A wireless system for multi-channel transmission of EEG Signals

A wireless system for multi-channel transmission of EEG Signals by Bin YU A thesis submitted in partial fulfillment of the requirement for the degree of Master of Science (Electrical Engineering) at the UNIVERSITY OF WISCONSIN-MADISON 2010 i This thesis was approved by Professor Nader Behdad Assistant Professor, Electrical and Computer Engineering University of Wisconsin – Madison Signature : Date : Name : Nader Behdad i a Abstract A miniaturized wireless data acquisition and real-time signal analysis system for monitoring and analysis multi-channel EEG signal is presented The system includes: multichannel EEG probes, second order 60Hz band-stop filters, instrumental differential amplifiers with 100dB CMRR, 12bit resolution analog to digital converters, low power consumption microcontrollers, Zigbee wireless point to point communication modules, serial port communication module, graphic user interface software and digital signal processing toolbox Key performance includes: long range communication (50 meters), miniaturized transmitter unit (40mm*40mm*10mm), high ADC sampling rate (400 samples/channel/second) and low power consumption of the transmitter unit in working mode (40mW) Spectrum measurement of the filter and channel real-time wireless data transmission test are applied on this system By implement the miniaturized transmitter unit on the EEG probes, the cumbersome cable could be removed Applications related in medical research and commercial products could be explored based on this wireless brain signal acquisition system, such as brain controlled games and disease diagnose ii Acknowledgment First and foremost, I would like to offer my sincerest gratitude to my advisor, Professor Nader Behdad, who has supported me with his patience and wisdom for my entire graduate student life My Master thesis would not have been possible without his inspiration and effort I would like to thank Professor Justin Williams and Professor Mark Allie for their support, giving me access for the equipments in their laboratory These equipments greatly accelerated my research process Special thanks to Tom Richner for providing me guidance and suggestions for this project I would like to thank my lab mates: Mudar A l-Joumayly, Meng Li, Eric Meunier, Dhananjaya Rao, and Hang Yang for their assistance and useful suggestions Especially, I would like to thank my parents for their support, encouragement and love iii Table of Contents Abstract …………………………………………………………………………………………………… i Acknowledgements ……………………………………………………………………………………… ii Table of Contents ………………………………………………………………………………………… iii Chapter Introduction 1.1Overview 1.2Organization of Chapters Chapter Analog front end and transmitter design for EEG signal 2.1 System overview 2.2 Miniaturized 60Hz hum filter design 2.3 Instrumental Amplifiers 2.4 Analog to digital converter 2.5 Point to point wireless communication Chapter3 LabVIEW Graphic user interface Design i 3.1 Serial Port communication between PC and microprocessor 3.2 IIR digital filter design 3.3 File I/O between LabVIEW and MATLAB Chapter Conclusion Chapter References Chapter Appendixes ii Chapter Introduction 1.1 Overview Electroencephalographic (EEG) signal records the electrical activity of the neurons near the scalp within the brain [1] Both physiological and pathological information could be obtained from EEG signal The study of EEG signal has its applications in diagnose and treatment of brain diseases, neuroscience, and cognitive science [2-3] Such as: psychogenic non-epileptic seizures, syncope, sub-cortical movement disorders, migraine variants, catatonia, adjunct test of brain death, prognosticate in patients with coma EEG recording method could be categorized into two groups: invasive electrode and noninvasive electrode Mathematical model of the brain is built up, and localization of the electrodes has been carefully studied to build up a 3-D EEG map of the brain [4-5], in order to cover the important spots on the scalp The invasive EEG test is far more complicated than noninvasive EEG First of all the head must be kept absolutely still when the electrodes are plugged into the brain, because any movement will damage the brain neuron by the electrodes The patient’s head will be placed in a frame which is pinned to his/her skull, in order to reduce the movement of the head After this, the neurosurgeon will drill several holes on the skull with great precision, plug in the electrodes and read out the data A deficiency of the invasive EEG acquisition method is it usually took more than one month for the patient to recover completely from the surgery [6-7] The advantage of this invasive method is its high accuracy and sensitivity The signal to noise ratio of invasive EEG is from 10 to 100 times higher than non-invasive EEG recording method Currently, invasive EEG signal recording method emphasis in brain disease diagnoses The doctor will carefully evaluate the necessity to apply invasive EEG record, according to the non-invasive EEG measurement record of the patient The invasive EEG method is only applied when the doctors cannot diagnose the disease using the noninvasive method In other word, the noninvasive method has more application and more acceptable by the patient Noninvasive EEG recording method does not need the doctor plug the electrodes into the patient’s brain The EEG signal could be collected by placing the electrodes on the surface of scalp [8-10] Sometimes the conductive gel is used to improve the signal to noise ratio (SNR) The EEG recording and analysis system is a complex data acquisition system, which usually includes probes, analog front end, wireless transmission and data analysis system First of all the non invasive electrodes were placed on the scalp to collect brain signal After the electrode would be the analog front end module, which has different layers of amplifiers and filters, to reduce the noise and amplify the EEG signal [11-12] The amplifier system usually consists of the following components Buffer amplifiers used to transfer the input/output impedance, differential pre amplifiers aimed to eliminate the common mode and 60Hz noise, instrumental amplifiers used to provide a high gain to amplify the EEG signal The filter system usually consists of DC block filter/high pass filter, 60Hz band stop filter, and low pass filter to reduce high frequency noise, includes the higher order of harmonic of 60Hz noise [13] After reducing the noise and amplifying the brain signal to appropriate amplitude, an analog to digital converter is used to convert the analog signal to digital signal Subsequently a compact low power radio frequency transceiver is used to transmit the digitalized EEG signal [14-15] Practical EEG signal acquisition usually involves in large amount of data For example, a typical EEG data acquisition system has 32 channels, 256 samples/second/channel, and the sample period of practical experiment usually last for hours EEG data compression becomes necessary not only for reducing the storage space, but also for shorten the sample periodic and increasing the sample ratio Data compression technology is usually applied to large amount of data during wireless data transmission [16-18] The most common way to compress data is the wavelet transform, the advantages of this algorithm include: high compression efficiency and low computational cost An EEG monitoring and analysis system will receive the transmitted the signal via serial port connected on the desktop computer [19-20] All these hardware components will work together in the data acquisition system, to ensure the EEG signal could be accurately sampled, and transmitted to remote computer Advanced signal analysis system is built in the remote computer to perform complex analysis Such as spectrum analysis, principle component analysis, signal segmentation, independent component analysis, chaos and dynamic analysis, filtering and averaging, event related potentials, and pattern classification [21-24] Feed back and control system is also a key component in EEG signal acquisition and analysis system One category is EEG – based control of reaching to visual targets For example, BCI2000 system could allow people input characters using brain signal without any additional motor actions [25-26] Another category is EEG-based control of mechanical objects For example, the neuron scientists classifies EEG signal into different categories, such as move left, right, forward and backward, to control an toy car or an robot hand [27-30] Control and feedback system focus on the practical applications of analyzing EEG signal, and helps people regain part of the lost body movement functions In this thesis, a wireless data acquisition and real-time signal analysis system for monitoring multi-channel EEG signal is presented This system consists of: non-invasive EEG electrodes, DC filters, 60Hz band stop filters, differential amplifiers, instrumentation amplifiers, analog to digital converters, Bluetooth wireless point to point communication modules, serial port communication modules (for those laptops without a serial port, an serial to USB port converter is provided), LabVIEW based graphic user interface and MATLAB based signal analysis This system could provide a wireless bridge connecting electrodes on the scalp and EEG analysis algorithms in the computer The prototype for both channels EEG and 16 channels EEG is designed and fabricated The performances of this proposed system are as follows Communication range: 10m, maximum wireless data transmission rate: 250k bps, number of EEG channels: 8/16 channels, data acquisition rate: 400 sample/channel/second, common mode rejection ratio: 100dB, 60Hz noise suppression ratio: 50dB For channels application, the size of the transmitter is 40*40*10 mm, and for 16 channel applications, the size is 80*100*20 mm The power consumption is smaller than 40mw for both and 16 channels applications applications, we use an AVR XMEGA Microcontroller with 16 channel integrated analog to digital converter The size of CC2430 is a QLP48 package (about 7*7 mm), comparing to the AVR XMEGA Microcontroller, which is a TQFP64 package (about 17*17 mm) Both CC2430 and AVR XMEGA could reach 400 samples/channel/second at 12bit analog to digital conversion resolution rate, which is sufficient enough to sample the EEG signal 16 2.5 Point to point wireless Communication The practical requirements include low power consumption, compact size, relative high performance and low price for the hardware Taking these factors into consideration, Zigbee communication protocol is chosen to realize this data acquisition system IEEE 802.15.4 (Zigbee) focus on low-power (