This document provides an overview of a student project to build a multi-channel electroencephalography (EEG) brain-computer interface. The project aims to record EEG data from electrodes attached to a user's scalp using a microcontroller. The data will then be processed using machine learning to classify the user's thoughts and allow them to control a computer. Safety is the top priority given the medical nature. The project has multiple checkpoints and may aim to detect colors, mouse movement, or sleep apnea depending on progress.
1. The document describes the progress made on building an analog amplification circuit with a gain of 6,400 to amplify electroencephalography (EEG) signals as part of a brain-computer interface project.
2. Challenges faced include a damaged FTDI chip on their target board and not utilizing the full dynamic range of the analog-to-digital converter due to the biased input signal.
3. Future plans are to add circuitry to scale and bias the signal, develop the brain-computer interface to detect event-related potentials and allow selection of items, and potentially create a neurofeedback device if time does not allow finishing the brain-computer interface.
Biometrics: The passwords of the Future
National Institute of Science and Technology (NIST) defines Biometrics as automated method of identifying or authenticating an individual based on his/her physiological or behavioral characteristic.
The document summarizes the current progress, challenges, and future plans of a project to create a brain-computer interface using multi-channel electroencephalography. It discusses modifying the firmware to improve accuracy and save power, constructing an EEG helmet with electrodes in standard positions, observing beta and theta brain waves, and facing issues with the real-time kernel. It also outlines plans to finish the firmware, construct a solder board, connect LEDs and a piezo for frequency visualization, and continue tuning the analog circuit.
This document describes an intelligent power emulator module that allows testing of various power converters on a single integrated device. Key features of the proposed module include:
- Housing various converter circuits, driver circuits, protective devices, sensors, and a microcontroller on one module to save space compared to testing converters individually.
- Using a transformer with multiple outputs as the driver circuit and a rectifier IC to provide constant DC voltage to switches in the module.
- Incorporating a digital storage oscilloscope within the module to monitor and analyze output waveforms without occupying additional space.
- Implementing rotary switches and patch cords on the module to easily configure different converter circuit connections and test various operating conditions in a compact
This document discusses EEG signal background and real-time processing. It begins by describing different methods of measuring brain activity, including EEG. It then discusses the source of EEG signals and how they are generated by synaptic activity and summed across electrodes. The document outlines controlling alpha oscillations and using them in brain-computer interfaces. Finally, it discusses real-time processing and closed-loop systems, including buffering data, connecting different recording devices, and creating online analysis pipelines to generate control signals.
This document provides instructions for building music reactive multicolor LED lights that change color in response to the beat of music. The lights are made using an Arduino, RGB LED strip, audio input jack, and transistor circuit to amplify the Arduino signal. The 11 step instructions cover preparing an enclosure, soldering components, connecting the circuit to Arduino, adding an audio input, power, code upload, and using the finished project.
Robotics & Embedded IoT System Design [Day-2]Deepam Dubey
This document discusses a 4-week summer training program on electronics components. It covers topics like active components such as transistors and integrated circuits, sensors and actuators, different types of memories, and indicating devices. Analogies are provided to explain concepts of voltage, current, and resistance using a water tank. Different types of materials and their resistances are explained. Semiconductor devices like diodes, transistors, and integrated circuits are discussed along with examples. The document also talks about basic building blocks of a robot including sensors, actuators, control systems, power sources, and communication abilities. Specific sensors like IR proximity, sharp IR range finder, and position encoders are explained.
Arduino Workshop Day 1 Slides
Basics of Arduino - Introduction, Basics of Circuits, Signals & Electronics, LED Interfacing, Switch, Buzzer, LCD & Bluetooth Communication.
1. The document describes the progress made on building an analog amplification circuit with a gain of 6,400 to amplify electroencephalography (EEG) signals as part of a brain-computer interface project.
2. Challenges faced include a damaged FTDI chip on their target board and not utilizing the full dynamic range of the analog-to-digital converter due to the biased input signal.
3. Future plans are to add circuitry to scale and bias the signal, develop the brain-computer interface to detect event-related potentials and allow selection of items, and potentially create a neurofeedback device if time does not allow finishing the brain-computer interface.
Biometrics: The passwords of the Future
National Institute of Science and Technology (NIST) defines Biometrics as automated method of identifying or authenticating an individual based on his/her physiological or behavioral characteristic.
The document summarizes the current progress, challenges, and future plans of a project to create a brain-computer interface using multi-channel electroencephalography. It discusses modifying the firmware to improve accuracy and save power, constructing an EEG helmet with electrodes in standard positions, observing beta and theta brain waves, and facing issues with the real-time kernel. It also outlines plans to finish the firmware, construct a solder board, connect LEDs and a piezo for frequency visualization, and continue tuning the analog circuit.
This document describes an intelligent power emulator module that allows testing of various power converters on a single integrated device. Key features of the proposed module include:
- Housing various converter circuits, driver circuits, protective devices, sensors, and a microcontroller on one module to save space compared to testing converters individually.
- Using a transformer with multiple outputs as the driver circuit and a rectifier IC to provide constant DC voltage to switches in the module.
- Incorporating a digital storage oscilloscope within the module to monitor and analyze output waveforms without occupying additional space.
- Implementing rotary switches and patch cords on the module to easily configure different converter circuit connections and test various operating conditions in a compact
This document discusses EEG signal background and real-time processing. It begins by describing different methods of measuring brain activity, including EEG. It then discusses the source of EEG signals and how they are generated by synaptic activity and summed across electrodes. The document outlines controlling alpha oscillations and using them in brain-computer interfaces. Finally, it discusses real-time processing and closed-loop systems, including buffering data, connecting different recording devices, and creating online analysis pipelines to generate control signals.
This document provides instructions for building music reactive multicolor LED lights that change color in response to the beat of music. The lights are made using an Arduino, RGB LED strip, audio input jack, and transistor circuit to amplify the Arduino signal. The 11 step instructions cover preparing an enclosure, soldering components, connecting the circuit to Arduino, adding an audio input, power, code upload, and using the finished project.
Robotics & Embedded IoT System Design [Day-2]Deepam Dubey
This document discusses a 4-week summer training program on electronics components. It covers topics like active components such as transistors and integrated circuits, sensors and actuators, different types of memories, and indicating devices. Analogies are provided to explain concepts of voltage, current, and resistance using a water tank. Different types of materials and their resistances are explained. Semiconductor devices like diodes, transistors, and integrated circuits are discussed along with examples. The document also talks about basic building blocks of a robot including sensors, actuators, control systems, power sources, and communication abilities. Specific sensors like IR proximity, sharp IR range finder, and position encoders are explained.
Arduino Workshop Day 1 Slides
Basics of Arduino - Introduction, Basics of Circuits, Signals & Electronics, LED Interfacing, Switch, Buzzer, LCD & Bluetooth Communication.
PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...Md Kafiul Islam
This document summarizes an oral defense presentation for a PhD dissertation on artifact characterization, detection, and removal from neural signals. The presentation outlines the background on in-vivo neural signals and EEG, problems and motivation regarding artifacts corrupting signals, thesis objectives, literature review on existing artifact removal methods, contributions of the dissertation including artifact study and proposed removal algorithms, and plans for future work. The presentation aims to investigate artifacts in neural data, develop automated detection and removal without distorting signals, evaluate methods, and improve applications like epilepsy detection and brain-computer interfaces.
Arduino Workshop Day 2 - Advance Arduino & DIYVishnu
Arduino Workshop Day 2 - IR, Ultrasonic & Temperature - Humidity Sensor Interfacing & Do It Yourself - Line Follower, Light Follower & Obstacle Avoider.
This document outlines an Arduino workshop. It includes an overview of the agenda which involves introductions, checking equipment, experimentation time, and creating personal projects. It then details introducing participants and encouraging collaboration. A list of included parts in the kits is provided. Instructions are given for installing the Arduino software and development environment. Examples are shown for breadboard layouts and code for simple projects like blinking an LED and reading input from a button. Additional experiments suggested include using sensors, LCD displays, motors, and programming an RGB LED with a joystick. Sources for parts, tutorials, and inspiration are listed to encourage continued learning.
This document describes the design and testing of a two station aircraft intercom circuit. The circuit uses a summing amplifier to mix audio inputs from the pilot and co-pilot microphones. An audio amplifier then amplifies the mixed signal for the headsets. A comparator circuit ensures the pilot can hear their own voice by comparing the microphone input to a threshold voltage. The author prototypes the circuit on a breadboard, then builds it on stripboard. Extensive testing of each component and the full circuit is described. The completed intercom unit is installed in an aluminum enclosure with controls.
This document describes the design of an adaptive traffic light controller based on a custom protocol. The system uses an AT89S52 microcontroller, infrared sensors, LEDs, a 555 timer, and other components to automatically control traffic lights based on vehicle density. It summarizes the components used - including the microcontroller, infrared LEDs, 555 timer, photodiodes, and LED traffic lights. It also provides details on how these components work and are integrated into a circuit to automatically detect vehicles and adaptively control the traffic signals.
LEDs emit light when operated in a forward biased direction through a semiconductor chip. They convert electrical energy into light energy and are used as indicator lights. The chip has a PN junction that allows current to flow when voltage is applied, causing electrons to recombine with positive charges and emit photons of light. LEDs come in different colors depending on the semiconductor material and require a resistor and correct polarity to operate safely without burning out.
This document is a project report submitted to fulfill the requirements for a Bachelor of Science degree in Electronics. It describes the design and implementation of an automatic in/out indicator circuit with a doorbell. The circuit uses a touch plate that detects the presence or absence of an individual and automatically changes the display from "IN" to "OUT". It is designed to be simple, reliable, and easy to assemble to improve home and office technology. The report includes sections on the objectives, components, circuit diagram, operation, installation, and conclusions.
The document is an introduction guide to using Arduino microcontrollers. It describes that the Arduino is an open-source hardware platform used for building interactive objects and prototypes. The guide covers what is needed to set up an Arduino system, including the hardware components, software installation, and how to write basic programs to control an LED using the Arduino board.
The document discusses light emitting diodes (LEDs). It begins by stating the objectives of learning about LED design principles, relating semiconductor properties to the LED principle, and selecting appropriate materials for different LED types. It then provides background on LEDs, noting they are semiconductor p-n junctions that emit light through electroluminescence when forward biased. The document outlines key topics that will be covered including device configuration, materials requirements, selection, and issues. It poses questions for the reader on topics like LED construction, materials selection issues, band gap engineering, and examples of materials that emit different wavelengths of light. References and materials that will be provided are also listed.
The Arduino platform allows users to create interactive electronic objects by providing an open-source hardware and software environment. It consists of a microcontroller board and IDE that allows users to write code to control sensors, LEDs, motors and more. The Arduino is inexpensive, easy to use, and has a large community that shares tutorials and projects online. It is well suited for interactive art, design prototypes, and physical computing projects.
EEG artefacts arise from unwanted electrical activity from sources other than the brain, such as eye movements, muscle activity, and environmental noise. Identifying artefacts can be challenging as some resemble brain activity. Methods for removing artefacts include filtering, regression-based approaches, and independent component analysis, which transforms scalp channel data into spatially independent sources that may represent brain or non-brain activity. Careful inspection of component properties like scalp maps, time courses, and spectra is needed to classify them as representing brain activity or artefacts.
This document summarizes a project to design a printed circuit board (PCB) for a remote data acquisition node powered over an optical fiber. Key points:
- The goal was to design a PCB that uses power-over-fiber technology to supply power via laser light converted to electricity, replacing traditional copper cables and power supplies.
- The designed PCB includes components like a laser power converter, ADC, FPGA, and optical transmitter. It underwent several revisions to address issues in the prototype.
- The final PCB design is presented, but further testing is needed to fully address open issues like powering the board using just the laser power converter.
This document provides an introduction to using Arduino, an open-source physical computing platform. It describes Arduino as a microcontroller board and IDE that allows users to write software to control sensors and actuators. The document outlines the basic Arduino hardware components, software interface, and guides setting up the IDE. It recommends verifying the setup by running a sample "Blink" sketch to toggle an onboard LED.
This document provides an introduction to using Arduino boards. It discusses getting started with the Arduino IDE, programming basics like digital I/O and timing functions. Examples are provided to blink an LED, read a digital sensor, read an analog sensor with a potentiometer, and fade an LED using pulse width modulation. Terminology around bits, bytes and serial communication is also explained. The document aims to teach Arduino fundamentals and provide practice examples for learning.
This document provides an introduction to a class on microcontrollers with Arduino. It discusses controlling Arduino from a computer and vice versa using serial communication. It introduces servomotors and how to control their position with pulse width modulation signals from Arduino. Examples are provided for moving a servo across its full range, controlling a servo from serial input, and addressing timing issues with serial servo control. The document also covers using RGB LEDs with Arduino by controlling the pulse width modulation on three pins to mix colors. Further topics discussed include reading serial strings and potential future projects involving servos, serial communications, and piezo elements.
This document provides an overview of a voice control home appliance project. The project uses a voice module, microcontroller, and mobile phone to allow voice commands to control home appliances like fans and lights. The microcontroller receives voice commands via Bluetooth from the mobile phone and voice module. It then sends signals to relays connected to appliances to switch them on or off based on the command. The aim is to automatically control appliances in response to spoken commands.
Signal processing and filters for reg review ms niangleel
This document provides an overview of amplifier function and signal filtering used in sleep disorder technology. It defines key terms like differential amplifier, common mode rejection, low frequency filters, high frequency filters, and notch filters. It explains how these components work to amplify signals and reduce unwanted frequencies. It also discusses the differences between alternating current and direct current amplifiers, as well as the differences between gain and sensitivity. Basic concepts in electricity, signal processing, and calibration are outlined.
Non-Invasive point of care ECG signal detection and analytics for cardiac dis...gptshubham
This document describes a project to develop a portable, non-invasive device for point-of-care ECG monitoring and cardiac disease detection. The goals are to analyze existing ECG devices, develop a micropatterned electrode system, refine an ECG dataset, build and train a convolutional neural network model to classify heart rhythms, and create a prototype circuit. The model achieves 81.36% accuracy on the test data. Challenges include sourcing hardware components and refining the electrode interface. Future work involves completing the circuit prototype and integrating online prediction via a cloud-based model.
In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...Md Kafiul Islam
This document summarizes an oral defense presentation for a PhD dissertation on artifact characterization, detection, and removal from neural signals. The presentation outlines the background on in-vivo neural signals and EEG, problems and motivation regarding artifacts corrupting signals, thesis objectives, literature review on existing artifact removal methods, contributions of the dissertation including artifact study and proposed removal algorithms, and plans for future work. The presentation aims to investigate artifacts in neural data, develop automated detection and removal without distorting signals, evaluate methods, and improve applications like epilepsy detection and brain-computer interfaces.
Arduino Workshop Day 2 - Advance Arduino & DIYVishnu
Arduino Workshop Day 2 - IR, Ultrasonic & Temperature - Humidity Sensor Interfacing & Do It Yourself - Line Follower, Light Follower & Obstacle Avoider.
This document outlines an Arduino workshop. It includes an overview of the agenda which involves introductions, checking equipment, experimentation time, and creating personal projects. It then details introducing participants and encouraging collaboration. A list of included parts in the kits is provided. Instructions are given for installing the Arduino software and development environment. Examples are shown for breadboard layouts and code for simple projects like blinking an LED and reading input from a button. Additional experiments suggested include using sensors, LCD displays, motors, and programming an RGB LED with a joystick. Sources for parts, tutorials, and inspiration are listed to encourage continued learning.
This document describes the design and testing of a two station aircraft intercom circuit. The circuit uses a summing amplifier to mix audio inputs from the pilot and co-pilot microphones. An audio amplifier then amplifies the mixed signal for the headsets. A comparator circuit ensures the pilot can hear their own voice by comparing the microphone input to a threshold voltage. The author prototypes the circuit on a breadboard, then builds it on stripboard. Extensive testing of each component and the full circuit is described. The completed intercom unit is installed in an aluminum enclosure with controls.
This document describes the design of an adaptive traffic light controller based on a custom protocol. The system uses an AT89S52 microcontroller, infrared sensors, LEDs, a 555 timer, and other components to automatically control traffic lights based on vehicle density. It summarizes the components used - including the microcontroller, infrared LEDs, 555 timer, photodiodes, and LED traffic lights. It also provides details on how these components work and are integrated into a circuit to automatically detect vehicles and adaptively control the traffic signals.
LEDs emit light when operated in a forward biased direction through a semiconductor chip. They convert electrical energy into light energy and are used as indicator lights. The chip has a PN junction that allows current to flow when voltage is applied, causing electrons to recombine with positive charges and emit photons of light. LEDs come in different colors depending on the semiconductor material and require a resistor and correct polarity to operate safely without burning out.
This document is a project report submitted to fulfill the requirements for a Bachelor of Science degree in Electronics. It describes the design and implementation of an automatic in/out indicator circuit with a doorbell. The circuit uses a touch plate that detects the presence or absence of an individual and automatically changes the display from "IN" to "OUT". It is designed to be simple, reliable, and easy to assemble to improve home and office technology. The report includes sections on the objectives, components, circuit diagram, operation, installation, and conclusions.
The document is an introduction guide to using Arduino microcontrollers. It describes that the Arduino is an open-source hardware platform used for building interactive objects and prototypes. The guide covers what is needed to set up an Arduino system, including the hardware components, software installation, and how to write basic programs to control an LED using the Arduino board.
The document discusses light emitting diodes (LEDs). It begins by stating the objectives of learning about LED design principles, relating semiconductor properties to the LED principle, and selecting appropriate materials for different LED types. It then provides background on LEDs, noting they are semiconductor p-n junctions that emit light through electroluminescence when forward biased. The document outlines key topics that will be covered including device configuration, materials requirements, selection, and issues. It poses questions for the reader on topics like LED construction, materials selection issues, band gap engineering, and examples of materials that emit different wavelengths of light. References and materials that will be provided are also listed.
The Arduino platform allows users to create interactive electronic objects by providing an open-source hardware and software environment. It consists of a microcontroller board and IDE that allows users to write code to control sensors, LEDs, motors and more. The Arduino is inexpensive, easy to use, and has a large community that shares tutorials and projects online. It is well suited for interactive art, design prototypes, and physical computing projects.
EEG artefacts arise from unwanted electrical activity from sources other than the brain, such as eye movements, muscle activity, and environmental noise. Identifying artefacts can be challenging as some resemble brain activity. Methods for removing artefacts include filtering, regression-based approaches, and independent component analysis, which transforms scalp channel data into spatially independent sources that may represent brain or non-brain activity. Careful inspection of component properties like scalp maps, time courses, and spectra is needed to classify them as representing brain activity or artefacts.
This document summarizes a project to design a printed circuit board (PCB) for a remote data acquisition node powered over an optical fiber. Key points:
- The goal was to design a PCB that uses power-over-fiber technology to supply power via laser light converted to electricity, replacing traditional copper cables and power supplies.
- The designed PCB includes components like a laser power converter, ADC, FPGA, and optical transmitter. It underwent several revisions to address issues in the prototype.
- The final PCB design is presented, but further testing is needed to fully address open issues like powering the board using just the laser power converter.
This document provides an introduction to using Arduino, an open-source physical computing platform. It describes Arduino as a microcontroller board and IDE that allows users to write software to control sensors and actuators. The document outlines the basic Arduino hardware components, software interface, and guides setting up the IDE. It recommends verifying the setup by running a sample "Blink" sketch to toggle an onboard LED.
This document provides an introduction to using Arduino boards. It discusses getting started with the Arduino IDE, programming basics like digital I/O and timing functions. Examples are provided to blink an LED, read a digital sensor, read an analog sensor with a potentiometer, and fade an LED using pulse width modulation. Terminology around bits, bytes and serial communication is also explained. The document aims to teach Arduino fundamentals and provide practice examples for learning.
This document provides an introduction to a class on microcontrollers with Arduino. It discusses controlling Arduino from a computer and vice versa using serial communication. It introduces servomotors and how to control their position with pulse width modulation signals from Arduino. Examples are provided for moving a servo across its full range, controlling a servo from serial input, and addressing timing issues with serial servo control. The document also covers using RGB LEDs with Arduino by controlling the pulse width modulation on three pins to mix colors. Further topics discussed include reading serial strings and potential future projects involving servos, serial communications, and piezo elements.
This document provides an overview of a voice control home appliance project. The project uses a voice module, microcontroller, and mobile phone to allow voice commands to control home appliances like fans and lights. The microcontroller receives voice commands via Bluetooth from the mobile phone and voice module. It then sends signals to relays connected to appliances to switch them on or off based on the command. The aim is to automatically control appliances in response to spoken commands.
Signal processing and filters for reg review ms niangleel
This document provides an overview of amplifier function and signal filtering used in sleep disorder technology. It defines key terms like differential amplifier, common mode rejection, low frequency filters, high frequency filters, and notch filters. It explains how these components work to amplify signals and reduce unwanted frequencies. It also discusses the differences between alternating current and direct current amplifiers, as well as the differences between gain and sensitivity. Basic concepts in electricity, signal processing, and calibration are outlined.
Non-Invasive point of care ECG signal detection and analytics for cardiac dis...gptshubham
This document describes a project to develop a portable, non-invasive device for point-of-care ECG monitoring and cardiac disease detection. The goals are to analyze existing ECG devices, develop a micropatterned electrode system, refine an ECG dataset, build and train a convolutional neural network model to classify heart rhythms, and create a prototype circuit. The model achieves 81.36% accuracy on the test data. Challenges include sourcing hardware components and refining the electrode interface. Future work involves completing the circuit prototype and integrating online prediction via a cloud-based model.
In this paper designing of a battery operated portable single channel electroencephalography (EEG) signal acquisition system is presented. The advancement in the field of hardware and signal processing tools made possible the utilization of brain waves for the communication between humans and computers. The work presented in this paper can be said as a part of bigger task, whose purpose is to classify EEG signals belonging to a varied set of mental activities in a real time Brain Computer Interface (BCI). Keeping in mind the end goal is to research the possibility of utilizing diverse mental tasks as a wide correspondence channel in the middle of individuals and PCs. This work deals with EEG based BCI, intent on the designing of portable EEG signal acquisition system. The EEG signal acquisition system with a cut off frequency band of 1-100 Hz is designed by the use of integrated circuits such as low power instrumentation amplifier INA128P, high gain operational amplifiers LM358P. Initially the amplified EEG signals are digitized and transmitted to a PC by a data acquisition module NI DAQ (SCXI-1302). These transmitted signals are then viewed and stored in the LAB VIEW environment. From a varied set of experimental observation it can be said that the system can be implemented in the acquisition of EEG signals and can stores the data to a PC efficiently and the system would be of advantage to the use of EEG signal acquisition or even BCI application by adapting signal processing tools.
This document provides an introduction to the first experiment on basic logic gates. The experiment aims to study the operation principles of AND, OR, INVERTER, NAND, and NOR gates through their truth tables, logic diagrams, and Boolean algebra representations. The theory section defines logic-1 and logic-0 voltage levels and explains that logic gates are the basic building blocks of digital circuits. It then discusses the operation and truth tables of each basic 2-input logic gate - AND, OR, INVERTER, NAND, and NOR. The experiment will examine these logic gates using integrated circuits to better understand their functions and applications in digital electronics.
Here an electronic circuit breaker is designed which is based on the current sensing across a series element typically a CT (current Transformer). The current sensed which is compared against the preset value proportional to the voltage by comparator which is inbuilt in arduino to generate an output that drives a relay through a MOSFET to trip the load very fastly.
The concept of electronic circuit breaker came into focus realizing that the conventional circuit breakers such as MCBs take longer time to trip.
The steadily increasing population has more demand and consumption of electric energy in the market as raised and that of equipment’s used like electrical and electronics are also costlier
So to protect the electrical system from overload or short circuit here is one possibility, which is by ultrafast acting electronic circuit breaker
The electronic circuit breaker is based on the voltage drop across a series element proportional to the load current, typically a low -value resistor.
This document contains information about experiments to be performed on logic gates and flip flops using integrated circuits. It includes:
- An introduction to the breadboard and safety procedures for building circuits.
- Details of common logic gate ICs such as AND, OR, NOT, NAND and NOR gates.
- Details of flip flop ICs such as D and JK flip flops.
- The aim and apparatus required for an experiment to verify the truth tables of logic gates and flip flop operations.
- Descriptions and truth tables of logic gate and flip flop operations.
This document describes a wireless electronic stethoscope based on Zigbee technology. It consists of a transmitter and receiver. The transmitter uses a microphone to pick up heart sounds, processes and samples the sounds, and sends them wirelessly via Zigbee module. The receiver uses a Zigbee module to receive the signals, converts them to analog via a DAC, and amplifies the sounds via a power amplifier. The system allows wireless auscultation by multiple doctors and storage of heart sounds on a PC for analysis or telemedicine consultations.
The research of_portable_ecg_monitoring_system_with_usb_host_interfaceArhamSheikh1
This document describes a portable ECG monitoring system with a USB host interface. The system uses a microprocessor and USB host interface chip to collect and store ECG signals. It has a circuit to acquire high-quality ECG data and amplify low ECG voltages. Software implements USB protocols to recognize and configure USB drives, identify file systems, and write collected ECG data to the drive in real-time using bulk transfer methods. The portable design with a USB host allows connection to additional modules as needed.
An ECG-on-Chip for Wearable Cardiac Monitoring Devices ecgpapers
This paper describes a highly integrated, low power chip solution for ECG signal processing in wearable
devices. The chip contains an instrumentation amplifier with programmable gain, a band-pass filter, a 12-bit
SAR ADC, a novel QRS detector, 8K on-chip SRAM, and relevant control circuitry and CPU interfaces. The
analog front end circuits accurately senses and digitizes the raw ECG signal, which is then filtered to extract the
QRS. The sampling frequency used is 256 Hz. ECG samples are buffered locally on an asynchronous FIFO and
is read out using a faster clock, as and when it is required by the host CPU via an SPI interface. The chip was
designed and implemented in 0.35ȝm standard CMOS process. The analog core operates at 1V while the digital
circuits and SRAM operate at 3.3V. The chip total core area is 5.74 mm 2 and consumes 9.6ȝW. Small size and
low power consumption make this design suitable for usage in wearable heart monitoring devices.
This document provides instructions and explanations for 100 transistor circuits, many using integrated circuits. It begins with an introduction explaining the differences between analog and digital signals and circuits. It then discusses transistors and equivalent types that can be substituted. The contents section lists 100 circuit ideas ranging from amplifiers and power supplies to detectors and oscillators. Descriptions and diagrams are provided for several circuits including a 3-phase sine wave generator, transformerless power supply, and connecting LEDs directly to mains power. The document aims to teach electronics through building practical circuits.
1) The document describes the design of an embedded wireless ECG system using IEEE 802.11G for wireless transmission. It acquires ECG signals from electrodes, amplifies and filters the signals, digitizes them using a PIC microcontroller, and transmits the data wirelessly.
2) The received data is processed using MATLAB to remove power line interference through an EMI filter designed using a core algorithm. This allows specialists to remotely monitor patients' ECG signals.
3) The EMI filter effectively tracks variations in interference frequency and amplitude to extract the power line signal from the ECG, improving over existing techniques. This allows clean ECG signals to be obtained with minimal computational resources.
The document describes a system to identify the location of faults in underground electrical cables using an Internet of Things (IoT) platform. The system uses resistors to represent the underground cable and detects changes in voltage across the resistors to determine the location of short circuits. When a short circuit occurs, the voltage data is sent to a microcontroller and IoT module to display the fault location. The system allows utilities to locate cable faults without disconnecting the cable from the grid.
The document discusses brain computer interfaces (BCI), which allow humans to control devices with their thoughts by detecting brain signals. It covers the history and principles of BCI, describing invasive and non-invasive techniques for acquiring brain signals like EEG, ECoG, and fMRI. Applications discussed include using BCI for gaming, robotics, medicine, and military purposes. Challenges include training users and improving signal acquisition, but proposed solutions involve selective attention strategies and signal preprocessing techniques. In conclusion, BCI technology continues to improve detection methods and provide new options for human-machine interaction.
Scaling Down Instrumentation Deploying Analog Mixed Signal TechnologyShivaprasad Tilekar
This document discusses the use of programmable system on chip (PSoC) technology for instrumentation and embedded system design. It provides an overview of PSoC features such as programmable analog and digital blocks, processing cores, and development tools. Examples are given of using PSoCs for applications like temperature measurement, pH measurement, electrical conductivity measurement, and humidity sensing. The document highlights the benefits of PSoCs compared to traditional ASICs such as reconfigurability, lower development costs, and good performance. It also lists several publications by the author applying PSoC technology to measurement of various physical parameters.
I have taken efforts in this project. However, it would not have been possible without the kind support and help of many individuals and organizations. I would like to extend my sincere thanks to all of them. I am highly indebted to Mr. I.P. CHANDRA, Mrs. NISHA DHIMAN, and Miss. PRIYANKA SINGH RANA for their guidance and constant supervision as well as for providing necessary information regarding the project & also for her support in completing the project. I would like to express my gratitude towards my parents & member of ROORKEE COLLEGE OF ENGINEERING, ROORKEE for their kind co-operation and the encouragement which helps me in the completion of this project.
The document provides an overview of various electronics-related topics featured in Elektor Magazine's March/April 2020 issue. It highlights two LoRa radio protocol projects: the Elektor LoRa Node, a versatile and configurable long-range remote control module, and an ESP32-based doorbell that transmits notifications via Telegram. It also mentions a Meadow F7 board for .NET development, reviews of environmental sensor and oscilloscope boards, and upcoming coverage of AI and other technologies at the Embedded World exhibition.
Jawar Singh discusses brain-inspired computing architectures. Traditional computing is inefficient compared to the human brain, which uses only 20W of power. Brain-inspired systems emulate the brain's neuronal and synaptic functions using simplified models that are efficient and scalable. IBM's TrueNorth chip demonstrates this approach, emulating neurons using 104 transistors while consuming only 70mW. Intel's Loihi chip also shows efficiency, evaluating applications using under 1W versus tens of kW for standard CPUs. Silicon models have been developed for neurons and synapses that could underpin brain-inspired hardware.
The document provides an overview and technical reference for the Analog Discovery, a multi-function instrument developed by Digilent to measure, record, and generate analog and digital signals. Key features include:
- Two channel oscilloscope, function generator, logic analyzer, and pattern generator controlled via a PC application.
- Core components include an FPGA, ADC, DAC, and precision clock generator.
- Block diagrams and schematics are presented to describe the analog input, output, reference, and clock circuits. Specifications for components like the op amps, switches, and converters are provided.
- The document is intended as a reference for the electrical functions and limitations of the Analog Discovery hardware.
Bluetooth based home appliances controlPROJECTRONICS
This document describes a Bluetooth-based home appliance control system that allows appliances to be operated remotely using a Bluetooth-enabled device like a smartphone. The system uses a microcontroller interfaced with a Bluetooth module to receive commands from a mobile app and control electrical loads accordingly. It consists of a power supply, DTMF decoder to receive signals from Bluetooth, motor driver, solid state relays, and other circuits. The system was designed and tested successfully in the lab to allow remote control of appliances in a way that helps elderly or disabled people. Potential future expansions are also discussed.
The game fenestra involves guiding an old man through puzzles by having him travel between two worlds - a dark modern city and a bright outdoor setting - whenever he dies. Players must use objects and layouts in both worlds to reach the exit door of each level. Puzzles may involve choosing where to die, pushing boxes, activating buttons and switches, carrying keys between worlds, and timing deaths with events. The overarching goal is to progress the story of the protagonist by beating levels, with the challenge being to figure out the tasks needed to access each exit.
Lost Pigeon Studios developed a puzzle game taking place in two parallel worlds with a narrative that motivates players and provokes thought. They exceeded their goals for puzzles and mechanics but the narrative was not as integrated as hoped. Their visual and sound design exceeded expectations by creating an immersive experience. Development challenges included allocating time properly, producing a large number of art assets, and preventing feature creep. They learned to be more open, flexible with time management, and set more realistic goals.
Fenestra is a 2D puzzle platformer that uses death as a gameplay mechanic. Players control an old man who awakens in two parallel worlds and must solve physics puzzles by manipulating his environment and finding ways to die in order to overcome obstacles and progress between the worlds. The game explores themes of mortality and permanence through its mature narrative and challenging puzzles that require reasoning through multiple realities to unlock doors and progress through each level using items and switches that persist across worlds.
The beta release of Fenestra added new levels, puzzles, art assets, and visual effects. The team was productive but needs to focus on content generation for the full release. Goals for the final release include cohesive gameplay, puzzles, and story. It will be successful if a player can complete all puzzles by understanding the core mechanics with no bugs. Risks include distraction from content or unsolvable levels, which may require a reset feature. Each team member's tasks for the final release are outlined.
The document provides an introduction and instructions for a puzzle game where players guide an old man through two worlds as he journeys through his memories to find meaning in his troubled past. Players must use the man's ability to travel between worlds whenever he dies to solve puzzles spanning both worlds, enter doors to progress to new levels, and approach memory markers to uncover scenes from his life. The goal is to discover the truth behind the man's mysterious past.
Fenestra is a puzzle game where the player controls a man who travels between two worlds when he dies. The player must solve puzzles by bypassing obstacles, activating buttons, and unlocking doors to progress through levels and learn more about the man's troubled past. The game has a level editor that allows designers to create and modify levels by adding and editing entities, scripts, and triggers.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document describes the authors' entry for the 2009 CS 2110 coding competition. Their goal was to emphasize performance while guaranteeing accurate results. They implemented caching, used Graphviz to generate phylogenomic graphs, developed a mirror index algorithm to find the best root animal, added multithreading to parallelize tasks, implemented Prim's algorithm using a Fibonacci heap, and thoroughly tested their work. While some ambitious ideas like 3D visualization were not completed, they were overall pleased with their successful project.
This document contains 5 problems related to motor control using a PID controller. It discusses using a diode to short current spikes when a motor is turned off, how hysteresis works in a comparator circuit, the tasks used in a TRT controller, shared variables between tasks, and setting the timer period to measure motor rotations at 1000 RPM.
The document summarizes a project to create an electronic craps game using an MSP430 microcontroller. Key aspects include:
- Dice rolls are displayed using LEDs arranged in dice shapes and controlled through Charlie-plexing.
- The game logic is represented as a finite state machine with states like "come out" and "point".
- A pseudo-random number generator determines dice rolls.
- Audio feedback is provided by a piezoelectric speaker playing songs based on note frequencies.
The document proposes creating a playable Craps game using an MSP430 microcontroller board. LEDs arranged in dice shapes will display dice rolls, and the microcontroller will act as the dice roller. A pseudo-random number generator using the on-chip temperature sensor will determine dice values. Components needed include LEDs in various colors and resistors to limit current. Optional additions could include audio feedback via a piezo speaker or an LED to indicate the game's current phase.
This document does not contain any text to summarize. It only contains names without any additional context or information. In 3 sentences or less, a summary cannot be provided as there is no information given about the names listed or what they may refer to.
The document describes a 3D car driving simulation implemented in C++ using various libraries. It focuses on realistic rigid body physics simulation using techniques like Euler integration, collision detection and response, and constraint solving. The car physics model accounts for longitudinal forces from the engine and resistance, as well as lateral forces from a Pacejka tire model. Additional areas of potential improvement are also outlined.
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This document proposes a 3D car driving simulation implemented in C++ using various libraries. It will use realistic rigid body physics with features like tire modeling, suspension, gearing and longitudinal/lateral forces. The simulation will start with basic car physics and progressively incorporate more advanced models. It provides details on reference materials, the project plan and potential extensions if time allows.
This document discusses rendering techniques for car simulations including per pixel lighting, bump mapping using normal textures, reflection mapping using a cube around the car, HDR lighting using lighting beyond 0-1 range, and motion blurring based on pixel movement.
This document describes a semester project involving rigid body sound synthesis. The project uses modal synthesis in the frequency domain to generate contact sounds based on forces from a rigid body simulation. It utilizes various math libraries like Armadillo for linear algebra and SVDLIBC for sparse matrix decompositions. The simulation models rigid bodies using finite elements, computes their vibration modes, and plays back the resulting sounds using SDL audio. Key aspects covered include matrix formation, material parameters, modal analysis calculations, and audio playback implementation.
1. Charles Moyes (cwm55) and Mengxiang Jiang (mj294)
Cornell University ECE 4760 Final Project:
Brain-Computer Interface using Multi-Channel
Electroencephalography
Project Overview
The goal of the project is to build a multi-channel electroencephalography (EEG) Brain-Computer Inter-
face (BCI). At a high level, the project will record and collect EEG data from the user using electrodes
attached to the patient’s scalp. The data will then be processed by a machine learning algorithm such as
a feed-forward artificial neural network (ANN) which will attempt to classify the data. We plan on either
trying to discover which color the user is thinking of or allowing the user to control a mouse pointer using
their thoughts. Because of the nature of the machine learning algorithms we will be using, an initial training
period will be required where the user is prompted to “think of” or “imagine” colors and mouse movement
directions. The EEG data corresponding to the user’s thoughts will then be used to train the neural network
via the back-propagation method. From there, the user will be able to control the system using their mind:
Because we feel that this project is fairly ambitious, there will be several “checkpoints” or stopping
points, in case we run out of time or encounter difficulties. The first goal will be to collect EEG data from
the user using the microcontroller’s analog-to-digital (ADC) converter. The analog circuitry involved will
consist of a three-stage amplification circuit with an instrumentation amplifier, an operation amplifier, and
a filter. The filter will consist of a high-pass filter with a cut-off frequency very close to zero to remove DC
offsets from the signal, along with a low-pass filter with a cut-off frequency of around 60 Hz for anti-aliasing
and possibly a 60 Hz notch filter to remove AC power noise induced in the user’s body. We ordered low-cost
EEG electrodes from a Chinese medical supply company on eBay for around $20, but we may resort to
home-made electrodes constructed from metal guitar picks borrowed from a friend who is a musician and an
old baseball cap if the budget does not permit us to use the medical-grade electrodes. Another note is that
a so-called “right-leg driver” connected to the user’s right leg will be necessary to cancel out common-mode
noise from the user’s body. This driver will be connected to the instrumentation amplifier to take advantage
of its common-mode rejection functionality. The main challenge will be amplifying a signal from the user’s
scalp on the order of microvolts, while filtering out the vast sources of noise.
This data could then be presented on a graphical pixel LCD attached to the microcontroller, or it could
be transmitted to a computer over USB serial UART. However, for now we will be content with merely
collecting the EEG data from the user and sending it to MATLAB over UART. This will require some C
code to be written for the microcontroller that will read the ADC input, format the data in Modular EEG
packet format, and send it to the PC over the serial port. Once we have a working EEG (this is one “check-
point”), we would like to add as many channels (up to 16, ideally) as our budget permits. We have found
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2. an instrumentation amplifier and Besselworth filter IC from Maxim IC and cheap op amps that we will use
for this project. A possible time-permitting feature is the addition of a Bluetooth system-on-chip wireless
transceiver (TI has one that we could sample). This would allow the user to control their smartphone or
Bluetooth-enabled laptop using the BCI, but it would also impact our budget significantly.
The next checkpoint would be processing the collected data. We have had significant coursework in
artificial intelligence (AI) and have been studying papers on diagnosing sleep apnea and processing EEG
data in the context of brain-computer interfaces throughout the semester. We cannot guarantee good results
with the latter since we have not done anything like it before, so if we are unable to obtain good results,
then we will merely determine the patient’s level of emotional arousal based on the EEG waveforms using
their frequency spectra. An example follows:
However, we would definitely like to take time to experiment, namely with ANN’s so that we can attempt
to classify the user’s thoughts based on their brainwaves. Another simpler possibility would be detecting
EEG arousals in users that have sleep apnea. This is fairly simple to do given that one has a real-time EEG
data source, and the details have been outlined in several IEEE papers. The goal would be to have a user
think of a color (e.g. red, blue, or green) and then have the ANN classify which color the user is thinking of
from the received EEG data. Yet another possibility is having them thinking of cardinal movement directions
and then using the EEG data to move the mouse pointer. We feel that this is significantly more advanced,
however, than the color-detection process. At this point, we would be very satisfied with our project and we
would be more than happy to call it complete.
Relevant Standards
We will follow the Modular EEG serial packet standard for transmitting EEG data over UART. Moreover,
serial communications will follow the RS232/USB standards. Another consideration is the IEC601 standard.
IEC601 is a medical safety standard for medical devices that ensures that they are safe for patient use.
Unfortunately, testing for IEC601 compliance is very much out-of-budget. Nevertheless, we discuss the
many safety considerations that we will absolutely adhere by in the Societal Impact of Project section
of this write-up. If we opt to use Bluetooth as a wireless communication interface for this project, we will
use the Texas Instruments CC2560 single-chip Bluetooth IC, which complies with FCC standards.
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3. Tentative Parts List
A tentative parts list follows:
For Each Analog EEG Channel:
Part Cost
MAX4194 - instrumentation amplifier IC SAMPLE
MAX7401 - 8th order Bessel filter IC SAMPLE
AD8605 - op amp IC SAMPLE
TL062CP - op amp IC 2*$0.26/channel (Digikey)
Filter capacitors and resistors LAB/CHEAP
2K Potentiometer LAB/CHEAP
4x NPN and PNP transistors LAB/CHEAP
EEG electrodes:
Part Cost
CONTEC ten pcs silver plated electrodes (budget-permitting) $20 (eBay)
OR OR
Metal Guitar Picks and Headband ALREADY OWN
RS232 isolation:
Part Cost
6N137 optoisolator (or equivalent) 2*$1.55 (1 if only using TX) (Digikey)
2x NPN transistors LAB/CHEAP
Unavoidable Lab Costs Incurred:
Part Cost
White Board OR Solder Board (Time-Permitting) $6 OR $2.50
4x AA Batteries ALREADY OWN
4 AA Battery Holder $1.33 (Digikey)
Mega644 Microcontroller $8
SIP Header Sockets/DIP sockets To Be Determined (TBD)
AT Mega 644 Target Board LAB
Extra features:
Part Cost
Graphical LCD (budget-permitting) $15 (SparkFun)
CC2560 - TI Single-Chip Bluetooth SAMPLE (time-permitting)
Societal Impact of Project/Safety Features
Because this is a device attached to a patient’s brain, safety is our utmost priority. The microcontroller
will only be powered by four 1.5V AA batteries, rather than through an AC power supply connected to
mains. Moreover, serial communication to a PC over USB will be isolated from the USB using optocouplers,
which we will test extensively to ensure that both ground loops are separated. Only laptops running off of
battery power supplies (no AC adapters connected) will ever be connected to the microcontroller over USB.
As a corollary, the microcontroller will never be connected to a user’s head while the programmer cable is
connected to a PC. Lastly, the EEG circuitry itself will have transistors connected directly to the electrodes
(in between the electrodes and the analog circuitry) to act as a user current/ESD protection mechanism.
We promise that 120 VAC power will never be connected to this project directly or indirectly. As a result,
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4. users will never be allowed to touch any other electrical devices while wearing the EEG helmet. We will take
safety very seriously throughout the development of the project.
This project will have great societal impact because it is designed for users with special needs. Using
brain-computer interfaces, users with special needs will be able to interact in computers in ways that were
not previously possible. Patients with sleep apnea will be able to collect and analyze their own EEG data
while asleep without having to participate in expensive overnight sleep studies at hospitals. They will be
able to see their data; rather than being shielded from it by a medical doctor. Because our budget is less
than $75, they will be able to do this at a very low cost. We are strongly considering releasing our schematics
and source code on the Internet free-of-charge under the Creative Commons license in order to give back to
society.
Project Block Diagram
An overall block diagram of the project follows:
EEG High-Pass High-Pass
Electrodes Filter Filter ADC
ESD Discharge
Instrumentation Operational Besselworth AT Mega 644
Protection
Amplifier Amplifier Filter Microcontroller
Circuit
User’s
Head
UART
EEG
Electrodes Right-Leg
Driver
ELECTRICALLY ISOLATED LINE:
Real-Time FFT
On-Screen with
Battery-Powered FTDI
Classification Artificial Neural
Laptop PC USB RS232
Output to User Network
in MATLAB
Tentative Schematics
NOTE: These tentative schematics are highly subject to change throughout the development of our project!
Analog Amplification Circuit
4
5. Opto-isolated UART Serial
As mandated by our safety procedures, we will electrically isolate the UART from the USB. The isolated
UART will use high-bandwidth 6N137 opto-couplers from Fairchild Semiconductor as follows:
It will interface with the UART Pin D.0 and Pin D.1 from the microcontroller. Note that we may have
to add NPN transistors to invert the output as specified in the 6N137 datasheet if we cannot figure out a
way to invert the UART output in software. The TX and RX lines will then connect to the FT232RL FTDI
chip, which will be connected to USB GND and USB 5V for power. Unlike Open EEG’s Modular EEG project,
we opted to use 6N137 optoisolators with USB rather than 6N139’s with RS232 using a MAX232 IC.
Code Overview
Sample code for reading the ADC values and transmitting them to MATLAB over the UART follows. This
code will be extended to support multiple ADC channels and output to a graphical LCD. Note that the
following code listing has not been tested yet and so we cannot guarantee code correctness:
1 # include < avr / io .h >
# include < avr / interrupt .h >
3 # include < avr / pgmspace .h >
# include < avr / eeprom .h >
5 # include < stdio .h >
# include < stdlib .h >
7 # include < string .h >
# include " lcd_lib . h "
9 # include < util / delay .h >
11 // UART file descriptor
// putchar and getchar are in uart . c
13 FILE uart_str = F D E V _ S E T U P _ S T R E A M ( uart_putchar , uart_getchar , _FDE V_SETUP_ RW ) ;
15 // ADC variables
volatile int Ain , AinLow ;
17 # define ADC_BITS 10
19 // ADC ISR
ISR ( ADC_vect )
5
6. 21 {
AinLow = ( int ) ADCL ;
23 Ain = ( int ) ADCH *256;
Ain = Ain + AinLow ;
25
fprintf ( stdout , " % d n r " , Ain ) ;
27
ADCSRA |= (1 < < ADSC ) ; // start another ADC conversion
29 }
31 void main ()
{
33 // init the A to D converter
// channel zero / left adj / EXTERNAL Aref
35 // !!! DO NOT CONNECT Aref jumper !!!!
// Need capacitor at AREF pin and AVCC set to VCC
37 ADMUX = (1 << REFS0 ) ;
39 // enable ADC and set prescaler to 1/128*16 MHz =125 ,000
// and clear interupt enable
41 // and start a conversion
ADCSRA = (1 < < ADEN ) | (1 < < ADIE ) + 7;
43
// sleep register
45 SMCR = (1 << SM0 ) ; // ADC sleep
47 // init the UART -- uart_init () is in uart . c
uart_init () ;
49 stdout = stdin = stderr = & uart_str ;
fprintf ( stdout , " Starting ADC demo ... n r " ) ;
51
sei () ; // start interrupts
53
// main loop
55 while (1)
{
57 sleep_cpu () ;
}
59 }
Listing 1: C Source Code Listing
References
[1] Openeeg project. http://http://openeeg.sourceforge.net/, 2009.
[2] Homebrew do-it-yourself eeg, ekg, and emg. http://sites.google.com/site/chipstein/home-page, 2011.
[3] F Lotte. A review of classication algorithms for eeg-based braincomputer interfaces. Journal of Neural
Engineering, 2007.
[4] G. Matsuoka, T. Sugi, F. Kawana, and M. Nakamura. Automatic detection of apnea and eeg arousals
for sleep apnea syndrome. In ICCAS-SICE, 2009, pages 4651 –4654, aug. 2009.
[5] Hazrati MKh and Efranian A. An online eeg-based brain-computer interface for controlling hand grasp
using an adaptive probabilistic neural network. PubMed, 2010.
[6] Jorge Baztarrica Ochoa. EEG Signal Classification for Brain Computer Interface Applications. PhD
thesis, Ecole Polytechnique Federale de Lausanne, 2002.
[7] Olson. Eeg page. http://www.pkfamily.com/users/solson/eeg/eeg.html, 2007.
[8] Kouhyar Tavakolian, Faratash Vasefi, Kaveh Naziripour, and Siamak Rezaei. Mental task classifica-
tion for brain computer interface applications. In First Canadian Student Conference on Biomedical
Programming.
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