Current motorized limb prostheses provide rudimentary functionality for the application in everyday life. Together with a
poor cosmetic appearance, this is the reason why a large percentage of amputees do not use their prosthetic device regularly. This
paper seeks to present an overview of current state of the art research on neural interfaces. The focus lies on non-invasive
recording with EMG and especially High-Density EMG sensors. Additionally, direct machine learning and pattern recognition
algorithms for the decoding of the recorded signals are discussed. Finally, promising research directions for advanced prosthesis
control will be discussed. The bionic arm uses EMG signals to control each action of the hand. In order to control them, we need to
record the EMG signal for different actions. And compare it with real-time values to move the hand in a different manner. There
are separate servo motors to control the actions of each finger separately. So these are programmed by using microcontrollers.
Current motorized limb prostheses provide rudimentary functionality for the application in everyday life. Together with a
poor cosmetic appearance, this is the reason why a large percentage of amputees do not use their prosthetic device regularly. This
paper seeks to present an overview of current state of the art research on neural interfaces. The focus lies on non-invasive
recording with EMG and especially High-Density EMG sensors. Additionally, direct machine learning and pattern recognition
algorithms for the decoding of the recorded signals are discussed. Finally, promising research directions for advanced prosthesis
control will be discussed. The bionic arm uses EMG signals to control each action of the hand. In order to control them, we need to
record the EMG signal for different actions. And compare it with real-time values to move the hand in a different manner. There
are separate servo motors to control the actions of each finger separately. So these are programmed by using microcontrollers.
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
This slide is about the basic theories of Neurotechnology.
It shows
1. An overview of this area
- Market value, etc
2. Basic knowledge
- Types of neurotechnologies
- Basics of neuroscience
- software engineering.
3. Use cases with neurotechnologies.
fNIRS and Brain Computer Interface for CommunicationInsideScientific
LIVE WEBINAR: June 8, 2017
Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states.
Neural activity is accompanied by a hemodynamic (vascular) responses that is sensitive to a host of features of coordinated brain function. Relating these measures to the seemingly endless breadth of human behavior is a principal aim of many scientific investigations. Fortunately, learning, language acquisition, sensory and motor functions, emotion, social interactions, and the influence of a host of disease processes can all be explored from measures of the functional near-infrared spectroscopy (fNIRS) signal. Wearable fNIRS technology exists that is portable, safe and easy to use, resistant to motion artifacts and can be employed in a subjects natural environment.
A promising application for fNIRS is the design of brain-computer interfaces (BCIs) for communication with completely locked-in patients. In the so called ‘locked-in’ state, fully conscious and awake patients are unable to communicate naturally due to severe motor paralysis. These patients are, however, able to modulate their brain activity which can be decoded and understood by exploring the fNIRS signal.
In this exclusive webinar sponsored by NIRx Medical Technologies, experts present the basic principles of fNIRS and BCI, technical setup and guidelines for running a successful fNIRS study and a comparison of fNIRS with other functional neuroimaging methods. Presenters highlight groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in a completely locked-in state. Specifically, Dr. Ujwal Chaudhary (University of Tübingen) shares results of his research with healthy participants and patients with locked-in syndrome due to amyotrophic lateral sclerosis (ALS). Dr. Bettina Sorger (Maastricht University) presents data from a recent study demonstrating the feasibility of a multiple-choice fNIRS-based communication BCI using differently-timed motor imagery as an information-encoding strategy.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Non invasive modalities of neurocognitive science used for brain mappingeSAT Journals
Abstract The brain plays a pivotal role in the study neurocognitive science. It is the seat of intelligence and is a complex organ which is being widely studied. Although mapping different areas of the brain has been carried a lot still remains for the study of cognition. Various non invasive modalities used for brain mapping are classified according to the measurement techniques used. Electromagnetic Technique uses two modalities for studying electromagnetic and electrical activity of the brain EEG (Electroencephalography) MEG (Magnetoencephalogrphy).Whereas hemodynamic technique uses recording of hemodynamic activity of the brain, these modalities are MRI (Magnetic Resonance Imaging).fMRI (functional Magnetic Resonance Imaging) PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography) NRIS (Near Infrared Spectroscopy ). Hemodynamic techniques like MRI, fMRI, PET and SPECT provide excellent spatial resolution while electromagnetic modalities provide excellent temporal resolution .This paper explains various noninvasive modalities, their working principle and a comparative study of all the modalities. Index Terms: modalities, spatial and temporal resolution, Radionuclide, Artifacts
Combining Optical Brain Imaging and Physiological Signals to Study Cognitive ...InsideScientific
In this exclusive webinar sponsored by BIOPAC Systems and fNIR Devices, Dr. Hasan Ayaz, Dr. Kurtulus Izzetoglu and Frazer Findlay present new research capabilities enabled through the integration of optical brain imaging technology and physiological recording systems.
Key topics covered during this webinar include physiological and physical principles of optical brain imaging, theory of operation, hardware and software integration, essential fNIR signal processing (demonstrated using fnirSoft analysis software), common field applications of fNIR imaging, why and how researchers can measure physiological data such as EDA, HR and ECG and acquistion procedures for co-registration of fNIR data and physiological monitoring signals using AcqKnowledge data acquisition and analysis software.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Dr. Frankenstein’s Dream Made Possible: Implanted Electronic Devices The Innovation Group
The developments in micro-nano-electronics, biology and neuro-sciences make it possible to imagine a new world where vital signs can be monitored continuously, artificial organs can be implanted in human bodies and interfaces between the human brain and the environment can extend the capabilities of men thus making the dream of Dr. Frankenstein become true. This paper surveys some of the most innovative implantable devices and offers some perspectives on the ethical issues that come with the introduction of this technology.
www.theinnovationgroup.it
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
Aim of a brain-computer interface (BCI) is to provide a communication channel for paralyzed patients to interact with the outer world. I will start with the motivation behind brain-computer interfaces followed by description of a general BCI. I will then describe various kinds of BCI experiments and methods.
From the Un-Distinguished Lecture Series (http://ws.cs.ubc.ca/~udls/). The talk was given Jun. 8, 2007.
Image Processing Technique for Brain Abnormality DetectionCSCJournals
Medical imaging is expensive and very much sophisticated because of proprietary software and expert personalities. This paper introduces an inexpensive, user friendly general-purpose image processing tool and visualization program specifically designed in MATLAB to detect much of the brain disorders as early as possible. The application provides clinical and quantitative analysis of medical images. Minute structural difference of brain gradually results in major disorders such as schizophrenia, Epilepsy, inherited speech and language disorder, Alzheimer's dementia etc. Here the main focusing is given to diagnose the disease related to the brain and its psychic nature (Alzheimer’s disease).
Analysis of EEG data Using ICA and Algorithm Development for Energy Comparisonijsrd.com
This Electroencephalogram (EEG) signal analysis very useful in clinical research and brain computer interface application. EEG signal (brain wave) recordings are highly susceptible from artifacts which are originated from the non-cerebral origin of the brain. EEG detection and rejection of artifacts are necessary for acquiring correct information from EEG signal. Emotiv, Epoc headset can record 16 channels from the scalp of the electrode. EEGLAB allows analysis of EEG signal through Event related potential (ERP) analysis, Independent component analysis (ICA), and time/frequency analysis. Independent component analysis (ICA) may be suitable method for detecting artifacts. We analyzed EEG data which are recorded using emotiv epoc in a different situation for a single person. EEG data are preprocessed by EEGLAB and decomposes the data by the ICA. Using statistical method, analyzed the all the dataset and finding the relationship among the dataset. T- Test shows that EEG pattern is unique in a person. EEG data is divided into different frequency band to find the relationship between the dataset. Also develop the algorithm for calculating energy of dataset for each channel. Comparing the energy for each dataset and each channel to find the maximum and minimum value of energy. In higher frequency range (13-100 Hz) dataset D (meditation) contains maximum value of energy for most channels among all datasets.
This slide is about the basic theories of Neurotechnology.
It shows
1. An overview of this area
- Market value, etc
2. Basic knowledge
- Types of neurotechnologies
- Basics of neuroscience
- software engineering.
3. Use cases with neurotechnologies.
fNIRS and Brain Computer Interface for CommunicationInsideScientific
LIVE WEBINAR: June 8, 2017
Dr. Ujwal Chaudhary and Dr. Bettina Sorger present groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in completely locked-in states.
Neural activity is accompanied by a hemodynamic (vascular) responses that is sensitive to a host of features of coordinated brain function. Relating these measures to the seemingly endless breadth of human behavior is a principal aim of many scientific investigations. Fortunately, learning, language acquisition, sensory and motor functions, emotion, social interactions, and the influence of a host of disease processes can all be explored from measures of the functional near-infrared spectroscopy (fNIRS) signal. Wearable fNIRS technology exists that is portable, safe and easy to use, resistant to motion artifacts and can be employed in a subjects natural environment.
A promising application for fNIRS is the design of brain-computer interfaces (BCIs) for communication with completely locked-in patients. In the so called ‘locked-in’ state, fully conscious and awake patients are unable to communicate naturally due to severe motor paralysis. These patients are, however, able to modulate their brain activity which can be decoded and understood by exploring the fNIRS signal.
In this exclusive webinar sponsored by NIRx Medical Technologies, experts present the basic principles of fNIRS and BCI, technical setup and guidelines for running a successful fNIRS study and a comparison of fNIRS with other functional neuroimaging methods. Presenters highlight groundbreaking research in the field of fNIRS-based BCI for communication with healthy subjects and patients in a completely locked-in state. Specifically, Dr. Ujwal Chaudhary (University of Tübingen) shares results of his research with healthy participants and patients with locked-in syndrome due to amyotrophic lateral sclerosis (ALS). Dr. Bettina Sorger (Maastricht University) presents data from a recent study demonstrating the feasibility of a multiple-choice fNIRS-based communication BCI using differently-timed motor imagery as an information-encoding strategy.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Non invasive modalities of neurocognitive science used for brain mappingeSAT Journals
Abstract The brain plays a pivotal role in the study neurocognitive science. It is the seat of intelligence and is a complex organ which is being widely studied. Although mapping different areas of the brain has been carried a lot still remains for the study of cognition. Various non invasive modalities used for brain mapping are classified according to the measurement techniques used. Electromagnetic Technique uses two modalities for studying electromagnetic and electrical activity of the brain EEG (Electroencephalography) MEG (Magnetoencephalogrphy).Whereas hemodynamic technique uses recording of hemodynamic activity of the brain, these modalities are MRI (Magnetic Resonance Imaging).fMRI (functional Magnetic Resonance Imaging) PET (Positron Emission Tomography), SPECT (Single Photon Emission Computed Tomography) NRIS (Near Infrared Spectroscopy ). Hemodynamic techniques like MRI, fMRI, PET and SPECT provide excellent spatial resolution while electromagnetic modalities provide excellent temporal resolution .This paper explains various noninvasive modalities, their working principle and a comparative study of all the modalities. Index Terms: modalities, spatial and temporal resolution, Radionuclide, Artifacts
Combining Optical Brain Imaging and Physiological Signals to Study Cognitive ...InsideScientific
In this exclusive webinar sponsored by BIOPAC Systems and fNIR Devices, Dr. Hasan Ayaz, Dr. Kurtulus Izzetoglu and Frazer Findlay present new research capabilities enabled through the integration of optical brain imaging technology and physiological recording systems.
Key topics covered during this webinar include physiological and physical principles of optical brain imaging, theory of operation, hardware and software integration, essential fNIR signal processing (demonstrated using fnirSoft analysis software), common field applications of fNIR imaging, why and how researchers can measure physiological data such as EDA, HR and ECG and acquistion procedures for co-registration of fNIR data and physiological monitoring signals using AcqKnowledge data acquisition and analysis software.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Dr. Frankenstein’s Dream Made Possible: Implanted Electronic Devices The Innovation Group
The developments in micro-nano-electronics, biology and neuro-sciences make it possible to imagine a new world where vital signs can be monitored continuously, artificial organs can be implanted in human bodies and interfaces between the human brain and the environment can extend the capabilities of men thus making the dream of Dr. Frankenstein become true. This paper surveys some of the most innovative implantable devices and offers some perspectives on the ethical issues that come with the introduction of this technology.
www.theinnovationgroup.it
SRGE Workshop on Intelligent system and Application, 27 Dec. 2017 in the framework of the int. conf of computer science, information systems, and operation research, ISSR, Cairo University
Aim of a brain-computer interface (BCI) is to provide a communication channel for paralyzed patients to interact with the outer world. I will start with the motivation behind brain-computer interfaces followed by description of a general BCI. I will then describe various kinds of BCI experiments and methods.
From the Un-Distinguished Lecture Series (http://ws.cs.ubc.ca/~udls/). The talk was given Jun. 8, 2007.
Image Processing Technique for Brain Abnormality DetectionCSCJournals
Medical imaging is expensive and very much sophisticated because of proprietary software and expert personalities. This paper introduces an inexpensive, user friendly general-purpose image processing tool and visualization program specifically designed in MATLAB to detect much of the brain disorders as early as possible. The application provides clinical and quantitative analysis of medical images. Minute structural difference of brain gradually results in major disorders such as schizophrenia, Epilepsy, inherited speech and language disorder, Alzheimer's dementia etc. Here the main focusing is given to diagnose the disease related to the brain and its psychic nature (Alzheimer’s disease).
Brain Computer Interface (BCI) aims at providing an alternate means of communication and control to people with severe cognitive or sensory-motor disabilities. These systems are based on the single trial recognition of different mental states or tasks from the brain activity. This paper discusses the major components involved in developing a Brain Computer Interface system which includes the modality to obtain brain signals and its related processing methods.
Brain Computer Interfacing using Electroencephalography and Convolutional Neu...ijtsrd
A brain computer interface BCI is a communication system that interprets brain activity into commands for a computer or other devices .This technology is the emphasis of a rapidly growing research and development initiative that is greatly exciting experts, and the public in general. Today, humans can use the electrical signals from brain activity to interrelate with, influence, or change their surroundings. The evolving field of BCI technology may allow persons in capable of speaking and or use their limbs to yet again interconnect or operate assistive devices for walking and functional objects. In other words, a BCI permits users to act on their environment by using only brain activity, without using outlying nerves and muscles. In this paper we give an overview to some of the aspects of BCI research mentioned above, present a tangible example of a BCI system, and highlight recent progresses and open complications. Ms. Dhiviyaa S | Ms. Sri Priyanka J | Ms. Meghala S "Brain Computer Interfacing using Electroencephalography and Convolutional Neural Networks" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30174.pdf Paper Url :https://www.ijtsrd.com/other-scientific-research-area/other/30174/brain-computer-interfacing-using-electroencephalography-and-convolutional-neural-networks/ms-dhiviyaa-s
Brain Computer Interface Next Generation of Human Computer InteractionSaurabh Giratkar
In the area of HCI research the main focus is on defining new ways of human interaction with computer system. With the passes of time a number of inventions have been made in this field. In initial days we used only keyboards to access our computer system (e.g. in Unix Terminal). In Second phase, after invention of mouse and other pointing devices, we started using graphical user interface using pointing devices like mouse which make the use of computer more easy and comfortable. Nowadays we are using pressure-driven mechanism, i.e. touch screen, which is common at ATMs, Mobile phones and PDAs etc. Although it is not as common in daily works but the release of tablet PCs and its popularity shows that the day is not much far when we wouldn’t be having keyboards and mouse at all.
All of these inventions have been made for balancing the requirements of society and user. E.g. Games, Multimedia Applications etc are not possible using only-Keyboard so we need mouse driven system for such applications, similarly we cannot have large keyboard on mobile so we need a touch screen system for mobiles. In addition to these traditional HCI models, there are some more advance HCI technology too for adding more flexibility and hence making the product more useful. E.g. swap card system at office doors for attendance and ATM-swap card for shopping. Speech processing systems are also there where we can access our computer system using our speech. Fig 1 shows most popular traditional HCI system.
Review Paper on Brain-Computer Interface and Recent TrendsEditor IJMTER
Although the development in computer hardware and software has been enormous in
recent decades, but the development in Human-computer interface (HCI) has been very slow but
discontinuous. From punch cards, text console, mouse to recently introduced gesture and voice
controls the development is enormous, the recent addition to this is Brain computer interface (BCI).
BCI makes uses of changes in brain and Electrical activity of brain to certain actions/thought
processes and uses algorithms to interpret the intention of the user, and reports the same to computer.
This paper focuses to review this area of HCI and demystify the techniques and concepts used and
also give a short report on recent development and research on the same .This technique of BCI not
only would be helpful for disabled to gain new strength but also would change the way we interact
with machine...FOREVER
Classification of EEG Signals for Brain-Computer InterfaceAzoft
This e-book gives you a sneak peak into how the classification of right hand movements via EEG could contribute to the development of a brain-computer interface. The Azoft R&D department, along with Sergey Alyamkin and Expasoft provide detailed data from research done for the "Grasp-and-Lift EEG Detection" competition organized by Kaggle. You’ll learn why the deep learning algorithms can be effective in various types of signal classifications and how to apply convolutional neural networks for a specific task such as identifying hand motions from EEG recordings.
See more details on our website: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
This presentation is given in (2015) . As the power of modern computers grows alongside our understanding of the human brain, we move ever closer to making some pretty spectacular science fiction into reality.
Non-contact, camera-based physiological measurements -
such as blood volume pulse and respiration rate - can now
be inferred by neural networks based on facial videos. This
technology has the potential to enable medical professionals
to make more informed telehealth decisions. Currently, this
software only runs on PCs, without a user interface. The neu-
ral network has a significant computational cost, making it
difficult to deploy on low-cost mobile devices. It also performs
poorly in varied environmental, sensor, personal, and contex-
tual conditions - such as darker skin tones. In this project,
we implement this neural network as an Android app that
runs in real-time; develop a more efficient architecture; evalu-
ate these architectures on older smartphones; and provide an
open-source, simple personalization pipeline to enable users to
calibrate the app. This all serves to make the technology more
democratic: making it available to as many users as possible,
while giving them the means to train and develop it further.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
Low power architecture of logic gates using adiabatic techniques
Senior Thesis
1. Hardware Implementation of a Wireless
Backscatter Communication Protocol for
Brain-Controlled Spinal Interfaces
Anand Sekar, Laura Arjona, James Rosenthal, Joshua R. Smith, Chet T. Moritz
Abstract—Brain-Controlled Spinal Interfaces are innovative medical devices which can aid physical rehabilitation to
restore volitional movement in areas paralyzed by cervical Spinal Cord Injury. The device entails reading neural
signals in the motor cortex, decoding the intention to perform a specific movement from those signals, and stimulating
the spinal cord when movement is intended. Decoding the neural signals is computationally-intensive and therefore
power-hungry, making portable devices difficult to implement. A wireless implementation of a Brain-Controlled
Spinal Interface consisting of an external computer (reader) which utilizes backscatter communication to collect data
from an implanted neural sensor (implant) can perform that intensive computation. This thesis focuses on the
hardware, implant-side implementation of the wireless backscatter communication protocol on a low-power FPGA.
Index Terms—BCSI, BCI, backscatter communication, FM0, neural implant, Verilog
Acknowledgements
I would like to express my gratitude to Dr. Joshua Smith for
overseeing my research and Dr. Chet Moritz for bringing me
onto the Restorative Technologies Lab. My sincere thanks to
Nicholas Tolley and Soshi Samejima for showing me the
ropes with hands-on neural decoding. I would like to
recognize the invaluable guidance my mentors James
Rosenthal and Laura Arjona have provided through this
project.
This research would not have been possible without the
support from the Computational Neuroscience Training Grant
(CNTG) and the Center for Neurotechnology. I am grateful to
Dr. Eric Shea-Brown who introduced me to the extraordinary
field of computational neuroscience and persuaded me to
apply to the CNTG. I wish to thank the UW CSE
Departmental Honors program for motivating this thesis.
I acknowledge the contribution of all the amazing professors
at the University of Washington. Heartfelt thanks to my
friends and family for supporting me on this journey.
I. Introduction
The paper is organized as follows. §I introduces the problem
of spinal cord injury and the concept of neuroplasticity. §II
explains how a brain-controlled spinal interface is an effective
solution and how each component works. §III covers the
various ways brain-controlled spinal interfaces can be
implemented. §IV explains the wireless communication
protocol and the reader-side implementation. §V covers the
implant-side implementation, which is the focus of this thesis.
§VI demonstrates the integration of wireless components.
§VII theorizes future work. §VIII concludes.
A. Spinal Cord Injury
here are roughly 17, 500 new cases of Spinal Cord Injury
(SCI) in the United States each year, primarily caused by
vehicular accidents and falls (“Spinal cord injury facts and
figures at a glance,” 2020). The cervix is the top portion of the
spinal cord (containing vertebrae C1-C7) supporting the neck;
injuries in this area result in a host of complications, primarily
incomplete/ complete tetraplegia/ quadriplegia, i.e. various
levels of limited or absent movement and sensation below the
neck; the higher the injury, the more severe the effects (“Acute
spinal cord injury,” 2015). There is no known cure for SCI,
however, there are physical rehabilitation interventions which
can help patients to recover partial functions. The restoration
of upper limb function is the highest treatment priority for
improving quality of life for patients with cervical SCI
(Inanici, 2018).
B. Neuroplasticity
Donald Hebb wrote a postulate in his book, The Organization
of Behavior, which is popularly summarized as “neurons that
fire together wire together” (Hebb, 1949). This wiring, also
2. known as Hebbian plasticity or neuroplasticity, takes place
during the natural development of the nervous system and
during learning, but new research shows that closed-loop,
activity-dependent neural devices can strongly influence this
wiring (Moritz, 2018). For instance, an artificial connection
can be created between two sites in the motor cortex by
recording the neuronal activity in one site and electrically
stimulating the other site correspondingly; just a couple days
of this activity-dependent stimulation resulted in changes
which persisted for more than a week (Jackson, Mavoori, &
Fetz, 2006).
II. Brain-Controlled Spinal Interfaces
A. BCI to BCSI
This view of neuroplasticity explains how emerging
brain-computer interfaces (BCIs) can be used to restore motor
function (Tolley, 2019; Capogrosso et al., 2016; Carlson &
Millan, 2013; Hochberg et al., 2012; Moritz, Perlmutter, &
Fetz, 2008). BCIs are devices which - unlike a keyboard or
mouse - extract a user’s intention more directly - such as from
neural signals - in order to interact with a computer; such
technologies can enable users to, for example, control robotic
prosthetic limbs (Rao, 2019). Brain-controlled spinal
interfaces (BCSIs) could be considered as a subset of BCIs,
except the end-effector isn’t a computer’s cursor or robotic
arm, it’s - indirectly - the user’s own muscles. This indirection
is twofold: first, the BCSI stimulates the area of the spinal
cord which contains nerves associated with the targeted
muscles, not the muscle itself; second, the BCSI only helps the
user regain control over the targeted muscles more effectively,
it doesn’t simply move the muscles as the user intends. The
goal of a BCSI is to promote neuroplasticity to restore severed
pathways from SCI.
As shown in Figure 1, BCSIs entail (1) recording neural
signals from the motor cortex, (2) decoding these signals to
predict intended movement, and (3) stimulating the spinal cord
surface at the site of injury.
Fig. 1. The three main steps of a BCSI (Moritz, June 7, 2014).
B. Recording Neural Signals
As shown in Figure 2, there are three main methods used to
record neural activity: (1) electroencephalography (EEG) with
electrodes placed on the scalp, (2) electrocorticography
(ECoG) with electrodes implanted over the surface of the
cortex, and (3) intracortical electrodes which penetrate the
neural tissue (Tolley, 2019). These respectively are more
invasive but have a better signal-to-noise ratio. Intracortical
electrodes are traditionally used to record the spiking activity
of individual neurons, and have worked well for many BCIs;
however, local field potentials (LFPs), which represent the
sum of synaptic input into neurons, are more stable over time
and are also successful at capturing neural signals related to
movement intention (Fazli et al., 2009, as cited in Tolley,
2019). The data which our BCSI sensor transmits are LFPs.
Fig. 2. Cross-section of the brain representing each of the three
common neural recording methods (Leuthardt, Schalk, Roland, Rouse,
& Moran, 2009).
C. Spinal Stimulation
Somewhat analogous to recording neural signals, there are
three primary methods for stimulating the spinal cord for
restoring movement after SCI: (1) transcutaneously with
electrodes on the skin above the spinal cord, (2) epidurally
with electrodes implanted over the surface of the spinal cord,
3. and (3) intraspinally with electrodes penetrating the spinal
cord (Ievins & Moritz, 2017). These are respectively more
invasive but have a tighter scope of stimulation.
Regardless of method, there are many aspects of stimulation
(electric/ magnetic, frequency, amperage, placement etc…)
with some configurations more effective for targeting specific
neural pathways than others. Each method of stimulation has
been shown to enable control over upper/ lower limbs as well
as autonomic (bladder, bowel, sexual etc...) functions (Ievins
& Moritz, 2017). Epidural stimulation is effective when
applied continuously, whereas intraspinal stimulation requires
activity-dependent stimulation to be more effective; these are
shown in Figure 3.
Fig. 3. Intraspinal and epidural stimulation (Mondello, Kasten, Horner,
& Moritz, 2014).
D. Neural Decoding
Neurons and neuronal activity encode information: they
contain some clues regarding what is happening: sensory
input, movement intention, thoughts, etc… Figuring out the
relationship between stimuli (events) and neural responses
further helps us understand this encoding, which enables us to,
for example, decode neural activity to predict when someone
wants to move a muscle.
The process of decoding involves identifying which features
or aspects of the neural data correspond to an event. In
developing a BCSI, we performed experiments with rats. The
rats were trained on a lever-pushing task where they touched a
nose-poke sensor while pushing a lever. On each successful
push, they were rewarded with apple juice. Eventually, they
were implanted with a 16-channel intracortical array which
recorded LFPs, as seen in Figure 4 (Tolley, 2019).
Fig. 4. Rat lever-pushing task (Tolley, 2019).
Thus, we ended up with simultaneous recordings of the neural
activity (LFPs) and the event (lever push + nose poke). The
data I helped collect from one iteration of the experiment is
shown in Figures 5 and 6.
Fig. 5. Amplitude-over-time plots of each of the 16 neural sensor
channels. Adapted from code by Nicholas Tolley.
Fig. 6. Nose-poke (green) and lever-push (blue) over time. Adapted
from code by Nicholas Tolley.
Khorasani et al. and Tolley et al. found that the feature in
LFPs which most clearly correlated with the lever push was
the frequency band power in a certain range located between 2
and 900 Hz. To achieve this, we first manually remove any
channels which clearly contain too much noise. Then, we pass
the raw LFP signals through a common average reference
(CAR) filter to get rid of noise common to all channels,
followed by a set of band-pass/ low-pass filters and a rectifier,
4. ending up with the power in the desired frequency band over
time. The core part of our decoder is a canonical correlation
analysis (CCA) filter, which we train with the data. This filter
results in a set of coefficients, one for each channel. We can
then linearly combine (multiply and add) the weighted band
power from each channel to result in a signal which finally
predicts lever movement, as seen in Figure 7.
Fig. 7. Real level movement (blue) and roughly decoded lever
movement (red). Adapted from code by Nicholas Tolley.
After placing a threshold on predicted movement, we can
stimulate the spinal cord whenever that threshold is crossed,
i.e. when the prediction is strong enough, resulting in
activity-dependent stimulation.
Ultimately, we can consider the BCSI as artificial neurons
which promotes a new neural pathway around an injury in the
cervical spinal cord which severed a previous neural pathway:
decoding one’s intention to move, then encoding that intention
as stimulation to promote muscle movement.
III. Implementing BCSIs
Through developing the BCSI, the primary way of performing
the decoding has been via the TDT (Tucker-Davis
Technologies) system - a large, rack-mounted lab computer -
as pictured in Figure 8.
Fig. 8. Soshi Samejima using the TDT (photo taken by Nicholas
Tolley).
The development of this device will have to go beyond this
medium in order to be mobile. The electrodes used to record
the LFPs and the electrodes used to stimulate the spinal cord
are already small and relatively power-efficient, i.e. already
suitable for mobile use. However, this large computer
in-between which takes care of the computationally-intensive
(and thus power-hungry) decoding is not suitable for portable
use.
One method of implementing the BCSI decoding is via a
completely implanted application-specific integrated circuit
(ASIC) derived from an FPGA (field-programmable gate
array) which can perform the decoding. Such a device has
been developed by Ranganathan et al., called the Neural
Closed-Loop Implantable Platform (NeuralCLIP) shown in
Figure 9. Power supply is already a concern with
medically-implantable devices; the main downside of the
NeuralCLIP is that it must be carefully and specifically
designed to handle the computational load efficiently.
An alternative implementation which circumvents this issue is
a wireless one: an external reader utilizing backscatter
communication to transfer the data from the implanted
electrodes to a computer which can do the intensive decoding.
The hardware for such a device has been developed by
Rosenthal et al., called NeuroDisc. The tradeoffs between the
different implementation approaches are summarized in Table
10.
Fig. 9. [Left] NeuralCLIP and [Right] NeuroDisc (Ranganathan et al.,
2019; Rosenthal, Kampianakis, Sharma, & Reynolds, 2018).
TDT Implanted Wireless
Computational Resources High Low High
Mobility / Duration Very Low High/ Med High
Battery-dependency High High Low
Communication Latency Very Low Low High
Tab. 10. Comparing different implementations of decoding in BCSI.
IV. The Wireless Protocol
A. Overview
In order to communicate the implanted system with the
external reader, a protocol is necessary to specify in what
forms information will be exchanged. On a high-level, the
protocol involves communication between only two
components: the (external) reader, and the (internal) sensor
implant. For consistency, we will refer to the following terms
5. from the implant’s perspective: “uplink” as information sent
from the sensor to the reader, and “downlink” as information
sent from the reader to the sensor.
Fig. 11. Sequence diagram of the protocol. START and READ refer to
the reader commands, while FRAME contains the transmitted neural
data. D refers to the delay from the reader performing feature
extraction and responding to the implant.
Figure 11 outlines the protocol. The sensor implant acts like a
server and the reader acts like a client: the reader sends “start/
read/cont” commands downlink to request information from
the implant, and the implant sends back the information uplink
in packets (frames). Finally, the reader sends an “end”
command to stop the communication.
B. Downlink Communication
The downlink modulation and data encoding works as follows.
The reader commands are encoded by Pulse Interval Encoding
(PIE), as represented in Figure 12.
Fig. 12. PIE encoding. PW refers to the duration of the Pulse Width.
The pulse width (PW) corresponds to the duration of half a bit
‘0’. A bit 0 is represented as high amplitude for one PW
followed by low for one PW. It is important to note here that a
bit 0 or 1 corresponds to exactly one 0 or 1 PIE symbol. A bit
1 is represented as high amplitude for two PWs followed by
low for one PW. Then, the data is modulated by an analog
carrier wave via Amplitude Shift Keying (ASK), as
represented in Figure 13.
Fig. 13. An example of ASK with on-off keying (OOK). The baseband
signal on the left, modulated by the carrier wave on the right, results in
the transmitted signal. Adapted from code written by Joshua Smith.
ASK simply oscillates the radio signal at a higher amplitude/
power for a bit 1 and at a lower amplitude for a bit 0. PIE
keeps the transmitted signal mostly high, with the goal of
maximizing the energy flow to power the implant during data
transmission. This is the main reason we selected PIE
encoding for our reader symbols: since we are using
backscatter communication, we want to maximize the energy
delivered and available at the implant.
Currently, the PW duration is 6.25μs, making the encoded
symbol 0 with duration 2PW = 12.5μs and the encoded
symbol 1 with duration 3PW = 18.75μs. Assuming an equal
number of 1s and 0s transmitted, this downlink
communication achieves a data rate of 64kbps.
There are four commands which are sent: Start, Read, Cont,
and End. The Start command consists of the bitstream ‘00’
followed by the 12-bit device ID (to select a particular implant
to communicate with). The Read command consists of the
bitstream ‘01’ followed by a 16-bit stream indicating which
of the 16 channels to transmit and an 8-bit stream indicating
the number of consecutive frames to transmit within two
reader commands (Frame Count). The Cont command is
simply the bitstream ‘10;’ this is sent after every Frame Count
number of frames to maintain synchronization. The End
command consists of ‘11’ followed by the 12-bit device ID.
These are summarized in Table 14.
6. Command Structure
Start [2b: 00 | 12b: device ID]
Read [2b: 01 | 16b: active channels | 8b: frame count]
Cont [2b: 10]
End [2b: 11 | 12b: device ID]
Tab. 14. Reader commands
Each command begins with a frame-synchronization
(frame-sync) pulse which is a 12.5μs delimiter followed by a
PIE-encoded 0 and calibration pulse, as shown in Figure 15.
Fig. 15. Frame-sync. PW refers to the duration of the Pulse Width.
C. Uplink Communication
The protocol supports up to 16 neural channels, with 16 bits
per channel. After recording the data with the electrodes, the
implant encodes the data using Hamming encoding, which is a
linear error-correcting code that can detect two-bit errors and
correct one-bit errors. The goal of error-correcting codes, like
Hamming, is to send redundant information in different ways,
such that the data is robust in a noisy environment. Here, we
specifically use H(11, 15) which transmits 15 total bits for
every 11 data bits.
Next, the reader applies an interleaving algorithm to the frame.
The goal of the interleaving process is to increase the
likelihood of correction/ detection of burst errors (which
become spread). In particular, we use a pattern interleaving
algorithm that requires a permutation vector with the same
length in bits as the frame. This vector will be predetermined
by the reader and implant, so that an adversary would not be
able to interpret the intercepted data.
After the hamming and interleaving blocks, the resulting
frame bits are encoded via FM0. FM0, also known as
differential manchester encoding, is a line encoding scheme
which involves a switch on every symbol period. If the bit is
0, it switches again halfway through the period, otherwise if
the bit is 1, it stays constant during the period.
Fig. 16. FM0 encoding (shown by the yellow signal).
As seen in Figure 16, the transitions of the digital signal
indicate a logical value (1/ 0), not the value of the digital
signal itself (high/ low). One reason to use FM0 modulation to
wirelessly transmit the data from the implant is that a
transition is guaranteed at every bit boundary, making the
synchronization between the implant and the reader easier to
achieve. FM0 is also less error-prone in noisy environments
than simply comparing the signal levels against a threshold.
Moreover, it achieves Zero DC bias, i.e. if the high/ low
analog signals are the same magnitude/ opposite polarity, the
average voltage is zero, resulting in lower transmitting power
necessary and minimal noise (Schouhamer Immink &
Pátrovics, 1997).
Finally, the FM0-encoded frame is transmitted using ASK or
Phase-Shift Keying (PSK) as Binary-PSK (BPSK) or
Differential Quadrature PSK (DQPSK). Quadrature amplitude
modulation utilizes complex values to send two orthogonal
carrier waves, such that two bits can be sent per symbol,
yielding a higher data rate. DQPSK is the method used for
backscatter modulation in the NeuroDisc, but our current
implementation only uses ASK.
D. Backscatter Communication
One approach for the implant to send data to the reader would
be to actively generate and amplify its own carrier frequency.
However, this consumes a significant amount of power.
Another way for the implant to send data to the reader is for
the reader to broadcast a carrier wave, which the implant can
passively encode data on by switching the impedance on its
antennae, reflecting the carrier wave according to the data.
This approach was preferred and used for our system since it
consumes significantly less power (Rosenthal, Kampianakis,
Sharma, & Reynolds, 2018). The downside is that this results
in more path attenuation, i.e. power loss as a wave propagates
more distance, which could result in less reliable
communication in more noisy environments. To minimize the
impact of these possible errors, we used the hamming and
interleaving blocks.
E. OSI Model
The Open Systems Interconnection (OSI) model is a
hierarchical structure composed of layers which represents
how most modern communication infrastructures, such as the
Internet, work. This involves several layers of encapsulation -
from physical to application - to ensure robust (error-free)
communication. The OSI model applied to this protocol is
shown in Table 17.
7. Data Unit Layer Description
Data Application Implant: Neural signals (LFPs) recording
Reader: Neural signals decoding (via GNU
Radio)
Data Presentation Data representation: Binary 2’s complement
Data encryption: future work
Data Session N/A: The session layer specifies procedures
such as restart and termination of operation,
hence, there is no need for this layer in our
system
Segment Transport Controls the reliability of data transfer based
on a set of reader commands and implant
responses. The reader talks first, and keeps
track of the frame counter for frame
retransmission
Packet Network N/A: The network layer specifies routing with
multi-node networks, but our communication
is point-to-point
Frame Data link Implant: transmits data frames as specified
Reader: transmit commands as specified
Error correction and detection: Hamming
H(11,15) and Interleaving
Symbol Physical Wireless (backscatter) communication
Implant: 1Mbps, Reader: 64kbps
Encoding: FM0 (uplink), PIE (downlink)
Modulation: ASK/BPSK
Tab. 17. OSI model applied to the communication protocol.
F. Reader Implementation
Fig. 18. Communication block diagram for the reader, that can be
divided into two parts: received signal decoding and feature
extraction.
Figure 18 summarizes the reader-side implementation. The
reader uses a Universal Software Radio Peripheral (USRP)
software-defined radio to both transmit the Ultra-High
Frequency (UHF) waves at 915MHz carrying the reader
commands and receive the modulated backscatter signal
carrying the implant data. In particular, we use an USRP N210
with an SBX daughterboard. This information is transferred
from the USRP to a computer using Gigabit Ethernet which
runs GNU Radio. The GNU radio receiver uses a flowgraph
approach to process the received data frames and perform
feature extraction. The reader can be divided into 4 stages:
1. Signal detection: Gate, Matched Filter
2. Signal decoding: FM0 decoding
3. Error Detection/correction: De-interleaving,
Hamming decoding
4. Feature Extraction: CAR, BPF, CCA, LPF,
Peak-Finding
Then the reader sends a command back to the implant.
V. Implant Implementation
A. Hardware/ Software Utilized
For prototyping, we use the TinyFPGA BX board, which
comprises a 16MHz clock. As mentioned in Section III, we
decided to use an FPGA with the ultimate goal of developing a
completely implanted ASIC. This FPGA is programmed with
Verilog, a hardware description language (HDL). In order to
test the Verilog blocks, we used testbenches and the
GTKWave software. After simulation, we tested and verified
the hardware performance via an oscilloscope.
B. Uplink FM0 Encoding
Fig. 19. Block diagram for the FM0 module.
The goal of this module is to take a 126-bit packet (received
from the implanted electrodes and neural microchip) and
transmit it as FM0 symbols with the proper timing. In the
block diagram (Figure 19), each block is a Verilog module and
the dashed blocks are hardware-related elements. Every packet
8. corresponds to one 126-bits data frame from the sensor
implant.
The communication between the neural microchip and the
FPGA is application-dependent. For example, the NeuralClip
(Ranganathan et al., 2019) uses Serial Peripheral Interface
(SPI) to transfer the data between the implanted neural
microchip (Intan Technologies) and the FPGA.
The DFF module is a simple d-flip-flop: it takes in a clock and
input signal, and outputs that input signal synchronized to the
clock. It ensures that no timing issues could permeate over
from hardware. This is used to synchronize the reset signal
from the button (Figure 20).
Fig. 20. Simulation of the DFF module. When the input ‘d’ switches on
the positive edge of the clock, the output ‘q’ also switches
immediately. When the input ‘d’ switches in-between a clock cycle,
the output ‘q’ only switches on the next positive edge of the clock.
The Debouncer module takes in a clock and an input signal,
and outputs that signal in such a way that removes the bounces
caused by flipping a hardware switch. Debouncing ensures
that a switch only switches once per flip as intended instead of
multiple times in rapid succession. This ensures that when the
SEND button is flipped, the ‘send_enable’ signal only has a
single positive edge, as opposed to several from bouncing as
shown in Figure 21.
Fig. 21. Oscilloscope screenshot of debouncing signal. The blue (2)
signal is the raw signal from the SEND button, which oscillates several
times during a single switch flip. The purple (3) signal is the
debounced (and delayed) ‘send_enable’ signal which properly flips
once after a single switch flip.
The Div module outputs a clock ‘period’ times slower than
‘clk.’ This is used to create the symbol clock, which needs to
have a period of 6.25μs, which corresponds to a frequency of
160 KHz. We use this frequency because the implant sensor is
required to backscatter the data at 160kbps. The TinyFPGA
has a 16 MHz system clock, so we need our symbol clock to
be 100x slower than that, i.e. we set ‘period’ to 100. In the
simulation (Figure 22), we show how Div can generate a clock
that’s 4x slower and 10x slower than the input clock.
Fig. 22. Simulation of the Div module. When ‘period’ is set to 4,
‘out_clk’ has a period four times as long as ‘clk,’ and when ‘period’ is
set to 10, ‘out_clk’ has a period ten times as long as ‘clk.’
The Edge Detect module detects a positive or negative edge
from the input. If ‘pos’ is 1, it detects a positive edge,
otherwise it detects a negative edge. When an edge is detected,
‘out’ is raised for one clock cycle. This is used in several
modules, mainly to detect the positive (or negative) edges of
the symbol period. In the simulation (Figure 23), detecting a
posedge and negedge occurs respectively.
Fig. 23. Simulation of the Edge Detect module. When ‘pos’ is set to 1,
‘out’ becomes high for one clock cycle upon detecting the positive
edge from ‘in.’ When ‘pos’ is set to 0, ‘out’ becomes high for one
clock cycle upon detecting the negative edge from ‘in.’
The Counter module begins ‘count’ at zero. Once a positive
edge is detected in ‘send_enable,’ count begins incrementing
every symbol period. Once the count reaches the ‘max’ value,
it resets to 0. The ‘sending’ signal is high whenever the
module is incrementing and low when it isn’t. This counter is
used for indexing into the sensor data, sending it bit by bit. In
this case, we’re sending a 126-bit packet, so ‘max’ is set to
125. The ‘sending’ signal is used by the FM0 module to only
encode (flip back and forth) if data is being sent, and stay at a
flat zero otherwise.
The Sender module sends out each bit of a ‘data_in’ packet,
starting when a positive edge is detected in ‘send_enable,’ and
sending the next bit every symbol period. It stops sending
once the end of the packet is reached. The ‘sending’ signal is
high only when data is being sent. The sender simply uses the
counter to index through the sensor data. The 126-bit packet is
9. passed as ‘data_in.’ The ‘sending’ signal is passed through
from the counter.
Finally, the FM0 module encodes the ‘in’ signal as per FM0,
sending according to the symbol period and only sending
when ‘sending’ is high. This is shown in Figures 24 and 25
respectively.
Fig. 24. Simulation of the FM0 module. The ‘period_clk’ is twice the
period of the system ‘clk.’ The ‘out’ signal encodes the ‘in’ signal in
FM0. The ‘sending’ signal is assumed to be high for this simulation.
The Demo module is a high-level block which simply
connects the sender and FM0 modules (Figure 25).
Fig. 25. Simulation of the Demo module. When ‘send_enable’ is first
set to high, the packet ‘data_in’ starts being sent bit-by-bit by the
sender module through ‘data_out.’ The FM0 module encodes
‘data_out’ through ‘fm_out,’ and only does so when ‘sending’ is high.
The hardware testing setup is shown in Figures 26 and 27. The
two switches correspond to the reset and send_enable signals.
The oscilloscope’s yellow signal displays the FM0 output.
Fig. 26. Hardware testing setup: the circuit. The top button is RESET
and the bottom button is SEND. The oscilloscope grounding clip and
probe can be seen on the left.
Fig. 27. Hardware testing setup: the oscilloscope. This oscilloscope
has multiple channels, enabling us to detect when the ‘send_enable’
signal is triggered and simultaneously see the FM0_OUT signal.
For the sake of testing, the pattern that’s being transmitted is 6
bits repeating: 001101.
Fig. 28. FM0 as seen on the oscilloscope. The yellow (1) signal is the
transmitted signal, and the blue (2) is the ‘send_enable’ signal.
To verify the timing (Figure 28), we used the cursors to see
that the BX-AX label equals 6.3000μs and the 1/|dX| label
equals 158.7kHz, both of which approximately equal 6.25μs
and 160 kHz, our desired symbol period and frequency.
The entire packet is 126 bits, so we should expect the packet
length to be . We can see in Figure26 .25μs 87.5μs1 × 6 = 7
29 that the BX-AX label properly equals 788.0μs.
10. Fig. 29. A whole FM0-encoded 126-bit packet. The yellow (1) signal is
the transmitted signal, and the blue (2) is the ‘send_enable’ signal.
The reason why the measurements are slightly off is that the
granularity at which the cursors move is relatively large:
scrolling one unit left or right yields a measurement slightly
under or over our expected value.
C. Downlink PIE Decoding
For the sake of efficiency, instead of creating a set of Verilog
modules which time and recognize the changes in the input to
interpret PIE symbols, we created a correlator. Because the
protocol only handles a set of four (sufficiently
distinguishable) reader commands, as an initial approach, we
can simply correlate the incoming signal with the already
known reader commands.
The prototype module takes an input which is the encoded PIE
command, and outputs a correlation value with respect to the
commands. This module looked specifically for the ReadF
command, which consists of frame_sync + 0b’00 0000 0000
0000 0000 0000 1111 (Figure 30).
Fig. 30. Decoding the ReadF PIE command.
The module samples every 10 clock cycles for simplicity/
efficiency, which makes it such that the number of samples in
a ReadF command is 650. The correlation is currently
implemented by XOR'ing the sampled input with the
command and counting the number of set bits. In the
simulation (Figure 30), all the 650 bits match up; hence why
the correlation value here is 650.
VI. Integrated Demo
To validate the wireless data transmission, we need to
integrate wireless RF connectivity between our FPGA and the
reader. To do so, we initially integrated the TinyFPGA with
the Wireless Identification Sensing Platform (WISP) (Sample
& Smith, 2013). The goal is to control the RF front-end of the
WISP with our FPGA. Later on, we want to build an
Integrated Circuit (IC) to add the RF front end to our
TinyFPGA. James Rosenthal created the following integrated
demo, shown in Figure 31, where the WISP - a device which
performs the passive, sensor-side backscatter communication -
was connected to the TinyFPGA. A level-shifter was required
in the WISP Rx path to convert the voltage levels.
Fig. 31. Diagram of test setup.
To initially validate the RF front-end of our WISP-FPGA
setup, James used an RF signal generator that could perform
pulse modulation by sending bursts (Figure 32).
Fig. 32. Signal generator setup. It is set to 915 MHz at 20 dBm, with
pulse modulation.
11. Fig. 33. Overall test setup. The TinyFPGA is connected to a WISP and
an oscilloscope. The signal generator is connected to a base station
which can emit the backscatter carrier wave.
Fig. 34. Oscilloscope showing test results.
The yellow signal in the oscilloscope (Figure 34) is the (FM0-
encoded) transmitted packet coming from the FPGA. Green is
the mock reader command coming in from the base station
(signal generator).
This demo shows two things. Firstly, we successfully used the
WISP’s RF front-end to receive a digital signal of the correct
amplitude in our FPGA. Secondly, the FPGA successfully
detected a change in the received signal - emulating a read
command - and outputted an FM0-encoded data stream -
emulating the implant data frame (Figure 33).
VII. Future Work
Several things still need to be done in order to fully integrate
and test all components of this system. After the device has
been robustly tested to work with the mock data, we will
transmit pre-recorded neural data and test the decoding
performance. Once that works thoroughly, we can move
forward with live neural decoding.
One important feature which would be necessary for
implantation in humans is ensuring the privacy and security of
transmitted neural signals. Researchers at the University of
Washington have already developed a threat model for BCIs,
and have theorized a BCI Anonymzier to prevent malicious
attackers from extracting private data and interfering with BCI
operation (Bonaci, Calo, & Chizeck, 2015). We could utilize
the threat model to develop a similar defense, adding
additional encryption and decryption layers into the protocol.
VIII. Conclusion
A BCSI is an effective solution for restoring movement in
patients with cervical SCI. We can consider the BCSI as
artificial neurons which promote a new neural pathway around
an injury in the cervical spinal cord which severed a previous
neural pathway: first, decoding one’s intention to move, then
encoding that intention as stimulation to promote muscle
movement. A wireless backscatter medium is an efficient
implementation of a BCSI. We’ve developed and tested
components of the hardware implementation of this protocol
on the sensor-side: the FPGA can successfully handle both the
uplink and downlink communication with the reader.
Ultimately, there are several layers of encoding and decoding
involved in the communication protocol of the wireless BCSI,
much like the lower-level mechanisms of neurons in neural
pathways.
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