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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -58,32 +58,27 @@ $$p(f) = \left |\frac{1}{T}\sum_t^T \frac{a_1(t,f)}{\left| a_1(t,f) \right |} \
Similarly, you can compute the **spectral coherence** of these signals. The spectral coherence $c(f) \in [0,1]$ is given by:
$$c(f) = \frac{\left| \sum_t^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)}$$
$$c(f) = \frac{\left| \sum\_t a_1(t,f) \overline{a\_2(t,f)} \right|^2}{ \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.
Compute the coherence of the V4 signal with each of the flicker signals. We first start with the \textbf{phase coherence}, and next apply the \textbf{spectral coherence}. Which of the two flicker stimuli is attended?
\item
% 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?
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}\\
which contain training data sets for 100 trials recorded from one specific channel, corresponding to the non-attended and attended conditions, respectively. After applying any classifier, you can use \texttt{Kobe\_V1\_LFP1kHz\_NotAtt\_test.npy} and \texttt{Kobe\_V1\_LFP1kHz\_Att\_test.npy} files to evaluate your results.
To get started, similar to what was previously done, load these signals and compute their time-frequency spectrum for different frequency bands. Since you need these spectra for all the following sub-tasks, it is recommended that you save your results in separate files.
\item
% task 6b
Implement the \textbf{ROC analysis} as your own function/module. Then compute the \textbf{ROC curve} for different frequency bands. Plot the ROC accuracy over different frequencies: in which band do you get a better performance?
## 5.
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.
Compute the coherence of the V4 signal with each of the flicker signals. We first start with the **phase coherence**, and next apply the **spectral coherence**. Which of the two flicker stimuli is attended?
## 6.
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?
Here you will use some machine-learning techniques to classify **attended** against **non-attended** signals based on V1 LFPs. For this purpose, you have been provided with:
**V1\_LFP1kHz\_NotAtt\_train.npy** and **V1\_LFP1kHz\_Att\_train.npy**
which contain training data sets for 100 trials recorded from one specific channel, corresponding to the non-attended and attended conditions, respectively. After applying any classifier, you can use **V1\_LFP1kHz\_NotAtt\_test.npy** and **V1\_LFP1kHz\_Att\_test.npy** files to evaluate your results.
To get started, similar to what was previously done, load these signals and compute their time-frequency spectrum for different frequency bands. Since you need these spectra for all the following sub-tasks, it is recommended that you save your results in separate files.
## 7.
Implement the **ROC analysis** as your own function/module. Then compute the **ROC curve** for different frequency bands. Plot the ROC accuracy over different frequencies: in which band do you get a better performance?