An Implementation of SOPC-Based Neural Monitoring System

Huang, Qijun; Chang, Sheng; Peng, Junqi; Xueying, Mao; Wang, Hao

doi:10.1109/tim.2012.2190332

Cited by 4 publications

(5 citation statements)

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“…The data were exported from the Stellate Harmonie system into ASCII text files that were then imported to MATLAB for analysis. A 50 Hz notch filter has been applied to remove electrical interferences [26]. Artifact removal from physiological signals such as eye blinks, muscular movements, and cardiac pulses in ECoG recordings was performed with Wavelet Transform (WT) through the use of discrete wavelet (DW) decomposition and thresholding method.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, some important features of the electrophysiological signals determined by timefrequency analysis (e.g., different brain states, from sleep to cognitive processing) are reflected by different oscillations in the ECoG [27]. Spectral analysis was carried out within each band of interest, that is, delta (1-4 Hz), theta (4-7 Hz), alpha (8-13 Hz), beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), and gamma (30-100 Hz).…”

Section: Discussionmentioning

confidence: 99%

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Biomimetic Sonar for Electrical Activation of the Auditory Pathway

et al. 2017

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Relying on the mechanism of bat's echolocation system, a bioinspired electronic device has been developed to investigate the cortical activity of mammals in response to auditory sensorial stimuli. By means of implanted electrodes, acoustical information about the external environment generated by a biomimetic system and converted in electrical signals was delivered to anatomically selected structures of the auditory pathway. Electrocorticographic recordings showed that cerebral activity response is highly dependent on the information carried out by ultrasounds and is frequency-locked with the signal repetition rate. Frequency analysis reveals that delta and beta rhythm content increases, suggesting that sensorial information is successfully transferred and integrated. In addition, principal component analysis highlights how all the stimuli generate patterns of neural activity which can be clearly classified. The results show that brain response is modulated by echo signal features suggesting that spatial information sent by biomimetic sonar is efficiently interpreted and encoded by the auditory system. Consequently, these results give new perspective in artificial environmental perception, which could be used for developing new techniques useful in treating pathological conditions or influencing our perception of the surroundings. BackgroundUltrasound (US) plays an important role in the environmental perception of many species of mammals (such as rats and bats) for communication and survival; indeed, they use incoming US to gather information about incumbent danger, food availability, and navigational ranging [1]. Although Rodentia and Microchiroptera do not claim a common phylogenetic origin, recent literature reports that there are certain similarities in both mammals regarding anatomical organization [2,3]. Moreover, the cortical area arrangement and cortical frequency processing are similar to other mammals [4][5][6].US frequency range of bats and rats partially overlaps and both eutherian lineages possess similar encoding process of mechanical US waves, although with different aims as spatial navigation and social communication [7,8]. In this study, a method for stimulating rats neural centres by using a bats bioinspired neuroelectronic interface is proposed. The electronic system for US signal processing is based on the natural sonar of Pteronotus parnellii and was previously investigated for modulating brain activity [9,10]. This study demonstrated ultrasound waves, which are properly decoded into bioelectrical signals, travelled along the acoustic pathway, and successfully reached cortical areas, processing sensorial information according to the environment or to cognitive demands. Similarly, exogenous stimulation may alter sensory perception by acting on the neural networks [11].Hereafter, we have investigated how different signal patterns, emulating ultrasound echoes, affected brain activity by means of implanted electrodes connected to an electronic interface. Different patterns of echo signals at 50 ...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Biomimetic Sonar for Electrical Activation of the Auditory Pathway

et al. 2017

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“…The SOPC model combines the characteristics of software and logic, so it also integrates the verification methods of software and logic. The existing SOPC model dynamic test types include function test, performance test, interface test, boundary test, margin test, safety test, strength test, and other test types [4][5][6], which can cover the function requirements, performance requirements, safety requirements, and other requirements in the requirements specification from multiple dimensions, and can give specific data, such as data processing time, system response time, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Does not read and write the same address of dual-port RAM at the same time;(2) Two masters cannot write to the same address at the same time;(3) Does not read and write the same register at the same time; (4) The software reads the value of the updated logic register within N ms;(5) The interrupt status register does not lose an interrupt;(6) The time interval of the interrupt request shall not be less than 1 s;(7) The last interrupt ID is cleared before the next interrupt; (8) One bit of interrupt register cannot be read and written at the same time; (9) The interrupt status register does not lose an interrupt.…”

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confidence: 99%

An Approach to the State Explosion Problem: SOPC Case Study

Zhou,

Wang,

Xue

et al. 2023

Electronics

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The system on a programmable chip (SOPC) architecture is better than traditional central processing unit (CPU) + field-programmable gate array (FPGA) architecture. It forms an efficient coupling between processor software and programmable logic through an on-chip high-speed bus. The SOPC architecture is resource-rich and highly customizable. At the same time, it combines low power consumption and high performance, making it popular in the field of high reliability and other new industrial fields. The SOPC architecture system is complex and integrates multiple forms of intellectual property (IP). Because of this, the traditional dynamic test and the static test cannot meet the requirements for test depth. To solve the problem of verification depth, we should introduce formal verification. But there are some types of IP forms that formal tools cannot recognize. These include black box IP, encrypted IP, and netlist IP in the SOPC model. Also, the state space explosion caused by the huge scale of the SOPC model cannot be formally verified. In this paper, we propose a modeling method using SOPC architecture. The model solves the problem of formal tools not recognizing multi-form IPs. To compress the state space, we propose reducing SOPC variables and branch relationships based on verification properties. Then, we conduct a property verification experiment on the reduced SOPC model. The experiment result shows that the model can significantly reduce the verification time.

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“…It is now common for neuroscientists and clinicians to observe the behavior of diverse signals as part of their research, and this has motivated the development of lownoise, low-power neural recording systems [1][2][3][4]. Since most neurological and behavioral disorders can be observed by brain activity, brain computer interfaces (BCIs) have been developed for the acquisition of neural signals by electroencephalography (EEG) signals [5].…”

Section: Introductionmentioning

confidence: 99%