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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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10
advanced_programming/task_2/README.md
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@ -58,11 +58,12 @@ $$p(f) = \left |\frac{1}{T}\sum_t^T \frac{a_1(t,f)}{\left| a_1(t,f) \right |} \
Similarly, you can compute the **spectral coherence** of these signals. The spectral coherence $c(f) \in [0,1]$ is given by:
$$c(f) = \frac{\left| \sum_t a_1(t,f) \overline{a_2(t,f)} \right|^2}{1}$$
$$c(f) = \frac{\left| \sum_t^T a_1(t,f) \overline{a_2(t,f)} \right|^2}{ \left( \sum_t^T \left| a_1(t,f) \right|^2 \right) \left( \sum_t^T \left| a_2(t,f) \right|^2 \right)}$$
{ \left( \sum_t \left| a_1(t,f) \right|^2 \right) \left( \sum_t \left| a_2(t,f) \right|^2 \right)}
$T$ contains time and trials.
\item
% task 4
In the experiment, attention was devoted to one of the visual stimuli. You do not know to which one, but you know that V4 will selectively respond to the attended stimulus.
@ -73,10 +74,7 @@ $T$ contains time and trials.
% task 6a
You might have observed that also V1 activity is modulated by attention (explain which result of your previous analysis supports such a statement!). How well can location of attention be decoded from one recorded electrode?
% class\_dataset0\_train.npy -> Kobe\_V1\_LFP1kHz\_NotAtt\_train.npy
% class\_dataset1\_train.npy -> Kobe\_V1\_LFP1kHz\_Att\_train.npy
% class\_dataset0\_test.npy -> Kobe\_V1\_LFP1kHz\_NotAtt\_test.npy
% class\_dataset1\_test.npy -> Kobe\_V1\_LFP1kHz\_Att\_test.npy
Here you will use some machine-learning techniques to classify \textbf{attended} against \textbf{non-attended }signals based on V1 LFPs. For this purpose, you have been provided with:\\
\texttt{Kobe\_V1\_LFP1kHz\_NotAtt\_train.npy} and\\
\texttt{Kobe\_V1\_LFP1kHz\_Att\_train.npy}\\