |
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| author | Mattia Di Florio Yannick Bornat Marta Care Vinicius Rosa Cota Stefano Buccelli Michela Chiappalone |
| author_facet | Mattia Di Florio Yannick Bornat Marta Care Vinicius Rosa Cota Stefano Buccelli Michela Chiappalone |
| author_sort | Mattia Di Florio |
| collection | DOAJ |
| description | <italic>Goal</italic>: This study addresses the inherent difficulties in the creation of neuroengineering devices for real-time neural signal processing, a task typically characterized by intricate and technically demanding processes. Beneath the substantial hardware advancements in neurotechnology, there is often rather complex low-level code that poses challenges in terms of development, documentation, and long-term maintenance. <italic>Methods</italic>: We adopted an alternative strategy centered on Model-Based Design (MBD) to simplify the creation of neuroengineering systems and reduce the entry barriers. MBD offers distinct advantages by streamlining the design workflow, from modelling to implementation, thus facilitating the development of intricate systems. A spike detection algorithm has been implemented on a commercially available system based on a Field-Programmable Gate Array (FPGA) that combines neural probe electronics with configurable integrated circuit. The entire process of data handling and data processing was performed within the Simulink environment, with subsequent generation of hardware description language (HDL) code tailored to the FPGA hardware. <italic>Results</italic>: The validation was conducted through in vivo experiments involving six animals and demonstrated the capability of our MBD-based real time processing (latency <= 100.37 µs) to achieve the same performances of offline spike detection. <italic>Conclusions</italic>: This methodology can have a significant impact in the development of neuroengineering systems by speeding up the prototyping of various system architectures. We have made all project code files open source, thereby providing free access to fellow scientists interested in the development of neuroengineering systems. |
| format | Article |
| id | doaj-art-cf365b28a4cd48e6979eede1262981ce |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-cf365b28a4cd48e6979eede1262981ce2025-08-20T03:32:57ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762025-01-01631231910.1109/OJEMB.2025.353776810870096Enabling Model-Based Design for Real-Time Spike DetectionMattia Di Florio0https://orcid.org/0000-0002-5263-1133Yannick Bornat1Marta Care2Vinicius Rosa Cota3https://orcid.org/0000-0002-2338-5949Stefano Buccelli4Michela Chiappalone5https://orcid.org/0000-0003-1427-5147Department of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Genova, ItalyLaboratoire de l'Intégration du Matériau au Système (IMS), University of Bordeaux, Bordeaux INP, CNRS UMR 5218, Talence Cedex, FranceIRCCS Ospedale Policlinico San Martino, Genova, ItalyRehab Technologies Lab, Istituto Italiano di Tecnologia, Genova, ItalyRehab Technologies Lab, Istituto Italiano di Tecnologia, Genova, ItalyDepartment of Informatics, Bioengineering, Robotics, System Engineering (DIBRIS), University of Genova, Genova, Italy<italic>Goal</italic>: This study addresses the inherent difficulties in the creation of neuroengineering devices for real-time neural signal processing, a task typically characterized by intricate and technically demanding processes. Beneath the substantial hardware advancements in neurotechnology, there is often rather complex low-level code that poses challenges in terms of development, documentation, and long-term maintenance. <italic>Methods</italic>: We adopted an alternative strategy centered on Model-Based Design (MBD) to simplify the creation of neuroengineering systems and reduce the entry barriers. MBD offers distinct advantages by streamlining the design workflow, from modelling to implementation, thus facilitating the development of intricate systems. A spike detection algorithm has been implemented on a commercially available system based on a Field-Programmable Gate Array (FPGA) that combines neural probe electronics with configurable integrated circuit. The entire process of data handling and data processing was performed within the Simulink environment, with subsequent generation of hardware description language (HDL) code tailored to the FPGA hardware. <italic>Results</italic>: The validation was conducted through in vivo experiments involving six animals and demonstrated the capability of our MBD-based real time processing (latency <= 100.37 µs) to achieve the same performances of offline spike detection. <italic>Conclusions</italic>: This methodology can have a significant impact in the development of neuroengineering systems by speeding up the prototyping of various system architectures. We have made all project code files open source, thereby providing free access to fellow scientists interested in the development of neuroengineering systems.https://ieeexplore.ieee.org/document/10870096/Signal processingin vivo experimentsHDL coderField-Programmable Gate Array (FPGA)neuroengineering |
| spellingShingle | Mattia Di Florio Yannick Bornat Marta Care Vinicius Rosa Cota Stefano Buccelli Michela Chiappalone Enabling Model-Based Design for Real-Time Spike Detection IEEE Open Journal of Engineering in Medicine and Biology Signal processing in vivo experiments HDL coder Field-Programmable Gate Array (FPGA) neuroengineering |
| title | Enabling Model-Based Design for Real-Time Spike Detection |
| title_full | Enabling Model-Based Design for Real-Time Spike Detection |
| title_fullStr | Enabling Model-Based Design for Real-Time Spike Detection |
| title_full_unstemmed | Enabling Model-Based Design for Real-Time Spike Detection |
| title_short | Enabling Model-Based Design for Real-Time Spike Detection |
| title_sort | enabling model based design for real time spike detection |
| topic | Signal processing in vivo experiments HDL coder Field-Programmable Gate Array (FPGA) neuroengineering |
| url | https://ieeexplore.ieee.org/document/10870096/ |
| work_keys_str_mv | AT mattiadiflorio enablingmodelbaseddesignforrealtimespikedetection AT yannickbornat enablingmodelbaseddesignforrealtimespikedetection AT martacare enablingmodelbaseddesignforrealtimespikedetection AT viniciusrosacota enablingmodelbaseddesignforrealtimespikedetection AT stefanobuccelli enablingmodelbaseddesignforrealtimespikedetection AT michelachiappalone enablingmodelbaseddesignforrealtimespikedetection |