Preparation By -:Mustafa R. AbassZeena S. AliWalaa K. Azeez

Overview Offers By Walaa K. Azeez Chapter One-:

To neural Signal processing (4 slides) Introduction

Chapter Two:- Offers By Mustafa R. AbassEEG signal processing technique (4 slides)

Chapter Three:-Offers By Zeena S. AliSignal processing by filtering (5 slides)

Chapter OneIntroduction

1.1 Neural signal Neurons are remarkable among the cells of the body in their

ability to propagate signals rapidly over large distances. They do this by generating characteristic electrical pulses called action potentials, or more simply spikes, that can travel down nerve fibers. Neurons represent and transmit information by firing sequences of spikes in various temporal patterns. The study of neural coding. seen in the drawings of figure 1.1, the dendrites that receive inputs from other neurons and the axon that carries the neuronal output to other cells. The elaborate branching structure of the dendritic tree allows a neuron to receive inputs from many other neurons through synaptic connections. The cortical pyramidal neuron of (figure 1.1) and the cortical interneuron of figure 1.1 each receives thousands of synaptic inputs, and for the cerebellar Purkinje cell of figure axons and the number is over 100,000 synaptic inputs .

1.2 Neural Signal ProcessingNeural signal processing is a discipline within neuro engineering. This interdisciplinary approach combines principles from machine learning, signal processing theory, and computational neuroscience applied to problems in basic and clinical neuroscience. The ultimate goal of neuro engineering a technological revolution, where machines would interact in real time with the brain. Machines and brains could interface, enabling normal function in cases of injury or disease, brain monitoring, and/or medical rehabilitation of brain disorders.all processing methods appropriate for neural data Creative ways of extracting meaningful features from huge data sets.

1.2.1 The goals of neural signal processingWe have-Novel experimental paradigms. -New neural recording technologies. -Huge and rich potential data set.Goals Further our basic understanding of brain

function Develop biomedical devices that interface with the brain.

-Signal processing methods appropriate for neural data Creative ways of extracting meaningful features from huge data sets.

 

1.2.3 Neural Encoding and DecodingNeural encoding – the map from stimulus to neural response.Neural decoding – the map from response to stimulus.

Chapter TwoEEG signal processing technique

2.1 INTRODUCTIONEEG is one of the brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders.

2.2 BRAIN SIGNAL PROCESSINGSignal processing is the enabling technology for the generation, transformation, and interpretation of information. These brain signals used for various purposes so that it is possible to study the functionalities of brain properly by generating, transforming and interpreting the collected signal.

2.2.1 Brain waves classification Brain waves have been categorized into four basic

groups:-beta (>13 Hz), alpha (8-13 Hz), theta (4-8 Hz), delta (0.5-4 Hz).

2.2.2 Brain Waves and EEGThe electroencephalogram (EEG) recording is a useful tool for studying the functional state of the brain and for diagnosing certain disorders. EEG signals contain more relevant information about brain disorders and different types of artifacts Signals.After that we will observe the EEG signals to recognize and eliminate different disease relate artifacts. Then unwanted signal will be subtracted by differential amplifier. Finally we will proceed for the signal filtering based on the different types of brainwave frequencies to diagnosis and simulate variety of brain disorders by using MATLAB.

3.2.3 Brain computer interface processing steps

EEG recording techniquesEncephalographic measurements employ recording system consisting of:-electrodes with conductive mediaamplifiers with filtersA/D converterrecording device.

2.3 Amplifiers and filters The signals need to be amplified to make them compatible with devices such as displays, recorders, or A/D converters. Amplifiers adequate to measure these signals have to satisfy very specific requirements. They have to provide amplification selective to the physiological signal, reject superimposed noise and interference signals, and guarantee protection from damages through voltage and current surges for both patients and electronic equipment .

The output signal contain different type of noise it can be removed by using special type of filter such as impulse filter, gussian filter, low and high band pass filter and band stop filter. A high-pass filter is needed for reducing low frequencies coming from bioelectric flowing potentials (breathing, etc.),and a low-pass filter is used with frequency (in the range from 40 Hz up to

less than one half of the sampling rate) .

Chapter Three

Signal processing by filtering

3.1 Introduction Whenever frequency analysis is performed, it is desirable that a choice of filter type should be available to suit the specific application. In acoustics there is a long tradition for using octave and one third octave-band filters, with standardized filter characteristics. For vibration analysis, narrow- band spectra based on constant-bandwidth analysis are usually preferred

3.2 Filter Analysis a filter is a device that transmits a signal in such a manner

that its output is the result of convolving the input signal with the impulse response function h (t) of the filter. In the frequency domain this corresponds to a (complex) multiplication of the frequency spectrum of the signal, by the frequency response function of the filter H(f ).

H (f ) = F {h ( t)}

3.2.1 Windowing Windows are functions defined across the time record which are periodic in the time record. They start and stop at zero and are smooth functions in between. When the time record is windowed, its points are multiplied by the window function, time-bin by time-bin, and the resulting time record is by definition periodic. It may not be identical from record to record, but it will be periodic (zero at each end).

3.2.2 Hanning The Hanning window is the most commonly used window. It has an amplitude variation of about 1.5 dB (for signals between bins) and provides reasonable selectivity. Its filter roll off is not particularly steep. As a result, the Hanning window can limit the performance of the analyzer when looking at signals close together in frequency and very different in amplitude.

 

3.2.3 FlattopThe Flattop window improves on the amplitude the Hanning window. Its amplitude about 0.02 dB. However, the selectivity is Unlike the Hanning, the Flattop window has very steep roll off on either side. Thus, sign but do not leak across the whole spectrum.

3.2.4 Blackman-HarrisThe Blackman-Harris window is a very good with SRS FFT analyzers. It has better amplitude (about 0.7 dB) than the Hanning, very good the fastest filter roll off. The filter is steep a reaches a lower attenuation than the other allows signals close together in frequency to be even when their amplitudes are very different.

3.2.5 KaiserThe Kaiser window has the lowest side-lobes and the least broadening for non-bin frequencies. Because of these properties, it is the best window to use for measurements requiring a large dynamic range.

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