# EEG Pattern Classification of Picking and Coordination Using Anonymous Random Walks
## 4 Conclusions and Future Work  
In the current study, we have designed a method that predicts the class label of coherence graph patterns extracted out of multi-channel EEG epochs taken from three different conditions: a no-task condition (resting state) and two cognitive tasks, which require an investment of cognitive resources, namely picking and coordination. The classification process was based on a coherence graph extracted out of the EEG record. Each node in the graph represents an electrode channel, and each edge represents the coherence between two different electrodes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Predicted Classes**} & \multicolumn{1}{c}{**True**} & \multicolumn{1}{c}{**False**} \\ \cline{3-6}  & & **Resting State** & **P = [1,0]** & **P = [0;1;0]** & **P = [0;0;1]** & **P = [0;0]** & **P \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **True** \\ **Classes** \\ \end{tabular} } & Resting State & 594 & 6 & 99.00\% & 1.00\% \\  & P = [1,0] & & & & & \\ \cline{1-1}  & Picking & 3 & 106 & 11 & 88.33\% & 11.67\% \\ \cline{1-1}  & P = [0;1;0] & & & & & \\ \cline{1-1}  & Coordination & 1 & 8 & 111 & 92.50\% & 7.50\% \\ \cline{1-1}  & P = [0;0;1] & & & & & \\ \hline \multicolumn{2}{c}{Positive Predicted Value} & 99.33\% & 90.60\% & 88.8\% & **Total Prediction Accuracy** \\ \hline \multicolumn{2}{c}{False Discovery Rate} & 0.67\% & 9.40\% & 11.2\% & **96.55\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hierarchical classification results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Resting State** & **Picking** & **Coordination** \\ \hline
**Positive Predicted Value (PPV)** & 0.9933 & 0.9060 & 0.8880 \\ \hline
**True Positive Rate** & 0.9900 & 0.8833 & 0.9250 \\
**(TPR)** & 0.9916 & 0.8945 & 0.9061 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Hierarchical classification performance evaluation. The classification process was performed hierarchically using two levels. The purpose of the first level was to separate between the two cognitive tasks (i.e., picking and coordination) and the resting-state condition. Since all the samples passed through the first level of the classifier, it was important to use a computationally efficient method such as a bag of node degrees (e.g., [27; 28]). To classify the graph embedding vector, we have used a random forest classifier. The purpose of the second level was to separate between picking and coordination. For this purpose, we have chosen a more computationally complex embedding process that preserves and encodes the spatial structure of the graph, namely an anonymous random walk [24] attached to a random forest classifier. It is noteworthy that while an anonymous random walk preserves the spatial properties of the graph, bag of node degrees encodes each node separately while disregarding the other nodes in the graph. Hence, this method enabled us to separate between the different two cognitive tasks. The contribution of this study is two-fold. First, we built an EEG-based classifier to categorize resting state and two types of tacit coordination task conditions, i.e., picking and coordination, by their respective functional connectivity patterns. That is, we have utilized a anonymous random walk graph embedding technique to spatially encode the graph's structure while calculating the coherence between nodes (EEG electrodes). Second, previous studies relied on graph theory measures (such as centrality, path length and modularity (e.g., [42; 43; 44])) in order to distinguish between different cognitive loads. These studies utilized either frequency- and power-based features [42] or the temporal properties of the raw EEG [44]. In contrast, the method used in the current study comprises all channels and frequency bands weighted by the coherence measure between all possible 16-channel combinations.