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A high level schematic of proposed low power FlashFPGA based micro-system of closed-loop PID Control operation for delivering further optogenetic stimulus.
Source publication
In this paper, we demonstrate that a low power flash FPGA based micro-system can provide a low power programmable interface for closed-loop brain implant inter- faces. The proposed micro-system receives recording local field potential (LFP) signals from an implanted probe, performs closed-loop control using a first order control system, then conver...
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... optogenetic control of epileptic brain activity. Our brain probe unit consists of an intelligent probe with on-board micro-LEDs, recording electrodes, and signal acquisition circuitry. The power consumption of this probe is 1mW at a 10% optical duty cycle. As such, a desirable power consumption for the control unit is to be in the same range. Fig. 1 describes exploratory flash FPGA-based closed-loop brain implant interface. We have implemented our control unit into a 50m x 25mm form factor. And it will be miniaturized further for use in small rodent research. ...
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... to the original signal. The proportion filter kernel is shown in Fig. 2 (b1), the fast Fourier transform (FFT) of proportion kernel is displayed in Fig. 2 (b2). In terms of integral filter design, if an x(t) signal is convolved with integral δ(t), output signal will be a time integral of the original signal. The integral filter kernel is shown in Fig. 2 (c1), the FFT of integral kernel is displayed in Fig. 2 (c2). Finally, If an x(t) signal is convolved with differentiated δ(t), output signal will be a time derivative of original signal. The derivative filter kernel is shown in Fig. 2 (d1), and FFT of integral kernel is displayed in Fig. 2 (d2). The proportional, integral, and derivative ...
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... δ(t), output signal will be a time integral of the original signal. The integral filter kernel is shown in Fig. 2 (c1), the FFT of integral kernel is displayed in Fig. 2 (c2). Finally, If an x(t) signal is convolved with differentiated δ(t), output signal will be a time derivative of original signal. The derivative filter kernel is shown in Fig. 2 (d1), and FFT of integral kernel is displayed in Fig. 2 (d2). The proportional, integral, and derivative kernel demonstrated in Fig. 2 (b), (c) and (d) will be stored as lookup tables in the flash FPGA hardware implementation. Physically, they are stored as three 100*1 column buffer banks consisting of an 8-bit column registers implementing ...
Citations
... They are particularly suitable for placement within static tissues like internal organs or intracranial tissues. Furthermore, numerous research groups are actively working on reducing the power consumption of various system components, aiming to extend the operating time achievable with a single charge [60]. These efforts enable the long-term operation of implant systems, including sensor and stimulation devices, within the body, thereby facilitating their regulatory and therapeutic applications. ...
The brain–computer interface (BCI) is one of the most powerful tools in neuroscience and generally includes a recording system, a processor system, and a stimulation system. Optogenetics has the advantages of bidirectional regulation, high spatiotemporal resolution, and cell-specific regulation, which expands the application scenarios of BCIs. In recent years, optogenetic BCIs have become widely used in the lab with the development of materials and software. The systems were designed to be more integrated, lightweight, biocompatible, and power efficient, as were the wireless transmission and chip-level embedded BCIs. The software is also constantly improving, with better real-time performance and accuracy and lower power consumption. On the other hand, as a cutting-edge technology spanning multidisciplinary fields including molecular biology, neuroscience, material engineering, and information processing, optogenetic BCIs have great application potential in neural decoding, enhancing brain function, and treating neural diseases. Here, we review the development and application of optogenetic BCIs. In the future, combined with other functional imaging techniques such as near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI), optogenetic BCIs can modulate the function of specific circuits, facilitate neurological rehabilitation, assist perception, establish a brain-to-brain interface, and be applied in wider application scenarios.
... A case study of phase shift algorithm with central frequency 5Hz and 180-degree shift is given at Fig. S1-2(b). The other suppressor technique, based on our previous work [25] [7] and designed neural mass [26], is a PID control system. This is described in Fig5. ...
Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we have developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. We have integrated our system with a custom-designed brain implant chip and demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro and in-vivo brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system reliable recording performance. The overall system consumes only 2.93 mA during operation with a biological recording frequency 50 Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.
... This detection delay is reasonable as microcontroller fundamental RISC architecture of executing instruction sets. Recently, Flash-FPGAs have been reported as a reliable tool for closedloop implantable neural interfaces because of its fast and efficient implementation and low static power consumption with great reprogrammability [7], [8], [9]. This makes Flash-FPGA a promising candidate for implementing closed-loop seizure detection algorithm to achieve low power consumption and minimal detection delay purpose. ...
... 7. A comparator is applied for comparing filter out with pre-defined threshold as a detection marker. 8: A counter and a comparator are created to compare with pre-defined frequency look up table to decide PWM output. 9. ...
Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.
Brain-machine Interfaces (BMI) hold great potential for treating neurological disorders such as epilepsy. Technological progress is allowing for a shift from open-loop, pacemaker-class, intervention towards fully closed-loop neural control systems. Low power programmable processing systems are therefore required which can operate within the thermal window of 2° C for medical implants and maintain long battery life. In this work, we developed a low power neural engine with an optimized set of algorithms which can operate under a power cycling domain. By integrating with custom designed brain implant chip, we have demonstrated the operational applicability to the closed-loop modulating neural activities in in-vitro brain tissues: the local field potentials can be modulated at required central frequency ranges. Also, both a freely-moving non-human primate (24-hour) and a rodent (1-hour) in-vivo experiments were performed to show system long-term recording performance. The overall system consumes only 2.93mA during operation with a biological recording frequency 50Hz sampling rate (the lifespan is approximately 56 hours). A library of algorithms has been implemented in terms of detection, suppression and optical intervention to allow for exploratory applications in different neurological disorders. Thermal experiments demonstrated that operation creates minimal heating as well as battery performance exceeding 24 hours on a freely moving rodent. Therefore, this technology shows great capabilities for both neuroscience in-vitro/in-vivo applications and medical implantable processing units.
