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An introduction to signals

Introduction

Signals are inputs which have a continuously variable value - this will typically be a voltage that's being output from some type of sensing device. For example, a blood pressure monitoring device might output a voltage proportional to pressure, or a gas analysis device might output a voltage proportional to the level of CO2 at its sensor.

To get a signal into ANY-maze, you need to use an I/O device which can convert the analogue voltage into a digital value that the software can process - this could be, for example, an ANY-maze Analogue interface, which includes four analogue inputs for just this purpose.

Converting signal inputs to the real-world value they represent

As mentioned above, a signal input will typically be a voltage, which means that ANY-maze will understand its value as being a certain number of volts. For example, a blood pressure monitoring device might be outputting a value of 0.5V, which might change over time to a value of 0.72V; but this isn't really what we want to know - we need to know blood pressure, probably in units of mmHg.

The voltage output by sensing devices, such as our hypothetical blood pressure monitor, usually has a linear relationship to the real-world value being sensed. For example, a voltage of 0V might mean a pressure of 0mmHg and a voltage of 1V might mean a pressure of 250mmHg. Knowing this we could deduce that a voltage of 0.5V would equate to a pressure of 125mmHg. Clearly then, it would be desirable to have ANY-maze report the data from the blood pressure signal in mmHg by having it perform this conversion for us; and that's what it can do. You just have to specify two values, such as 0V and 1V with their equivalent real-world values, which would be 0mmHg and 250mmHg in our example. The only requirement for ANY-maze to perform this type of conversion is that the relationship is linear.

Note that you don't have to convert the signal's value - if you choose not to, then the signal's data will just be reported in volts.

Reporting data in relation to a baseline

In our blood pressure monitoring example, we have seen how the voltage that's output by the device can be input into ANY-maze and then converted into mmHg, but what information about blood pressure would ANY-maze report? Well, the answer is 'quite a lot' - for example, it could tell you the maximum, minimum and average blood pressure during the test; it could report on blood pressure across the duration of the test (using the Analysis across time functions); it could report blood pressure when the animal was in different zones; etc. (The full list can be found here). This is all quite useful, but the fact that you're measuring blood pressure suggests that you're interested in how it changes, and you're likely to want to look at these changes in relation to a baseline - which would probably be the animal's blood pressure during a period at the start of the test (perhaps before you do something, like giving it access to some new area of the apparatus).

ANY-maze allows you to optionally define a baseline for a signal as being: the signal's value until either a set duration has elapsed, or until something occurs in the test - a door open, a sound starts playing, the animal moves into a certain zone, etc.

Having specified what ends the baseline period, you then need to specify what constitutes a 'deviation from the baseline' - this could be a change of:

Whatever basis you use, ANY-maze will determine when the signal deviates from the baseline (and when it returns to it) and will then report more information based on this - including such things as: latency to first deviation above the baseline, latency to return to the baseline, integral of deviations above the baseline, etc. - the full list is here (and remember if what you want isn't in the list, we'll gladly add more measures for free - you just have to ask).

Filtering signals

In some cases, the data that a sensing device outputs might be a mix of different information. A classic example of this is the data captured by an EEG, which is a mix of different waves - Delta, Theta, Alpha, etc. In these cases, the individual data can be extracted from the 'raw' signal by filtering it. For example, Theta waves have a frequency of between 4Hz and 7Hz, so if we filtered a raw EEG signal so that only those frequencies were retained we'd then have the Theta signal. Of course, we could also filter the same raw EEG signal for frequencies of between 15Hz and 30Hz, and we'd then have the Beta signal too.

ANY-maze can do this type of filtering - you just have to specify that you want to add a new filtered signal to an existing signal, and then specify the type of filter to apply. A single input signal can have any number of filters applied to it - so in the example above, we could have one to extract the Theta waves and another to extract the Beta waves.

By the way, filtering can also be useful when you want to remove some sort of artefact from a signal. For example, in our hypothetical blood pressure monitor, imagine that the device had a tendency to pick up interference caused by the 50Hz or 60Hz alternating current of the electrical supply. This might make our 'raw' blood pressure data almost unusable, but if you applied a filter to look at just the data from 0Hz to 20Hz, then the mains interference would be 'filtered out' and you'd be left with the true blood pressure signal. Of course, this assumes that the blood pressure wouldn't itself change at a frequency above 20Hz, but that seems rather unlikely.

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ANY-maze help topic T0156