NeuralCLIP: A Modular FPGA-Based Neural Interface for Closed-Loop Operation

Ranganathan, Vaishnavi; Nakahara, Jared; Samejima, Soshi; Tolley, Nicholas; Khorasani, Abed; Moritz, Chet T.; Smith, Joshua R.

doi:10.1109/ner.2019.8717135

Cited by 4 publications

(10 citation statements)

References 8 publications

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“…The miniaturized BCSI system was tested offline using a modular field-programmable gate array (FPGA)-based device, modified from our Neural Closed-Loop Implantable Platform (NeuralCLIP) [30]. This miniaturized electronic device could record cortical signals, process multi-channel LFP data, and decode movement intention to trigger stimulation.…”

Section: Miniaturized Bcsi Systemmentioning

confidence: 99%

“…Finally, we evaluated the performance of the autonomous decoding system on the NeuralCLIP. The NeuralCLIP was configured with signal processing blocks to implement our CCA decoder on an FPGA architecture [30]. The complete system was implemented on a PCB small enough to be implanted under the skin of a rodent.…”

Section: E Autonomous Bcsi Miniaturized Devicementioning

confidence: 99%

“…We constructed a complete brain to spinal cord interface capable of decoding LFPs using a computationally efficient algorithm in order to control epidural stimulation. We tested whether the entire LFP decoding system can be implemented in a miniaturized implantable device that utilizes a field-programmable gate array (FPGA) to perform onboard processing [30]. Finally, we explored the feasibility of implementing the decoder to trigger epidural stimulation using the customized processing pipeline on a miniaturized device.…”

Section: Introductionmentioning

confidence: 99%

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Brain-Computer-Spinal Interface Restores Upper Limb Function After Spinal Cord Injury

Samejima

Khorasani

Ranganathan

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain to control movement-evoking stimulation. Common decoding methods use spike-sorting and require frequent calibration and high computational complexity. Furthermore, most applications of closed-loop stimulation act on peripheral nerves or muscles, resulting in rapid muscle fatigue.Here we show that a local field potential-based BCI can control spinal stimulation and improve forelimb function in rats with cervical SCI. We decoded forelimb movement via multi-channel local field potentials in the sensorimotor cortex using a canonical correlation analysis algorithm. We then used this decoded signal to trigger epidural spinal stimulation and restore forelimb movement. Finally, we implemented this closed-loop algorithm in a miniaturized onboard computing platform. This Brain-Computer-Spinal Interface (BCSI) utilized recording and stimulation approaches already used in separate human applications. Our goal was to demonstrate a potential neuroprosthetic intervention to improve function after upper extremity paralysis.

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Section: Miniaturized Bcsi Systemmentioning

confidence: 99%

Section: E Autonomous Bcsi Miniaturized Devicementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Brain-Computer-Spinal Interface Restores Upper Limb Function After Spinal Cord Injury

Samejima

Khorasani

Ranganathan

et al. 2021

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Implantable biomedical devices are certainly one of the hottest application areas of RFID because of the great potential that wireless power and data communication capabilities, inherent in RFID, bring to this field. Hence, the RFID physical layer is ideal for applications such as neural recording, where implanted sensors do not require any source of energy of their own except for an external RF field [33,34]. An overview of the main areas of application of RFID sensing are shown in Table 1.…”

Section: Applicationsmentioning

confidence: 99%

“…An overview of the main areas of application of RFID sensing are shown in Table 1. [18][19][20] sensor innovations smart irrigation smart tractors monitoring key parameters [21,22] smart roads environment-connected vehicles people-centric integrated transportation [23][24][25] Retail Industry Neuro-Engineering security process automation real-time stock control product quality control [26,27] smart maintenance environment-aware objects real-time monitoring real-time monitoring of the platforms [30][31][32] biomedical device communications low-power communications backscatter communications [33,34] Figure 2. Communications scheme using a sensing RFID neural implant [33,34].…”

Section: Applicationsmentioning

confidence: 99%

A Review of IoT Sensing Applications and Challenges Using RFID and Wireless Sensor Networks

Landaluce

Arjona

Perallos

et al. 2020

Sensors

253

124

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Radio frequency identification (RFID) and wireless sensors networks (WSNs) are two fundamental pillars that enable the Internet of Things (IoT). RFID systems are able to identify and track devices, whilst WSNs cooperate to gather and provide information from interconnected sensors. This involves challenges, for example, in transforming RFID systems with identification capabilities into sensing and computational platforms, as well as considering them as architectures of wirelessly connected sensing tags. This, together with the latest advances in WSNs and with the integration of both technologies, has resulted in the opportunity to develop novel IoT applications. This paper presents a review of these two technologies and the obstacles and challenges that need to be overcome. Some of these challenges are the efficiency of the energy harvesting, communication interference, fault tolerance, higher capacities to handling data processing, cost feasibility, and an appropriate integration of these factors. Additionally, two emerging trends in IoT are reviewed: the combination of RFID and WSNs in order to exploit their advantages and complement their limitations, and wearable sensors, which enable new promising IoT applications.

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