Multichannel Systems NeuroExplorer - Manual (Page 70)

•
•
•
•
•
•
•
•
•
exceed the number of reference events. Then, the probability value
(bincount[1]/number_of_reference_events) could be larger than 1.
If Normalization is Spikes/Sec, bin counts are divided by NumRefEvents*Bin, where
NumRefEvents is the number of reference events.
If Normalization is Z-score, bin_value = (bin_count - Confidence_mean)/sqrt(Confidence_mean),
where Confidence_mean is the expected mean bin count calculated according to Conf. mean
calculation parameter. Please note that bin counts are assumed to have Poisson distribution
(therefore, standard deviation is equal to square root of expected mean) and Z-score can be
considered to have Normal distribution only for large values (more than 10) of the Confidence_mean.
If the option Count Bins In Filter is selected, for normalization Spikes/Second, NeuroExplorer will
divide bin count by NumTimesBinWasInFilter*Bin instead of NumRefEvents*Bin. The problem is
that when the interval filter is used, bins close to XMin and to XMax may often (when a reference
event is close to the beginning or to the end of the interval in the interval filter) be positioned outside
the filter and therefore will not be used for many reference events. Hence, the bins close to 0.0 will be
used in analysis more often than the bins close to XMin and XMax. If the option Count Bins In Filter
is selected, NeuroExplorer will count the number of times each bin was used in the calculation and
use this count, NumTimesBinWasInFilter, (instead of the number of reference events) to normalize
the histogram.
Peak and Trough Statistics
NeuroExplorer calculates histogram peak statistics the following way:
Maximum of the histogram is found
If the histogram contains several maxima with the same value, peak statistics are not calculated
Otherwise, the center of the bin, where the histogram reaches maximum, is shown as Peak
Position in the Summary of Numerical results
The mean M and standard deviation S of the bin values of the histogram background are
calculated: If Background parameter is set as Bins outside peak/trough, bins outside peak and
trough (i.e., bins that are more than PeakWidth/2 away from the bin with the histogram
maximum and the bin with the histogram minimum) are used to calculate M and S If Background
parameter is set as Shoulders, bins that are to the left of Left Shoulder or to the right of Right
Shoulder parameters are used to calculate M and S
The value M (mean of the background bin values) is shown as Background Mean in the
Summary of Numerical results
The value S (standard deviation of the background bin values) is shown as Background Stdev
in the Summary of Numerical results
The value (HistogramMaximum – M)/S is shown as Peak Z-score
The value (HistogramMaximum + M)/2 is shown as Peak Half Height
Histogram intersects a horizontal line drawn at Peak Half Height at time points TLeft and TRight.
(TRight - TLeft) is shown as Peak Width
Histogram trough statistics are calculated in a similar way. The only difference is that histogram
minimum instead of histogram maximum is analyzed.
Page 68