Principals of Digital Signal Recording

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How do we represent a continuously variable signal digitally?. Sampling. Sampling rate – number of measurements per unit time. Sampling depth or . quantization . – number of gradations by which the measurement can be recorded. ID: 536122 Download Presentation

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Principals of Digital Signal Recording

How do we represent a continuously variable signal digitally?. Sampling. Sampling rate – number of measurements per unit time. Sampling depth or . quantization . – number of gradations by which the measurement can be recorded.

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Principals of Digital Signal Recording




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Presentation on theme: "Principals of Digital Signal Recording"— Presentation transcript:

Slide1

Principals of Digital Signal Recording

Slide2

How do we represent a continuously variable signal digitally?

Sampling

Sampling rate – number of measurements per unit time

Sampling depth or

quantization

– number of gradations by which the measurement can be recorded

Slide3

How do we represent a continuously variable signal digitally?

Sampling

What would be the advantage to higher sampling rates?

Slide4

How do we represent a continuously variable signal digitally?

Sampling

What would be the advantage to higher sampling rates?

Nyquist

limit

Slide5

How do we represent a continuously variable signal digitally?

Sampling

What would be the advantage to higher sampling rates?

Nyquist

limit

Aliasing

What would be the disadvantage?

Data size

Compute time

Slide6

How do we represent a continuously variable signal digitally?

Sampling

What would be the advantage to greater sampling depth?

Finer resolution

What would be the disadvantage?

Data size

Possibly compute time

Slide7

How do we represent a continuously variable signal digitally?

Sampling

A note about data size and compute time:

New data size = increase in quantization

x

number of samples

x

number of electrodes!

Slide8

Filters used in EEG

Slide9

What is a filter?

Slide10

What is a filter?

Filters let some “stuff” through and keep other “stuff” from getting through

What do we want to let through?

What do we want to filter out?

Slide11

What is a filter?

The goal of filtering is to improve the signal to noise ratio

Can the filter add signal?

Slide12

Different Kinds of Filters

Low-Pass (High-Cut-Off)

High-Pass (Low-Cut-Off)

Band-Pass

Notch

Each of these will have a certain “slope”

Slide13

How do Filters Work?

Notionally:

Transform to frequency domain

Mask some parts of the spectrum

Transform back to time domain

Slide14

Are There Any Drawbacks?

Yes

Filters necessarily distort data

Amplitude distortion

Latency distortion

Forward/backward/zero-phase

Slide15

Recommendations

Should you filter?

Yes, when necessary to reveal a real signal

Problem: how do you know it’s “real”

No, always look at the unfiltered data first

What filters should you use?

Depends on your situation (e.g. what EEG band are you interested in? Do you have

60Hz

line noise?)

General rule: less aggressive filters are less distorting