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

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

Slide2How 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

Slide3How do we represent a continuously variable signal digitally?

Sampling

What would be the advantage to higher sampling rates?

Slide4How do we represent a continuously variable signal digitally?

Sampling

What would be the advantage to higher sampling rates?

Nyquist

limit

Slide5How 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

Slide6How 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

Slide7How 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!

Slide8Filters used in EEG

Slide9What is a filter?

Slide10What 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?

Slide11What is a filter?

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

Can the filter add signal?

Slide12Different Kinds of Filters

Low-Pass (High-Cut-Off)

High-Pass (Low-Cut-Off)

Band-Pass

Notch

Each of these will have a certain “slope”

Slide13How do Filters Work?

Notionally:

Transform to frequency domain

Mask some parts of the spectrum

Transform back to time domain

Slide14Are There Any Drawbacks?

Yes

Filters necessarily distort data

Amplitude distortion

Latency distortion

Forward/backward/zero-phase

Slide15Recommendations

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

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