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High speed digital interfacing for a neural data acquisition system

Enabling of live display functionality for a neural recording system
  • Andreas Bahr EMAIL logo , Lait Abu Saleh , Dietmar Schroeder und Wolfgang H. Krautschneider
Veröffentlicht/Copyright: 30. September 2016
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Current Directions in Biomedical Engineering
Aus der Zeitschrift Current Directions in Biomedical Engineering Band 2 Heft 1

Abstract

Diseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC) was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of a neural recording mixed signal integrated circuit for biomedical signal acquisition. Biomed Eng Biomed Tech Abstracts 2015; 60(S1): 298–299; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider WH. 16 Channel Neural Recording Mixed Signal ASIC. CDNLive EMEA 2015 Conference Proceedings, 2015.]. To enable the live display of the neural signals a multichannel neural data acquisition system with live display functionality is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality. The system has been successfully implemented and was used in a neural recording of a head-fixed mouse.

Keywords: high speed digital interfacing; live display of neural data; neural data acquisition system; neural recording system

1 Introduction

Disorders in the early development of the brain could lead to diseases like certain forms of schizophrenia and genetic epilepsy [1], [2]. This hypothesis of neural development from Ben-Ari et al. and Lewis et al. [1], [2] is investigated with the help of genetic mouse disease models. Recent findings of Marguet et al. [3] showed that by treatment at a critical stage of the brain development the rescue from a genetic epilepsy could be possible. To further investigate these relationships, the signals from neonatal mice have to be analysed. A custom designed application specific integrated circuit (ASIC) was developed that is optimized for recordings in neonatal mice [4], [5], [6].

For the recording of the neural signals from the head of the mouse a Neuronexus multichannel electrode [7] is placed in the cortex of the mouse. The correct placement of the electrodes is essential to enable the neural recording. Saving the data to a memory and analysing it after the recording is not feasible during experiments with living animals. The adjustment of the electrode would take much too long and the animal would be stressed longer than necessary.

To enable the live display of the neural signals, a neural recording data acquisition system is presented. It implements a high speed data transmission from the ASIC to a computer with a live display functionality of the neural data. High-speed data acquisition systems based on FPGA have been presented in [8], [9]. To adopt to the specific needs of the custom designed ASIC, a custom designed digital interfacing had to be developed.

2 Material and methods

The neural recording system is designed in such a way that it adopts to the custom designed integrated circuit and that it fits into the environment of the neuroscience. Existing interfaces like Neuroscope [10] are integrated to enable an easy handling of the interfaces for the experimenters. The selection of the field programmable gate array (FPGA) is based on the system requirements and influenced by the wide spread existence of the opal Kelly Interface board [11], that is used by the open source community open ephys [12] or the company Intan [13].

2.1 Specification and system requirements

The acquisition system interfaces to a custom designed integrated circuit [4], [14]. The circuit with the size of only 1.5 × 1.5 mm2 comprises of 16 analog inputs that are sampled with 20 kS/s/channel at an ADC resolution of 10 bit. The recording using a single integrated circuit results in a data rate of 3.52 Mb/s [4].

The ASIC implements a four wire serial peripheral interface (SPI) with an additional signal from the analogue to digital conversion (ADC). This additional end of conversion (EoC) signal is used as an interrupt to signal that a new sampled value is available. To handle this interrupt, a custom SPI master interface is implemented on the FPGA that requests the data from the ASIC each time the interrupt is triggered.

2.2 Neural data acquisition system

A block diagram of the designed neural data acquisition system is shown in Figure 1. The ASIC communicates with the acquisition board through a SPI interface. For the communication between the acquisition board and the computer a USB connection was chosen [14]. The purpose of the ASIC is the recording of the neural data and the acquisition board transmits the data to the computer. The data is shown immediately on screen in a live mode.

Figure 1: Block diagram of the neural data acqusisition system [14].
Figure 1:

Block diagram of the neural data acqusisition system [14].

2.2.1 Control programm and live display

For the controlling of the neural recording system a control program was developed that is running on the computer. This program writes the data into a binary .dat file. For the live display of the data the tool Neurosccope is used [10]. Neuroscope is a widely used interface in the neuroscientific community. A diagram of the data flow on the computer is shown in Figure 2.

Figure 2: Data flow diagram on the computer: the control program writes the data into a binary file. The data is then simultaneously read from the binary file with the program Neuroscope and displayed in a live mode.
Figure 2:

Data flow diagram on the computer: the control program writes the data into a binary file. The data is then simultaneously read from the binary file with the program Neuroscope and displayed in a live mode.

2.2.2 Data acquisition board

The data acquisition board consists of an FPGA Interface board (XEM 6010) from the company Opal Kelly [11], a power supply unit for the integrated circuit and connectors for interfacing. A block diagram of the data acquisition board is shown in Figure 3. The Interface Board consist of a Xilinx Spartan 6 FPGA, an SDRAM for buffering, a Phase Locked Loop (PLL) for clock generation and a USB Micro Interface block. The board communicates through a High-speed USB 2.0 Interface with the computer (data rate up to 38 Mb/s). The FPGA works at 100 MHz and has sufficient speed to handle the required data rate. The board includes an extra memory of 128 Mb SDRAM.

Figure 3: Block diagram of the data acquisition board.
Figure 3:

Block diagram of the data acquisition board.

Figure 4: Flow chart of the data acquisition phase of the control program.
Figure 4:

Flow chart of the data acquisition phase of the control program.

The Opal Kelly board integrates a communication library that is provided from the manufacturer. The library takes care of the communication between the FPGA on the board and a program on the computer by providing endpoints. By integration of the library into the C++ code and the verilog code, data can be transmitted between the computer and the FPGA by handing over the data to endpoints.

2.2.3 The control program

The data acquisition system and the SPI master are controlled by the control program, the program is written in C++. The program splits into two parts: A system configuration phase where the communication system parameters are initialized and the recording parameters are set and a data acquisition phase, where the data is requested from the board and written into a file on the PC-memory (hard drive).

In the first step of the configuration phase the recoding parameters of the control program are specified. Then a connection to the FPGA is established and the FPGA is programmed by loading the bit file. The SPI and ADC clocks are configured and started. Finally the integrated circuit is configured to the mode of operation.

In the data acquisition phase the data transfer from the FPGA to the PC is controlled by the counter “AvailalbeWordsInFIFO”. This counter holds the number of words that are stored in the buffer on the data acquisition board. The control program continuously checks the size of this counter. If the value exceeds a threshold the specified number of words is picked up from the FIFO and written into the .dat file. This process continues until the specified number of words is recorded.

For the implementation functions and libraries from OkLibrary.lib from Opal Kelly and from IntanTech rhd2000evalboar.lib are used [11], [13].

2.2.4 FPGA implementation with SPI master

The FPGA implements the SPI master, a buffer, a variable frequency clock generator and the communication endpoints. The task of the SPI master is to write the configuration file to the SPI slave on the integrated circuit and to read the neural data from the SPI slave and write it into a buffer on the FPGA.

The SPI master is implemented as a final state machine. The state machine is depicted in Figure 5.

Figure 5: State machine of the SPI master.
Figure 5:

State machine of the SPI master.

The ASIC is designed in a way that it can work with a wide range of clock frequencies. The clock is provided externally to the ADC. The lowest available clock from the PLL is approximately 2 MHz. To generate clock signals in the kilo Hertz range, a configurable frequency clock generator was implemented. It divides the clock from the PLL down by powers of two in the range of 20 to 28.

For communication the OkLibrary was integrated, this library provides communication endpoints [11].

The data is written by the SPI master into a buffer. The buffer is implemented as a 128 Mb SDRAM FIFO.

The ASIC sends data packages of 22 bits to the SPI master, this contains the data of two channels per SPI-block. To transmit the data to the PC two packages are put together into a container of 64 bis, to fit into multiples of 8. The packages are saved into a buffer on the interface board. As soon as 2048 packages of 64 data bits are available, this data set is transmitted to the C++ program on the PC.

3 Results

The presented system is successfully implemented and used in a neural recording of an adult mouse. An acute silicon probe (50 μm inter-site spacing) [7] was used in a recording in the hippocampus of a urethane-anaesthetized adult head fixed mouse. The neural signals from the cortex of the mouse were recorded with the data acquisition system and the data was displayed in the live view modus during the recording. The system worked at a data rate of 3.5 Mb/s. The control program writes to the .dat file and the Neuroscope program reads from the same file at the same time. Figure 6 shows an example low field potential (LFP) of a sharp wave-ripple event (SPW-R) recorded with the system.

Figure 6: Acquisition of LFPs from head fixed mice: (A) Example LFP traces and pseudocolor CSD from an acute silicon probe depth-profile recording in the hippocampus of a urethane-anaesthetized adult mouse during a sharp wave-ripple event (SPW-R). (B) Corresponding spectrogram of the pyramidal layer channel shows the expected ripple event frequency of 100–150 Hz.
Figure 6:

Acquisition of LFPs from head fixed mice: (A) Example LFP traces and pseudocolor CSD from an acute silicon probe depth-profile recording in the hippocampus of a urethane-anaesthetized adult mouse during a sharp wave-ripple event (SPW-R). (B) Corresponding spectrogram of the pyramidal layer channel shows the expected ripple event frequency of 100–150 Hz.

4 Conclusion

A custom designed high speed digital interfacing for a neural data acquisition system has been successfully developed. The system implements a custom SPI interface and interfaces to a a custom designed integrated circuit. It enables a live view functionality of neural data. The functionality has been shown in in-vivo experiments. The measured rate is approximately 30% higher compared to [9].

Acknowledgement

We would like to thank R. Hinsch, D. Isbrandt and his Team from the DZNE-German Center for Neurodegenerative Diseases Cologne for the support of this work and the execution of the measurements. We would like to thank A. Sirota from the Bernstein Center for Computation Neuroscience Munich for the data analysis of this experiment.

Author’s Statement

Research funding: This work was supported by the German Research Foundation, Priority Program SPP1665. Conflict of interest: Authors state no conflict of interest. Material and Methods: Informed consent: Informed consent has been obtained from all individuals included in this study. Ethical approval: The research related to animals complies with all the relevant national regulations and institutional policies for the care and use of animals.

References

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Published Online: 2016-9-30
Published in Print: 2016-9-1

©2016 Andreas Bahr et al., licensee De Gruyter.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/cdbme-2016-0022
Schlagwörter für diesen Artikel
high speed digital interfacing; live display of neural data; neural data acquisition system; neural recording system
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  59. Artificial blood circulatory and special Ultrasound Doppler probes for detecting and sizing gaseous embolism
  60. Detection of microsleep events in a car driving simulation study using electrocardiographic features
  61. A method to determine the kink resistance of stents and stent delivery systems according to international standards
  62. Comparison of stented bifurcation and straight vessel 3D-simulation with a prior simulated velocity profile inlet
  63. Transient Euler-Lagrange/DEM simulation of stent thrombosis
  64. Automated control of the laser welding process of heart valve scaffolds
  65. Automation of a test bench for accessing the bendability of electrospun vascular grafts
  66. Influence of storage conditions on the release of growth factors in platelet-rich blood derivatives
  67. Cryopreservation of cells using defined serum-free cryoprotective agents
  68. New bioreactor vessel for tissue engineering of human nasal septal chondrocytes
  69. Determination of the membrane hydraulic permeability of MSCs
  70. Climate retainment in carbon dioxide incubators
  71. Multiple factors influencing OR ventilation system effectiveness
  72. Evaluation of an app-based stress protocol
  73. Medication process in Styrian hospitals
  74. Control tower to surgical theater
  75. Development of a skull phantom for the assessment of implant X-ray visibility
  76. Surgical navigation with QR codes
  77. Investigation of the pressure gradient of embolic protection devices
  78. Computer assistance in femoral derotation osteotomy: a bottom-up approach
  79. Automatic depth scanning system for 3D infrared thermography
  80. A service for monitoring the quality of intraoperative cone beam CT images
  81. Resectoscope with an easy to use twist mechanism for improved handling
  82. In vitro simulation of distribution processes following intramuscular injection
  83. Adjusting inkjet printhead parameters to deposit drugs into micro-sized reservoirs
  84. A flexible standalone system with integrated sensor feedback for multi-pad electrode FES of the hand
  85. Smart control for functional electrical stimulation with optimal pulse intensity
  86. Tactile display on the remaining hand for unilateral hand amputees
  87. Effects of sustained electrical stimulation on spasticity assessed by the pendulum test
  88. An improved tracking framework for ultrasound probe localization in image-guided radiosurgery
  89. Improvement of a subviral particle tracker by the use of a LAP-Kalman-algorithm
  90. Learning discriminative classification models for grading anal intraepithelial neoplasia
  91. Regularization of EIT reconstruction based on multi-scales wavelet transforms
  92. Assessing MRI susceptibility artefact through an indicator of image distortion
  93. EyeGuidance – a computer controlled system to guide eye movements
  94. A framework for feedback-based segmentation of 3D image stacks
  95. Doppler optical coherence tomography as a promising tool for detecting fluid in the human middle ear
  96. 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
  97. Inside-Out access strategy using new trans-vascular catheter approach
  98. US/MRI fusion with new optical tracking and marker approach for interventional procedures inside the MRI suite
  99. Impact of different registration methods in MEG source analysis
  100. 3D segmentation of thyroid ultrasound images using active contours
  101. Designing a compact MRI motion phantom
  102. Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging
  103. Classification of indirect immunofluorescence images using thresholded local binary count features
  104. Analysis of muscle fatigue conditions using time-frequency images and GLCM features
  105. Numerical evaluation of image parameters of ETR-1
  106. Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation
  107. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography
  108. Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection
  109. Computer assisted assessment of progressing osteoradionecrosis of the jaw for clinical diagnosis and treatment
  110. Evaluation of reconstruction parameters of electrical impedance tomography on aorta detection during saline bolus injection
  111. Evaluation of open-source software for the lung segmentation
  112. Automatic determination of lung features of CF patients in CT scans
  113. Image analysis of self-organized multicellular patterns
  114. Effect of key parameters on synthesis of superparamagnetic nanoparticles (SPIONs)
  115. Radiopacity assessment of neurovascular implants
  116. Development of a desiccant based dielectric for monitoring humidity conditions in miniaturized hermetic implantable packages
  117. Development of an artifact-free aneurysm clip
  118. Enhancing the regeneration of bone defects by alkalizing the peri-implant zone – an in vitro approach
  119. Rapid prototyping of replica knee implants for in vitro testing
  120. Protecting ultra- and hyperhydrophilic implant surfaces in dry state from loss of wettability
  121. Advanced wettability analysis of implant surfaces
  122. Patient-specific hip prostheses designed by surgeons
  123. Plasma treatment on novel carbon fiber reinforced PEEK cages to enhance bioactivity
  124. Wear of a total intervertebral disc prosthesis
  125. Digital health and digital biomarkers – enabling value chains on health data
  126. Usability in the lifecycle of medical software development
  127. Influence of different test gases in a non-destructive 100% quality control system for medical devices
  128. Device development guided by user satisfaction survey on auricular vagus nerve stimulation
  129. Empirical assessment of the time course of innovation in biomedical engineering: first results of a comparative approach
  130. Effect of left atrial hypertrophy on P-wave morphology in a computational model
  131. Simulation of intracardiac electrograms around acute ablation lesions
  132. Parametrization of activation based cardiac electrophysiology models using bidomain model simulations
  133. Assessment of nasal resistance using computational fluid dynamics
  134. Resistance in a non-linear autoregressive model of pulmonary mechanics
  135. Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
  136. Determination of regional lung function in cystic fibrosis using electrical impedance tomography
  137. Development of parietal bone surrogates for parietal graft lift training
  138. Numerical simulation of mechanically stimulated bone remodelling
  139. Conversion of engineering stresses to Cauchy stresses in tensile and compression tests of thermoplastic polymers
  140. Numerical examinations of simplified spondylodesis models concerning energy absorption in magnetic resonance imaging
  141. Principle study on the signal connection at transabdominal fetal pulse oximetry
  142. Influence of Siluron® insertion on model drug distribution in the simulated vitreous body
  143. Evaluating different approaches to identify a three parameter gas exchange model
  144. Effects of fibrosis on the extracellular potential based on 3D reconstructions from histological sections of heart tissue
  145. From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms
  146. Flow optimised design of a novel point-of-care diagnostic device for the detection of disease specific biomarkers
  147. Improved FPGA controlled artificial vascular system for plethysmographic measurements
  148. Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation
  149. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment
  150. Control scheme selection in human-machine- interfaces by analysis of activity signals
  151. Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks
  152. Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach
  153. Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor
  154. Interactive monitoring system for visual respiratory biofeedback
  155. Development of a low-cost senor based aid for visually impaired people
  156. Patient assistive system for the shoulder joint
  157. A passive beating heart setup for interventional cardiology training
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Heruntergeladen am 19.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/cdbme-2016-0022/html
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