created and saved RNN and CNN data from MFDFA, assigned Data Loading …

…ipynb as the dataframe creator and saver for all analysis, plotted and saved MFDFA data, calculated and saved hurst exp data, plotted hurst exp topo map
Metaverse-Crowdsource · Aug 3, 2023 · c866373 · c866373
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diff --git a/Recurrent Neural Network with Long Short-Term Memory layers.ipynb b/Recurrent Neural Network with Long Short-Term Memory layers.ipynb
diff --git a/...checkpoints/ Recurrent Neural Network with Long Short-Term Memory layers-checkpoint.ipynb b/...checkpoints/ Recurrent Neural Network with Long Short-Term Memory layers-checkpoint.ipynb
diff --git a/.ipynb_checkpoints/Data Loading and Variable Assigning-checkpoint.ipynb b/.ipynb_checkpoints/Data Loading and Variable Assigning-checkpoint.ipynb
diff --git a/.ipynb_checkpoints/EEG Data 3D Scatter Plot of Channels as Attractors-checkpoint.ipynb b/.ipynb_checkpoints/EEG Data 3D Scatter Plot of Channels as Attractors-checkpoint.ipynb
diff --git a/.ipynb_checkpoints/Exploration and Analysis EEG-checkpoint.ipynb b/.ipynb_checkpoints/Exploration and Analysis EEG-checkpoint.ipynb
diff --git a/.ipynb_checkpoints/Multifractal Analysis-checkpoint.ipynb b/.ipynb_checkpoints/Multifractal Analysis-checkpoint.ipynb
diff --git a/Data Loading and Variable Assigning.ipynb b/Data Loading and Variable Assigning.ipynb
diff --git a/Exploration and Analysis EEG.ipynb b/Exploration and Analysis EEG.ipynb
diff --git a/Multifractal Analysis.ipynb b/Multifractal Analysis.ipynb
diff --git a/Readme.md b/Readme.md
@@ -34,8 +34,17 @@ This research could potentially contribute to the ambitious goal of directly sti
 
 This research could contribute significantly to the field of decoding and encoding sensory perceptions in the human brain. The proposed model, which integrates chaos theory, dynamical systems theory, and a neural network stack, could provide a robust and comprehensive tool for understanding how the brain responds to tES. Such an understanding is critical for decoding brain activity associated with sensual perception. Specifically, the proposed use of transfer entropy to measure causal relationships within the brain could provide unique insights into how different sensory regions interact and coordinate to produce perceptions. Once these perceptions are decoded[5], it becomes theoretically possible to encode similar patterns back into the brain. The development of a predictive model capable of accurately capturing these dynamics would be a pivotal step towards this goal. Thus, while the immediate focus of the research is predicting EEG effects of tES, the methodologies and insights derived from the work have broader implications and could be foundational for future efforts aimed at decoding and encoding sensual perceptions in the brain.
 
+encoding sensual perceptions in the brain.
+
+<br>
+<br>
+Choose the eeg data file and create your dataframes with the steps here,  **[here](https://github.com/Metaverse-Crowdsource/EEG-tES-Chaos-Neural-Net/blob/main/Data%20Loading%20and%20Variable%20Assigning.ipynb)**.
+<br>
+<br>
+
 <h1 align="center">References</h1>
 
+
 [1] L. Smirnova et al., "Organoid Intelligence (OI): The New Frontier in Biocomputing and Intelligence-in-a-Dish," Frontiers in Science, vol. 7, pp. 0, 2023.
 
 [2] A. E. Rochford et al., "Functional Neurological Restoration of Amputated Peripheral Nerve Using Biohybrid Regenerative Bioelectronics," Science Advances, vol. 9, no. 12, pp. eadd8162, 2023.

diff --git a/data_loading_processing b/data_loading_processing
@@ -0,0 +1,13 @@
+digraph {
+	LoadData [label="Load Data" fontsize=12 shape=rect]
+	FillNull [label="Fill Null Values" fontsize=12 shape=rect]
+	FilterUnwanted [label="Filter Unwanted Channels" fontsize=12 shape=rect]
+	TransposeEEG [label="Transpose EEG Data" fontsize=12 shape=rect]
+	DataFrameEEG [label="Create EEG DataFrame" fontsize=12 shape=rect]
+	DataFrameTriggers [label="Create Triggers DataFrame" fontsize=12 shape=rect]
+	LoadData -> FillNull [label="Load Data" fontsize=10]
+	FillNull -> FilterUnwanted [label="Fill Null Values" fontsize=10]
+	FilterUnwanted -> TransposeEEG [label="Filter Unwanted Channels" fontsize=10]
+	TransposeEEG -> DataFrameEEG [label="Transpose EEG Data" fontsize=10]
+	DataFrameEEG -> DataFrameTriggers [label="Create EEG DataFrame" fontsize=10]
+}