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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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advanced_programming/task_2/README.md
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@ -58,11 +58,12 @@ $$p(f) = \left |\frac{1}{T}\sum_t^T \frac{a_1(t,f)}{\left| a_1(t,f) \right |} \
Similarly, you can compute the **spectral coherence** of these signals. The spectral coherence $c(f) \in [0,1]$ is given by: Similarly, you can compute the **spectral coherence** of these signals. The spectral coherence $c(f) \in [0,1]$ is given by:
$$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. $T$ contains time and trials.
\item \item
% task 4 % 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. 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 % 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? 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:\\ 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\_NotAtt\_train.npy} and\\
\texttt{Kobe\_V1\_LFP1kHz\_Att\_train.npy}\\ \texttt{Kobe\_V1\_LFP1kHz\_Att\_train.npy}\\