Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network
Abstract
:1. Introduction
- Detecting informative spatiotemporal variables with respect to the dynamic evolving spike-driven patterns during the learning process in SNN models. This resulted in improving the output prediction/classification accuracy.
- Extracting spatiotemporal rules of spike occurrence during the dynamic clustering, which enhanced the interpretability and explainability of SNN learning behavior.
2. Materials and Methods
2.1. Method for Dynamic Spatiotemporal Clustering of Streaming Data in Spiking Neural Networks
- Spatiotemporal data encoding.
- SNN mapping and initializing.
- Unsupervised learning in SNN and simultaneously clustering the neurons.
- Quantitative analysis of the dynamic clustering patterns.
- Spatiotemporal fuzzy clustering.
- Spatiotemporal rule extraction from SNN clustering patterns.
- Supervised learning and pattern classification.
Algorithm 1. The dynamic spatiotemporal clustering algorithm at time point t of the unsupervised learning process. |
Input: Input spike data $\mathit{s}\mathit{p}$, number of neurons in the SNN model $\mathit{N}$, number of input variables $\mathit{v}$, connection weights $\mathit{w}\left[\mathit{N},\mathit{N}\right]$, and parameter $\mathit{\alpha}$, $\mathit{P}\mathit{S}\mathit{P},$ STDP, time $\mathit{t}$ Output: A vector of labelled neurons k, vector of spik events for each cluster 1: Procedure 2: $\left[\mathit{L}\mathit{V}\right]=\mathit{s}\mathit{i}\mathit{z}\mathit{e}\left(\mathit{s}\mathit{p}\right)$ 3: $Fsrc\in {\mathit{R}}^{\mathit{N}\times \mathit{v}}$, $A\in {\mathit{R}}^{\mathit{N}\times \mathit{N}}$ 4: For each time point t from the input stream data Do 5: $Update\mathit{w}withSTDP$ 6: $\mathit{S}=\mathit{D}A\mathit{D}$ 7: ${\mathit{F}}^{*}={\left(\mathit{I}-\mathit{\alpha}\mathit{S}\right)}^{-1}{\mathit{F}}_{\mathit{s}\mathit{r}\mathit{c}}$ 8: $\mathit{k}=\mathit{a}\mathit{r}\mathit{g}\mathit{m}\mathit{a}{\mathit{x}}_{\mathit{j}=\mathbf{1},\dots \mathit{v}}{\mathit{F}}^{*}{}_{\mathit{i}\mathit{j}}$ 9: Visualization of the clusters 10: Spatiotemporal rules within each cluster Do 11: If $\mathit{P}\mathit{S}\mathit{P}\left(\mathit{t}\right)\ge \mathit{e}\mathit{v}\mathit{e}\mathit{n}\mathit{t}-\mathit{t}\mathit{h}\mathit{r}\mathit{e}\mathit{s}\mathit{h}\mathit{o}\mathit{l}\mathit{d}$ 12: Cluster fires as active event in time $\mathit{t}$. 13: End if 14: End for 15: Algorithms to generate a set of spatiotemporal rules 16: End of procedure |
2.2. SNN Model Explainability through Dynamic Clustering Method
- Input spike train $\left({s}_{t}\right)$ to an SNN model.
- The mean of the cluster’s postsynaptic potentials $PSP$, indicated by ${\mathsf{\mu}}_{\mathrm{PSP}\left(\mathrm{t}\right)}$.
- The mean of the cluster’s spiking rates, indicated by $s{r}_{t}$.
- The size of the cluster (number of neurons).
- The mean of the neuron’s memberships (the number of spikes received by neurons from the cluster center).
- Among these five patterns of the cluster evolution, we further investigated the $PSP\left(t\right)$ patterns using the following techniques:
- Local maximum ${P}_{max}\left(t\right):$ the maximum value of the $PSP\left(t\right)$ was measured for each data sample.
- The area under a curve: this is computed from the $PSP\left(t\right)$ of each data sample defined by ${{\displaystyle \int}}_{1}^{l}P\left(t\right)dt,$ where l is the length of each sample (time points).
- Mid of potential: this is an average of the min value and max value in the $PSP\left(t\right)$, measured through $\left(max+min\right)/2$.
2.3. Spatiotemporal Fuzzy Clusters in SNN Models
2.4. Enhancing the SNN Explainability through Spatiotemporal Spike Rule Extraction
Algorithm 2. Defining the order of the time interval when spike actions A are detected. |
Inputs: Cluster $c$, Number of clusters $l$, $PSP$ timeseries, $PSP$ temporal length $T$, Spike-events in clusters ${c}_{i}\left(t\right)$ and spike time-interval $\mathcal{L}$ Outputs: Rules $R=\left(A,ord\right)$ as set of Action A and time orders Procedure: For $c=1tol$//for all the clusters $Baseline\leftarrow 1$ While $(Baseline<T-\mathcal{L})$ If ($\mathrm{Length}\mathrm{of}\{{c}_{i}\left(\mathrm{Baseline}:Baseline+\mathcal{L}\right)0\}\mathrm{equal}\mathrm{to}\mathcal{L})$ //sequential $\mathcal{L}$ number of spikes $Action\left(c,Baseline\right)\leftarrow A$ End If $\mathrm{Baseline}\leftarrow \mathrm{Baseline}+1$ End while End For Print sets of Actions as Rules For c = 1 to $l$ Ord$\leftarrow 1$ For t = 1 to T If $Actions\left(c,t\right)=A$ $R\left(ord\right)$ $\leftarrow Actions\left(c,t\right)$ Ord$\leftarrow ord+1$ End For End For End of Procedure |
2.5. Validity Measurement of the SNN Clustering
3. Results: Dynamic SNN Clustering of EEG Data, Spatiotemporal Rule Extraction and Feature Selection
3.1. Dynamic Spatiotemporal Clustering in SNN while Streaming EEG Data
3.2. Feature Selection through Modelling Dynamic Clustering Patterns in SNN
3.3. Spatiotemporal Fuzzy Clusters in SNN Models of EEG from Control and OP Groups
3.4. Capturing Spatiotemporal Spike Events during Unsupervised Learning in SNN Models
4. Conclusions and Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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${\mathit{P}}_{\mathit{m}\mathit{a}\mathit{x}}$ | Area under Curve | $\mathbf{Midrange}\mathbf{of}\mathbf{the}\mathit{P}\mathit{S}\mathit{P}$ | ||||||
---|---|---|---|---|---|---|---|---|
$\mathit{p}$-Value | EEG Channel | Channel Index | $\mathit{p}$-Value | EEG Channel | Channel Index | $\mathit{p}$-Value | EEG Channel | Channel Index |
2.4 × 10^{−11} | CPz | 17 | 1.2 × 10^{−11} | CPz | 17 | −1 × 10^{−11} | CPz | 17 |
2.2 × 10^{−9} | C4 | 14 | 1.3 × 10^{−8} | C4 | 14 | 8.4 × 10^{−9} | C4 | 14 |
4.7 × 10^{−9} | Pz | 21 | 2.4 × 10^{−8} | P4 | 22 | 1.7 × 10^{−8} | Pz | 21 |
9.9 × 10^{−9} | P4 | 22 | 1.8 × 10^{−7} | Pz | 21 | 4.9 × 10^{−8} | P4 | 22 |
0.00001 | F4 | 6 | 7.3 × 10^{−6} | F4 | 6 | 2.2 × 10^{−6} | F4 | 6 |
0.00008 | C3 | 12 | 3.9 × 10^{−5} | C3 | 12 | 8.2 × 10^{−5} | C3 | 12 |
0.00008 | Fz | 5 | 0.0007 | T6 | 23 | 0.0001 | Fz | 5 |
0.0002 | T6 | 23 | 0.002 | Fz | 5 | 0.0003 | T6 | 23 |
Methods | SNN | SVM | MLP | MLR | ECM |
---|---|---|---|---|---|
26 variables (reported in [33]) | 85.00 | 68.00 | 78.00 | 68.00 | 70.00 |
8 selected variables (feature selection) | 92.00 | 70.00 | 80.00 | 72.00 | 78.00 |
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Doborjeh, M.; Doborjeh, Z.; Kasabov, N.; Barati, M.; Wang, G.Y. Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network. Sensors 2021, 21, 4900. https://doi.org/10.3390/s21144900
Doborjeh M, Doborjeh Z, Kasabov N, Barati M, Wang GY. Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network. Sensors. 2021; 21(14):4900. https://doi.org/10.3390/s21144900
Chicago/Turabian StyleDoborjeh, Maryam, Zohreh Doborjeh, Nikola Kasabov, Molood Barati, and Grace Y. Wang. 2021. "Deep Learning of Explainable EEG Patterns as Dynamic Spatiotemporal Clusters and Rules in a Brain-Inspired Spiking Neural Network" Sensors 21, no. 14: 4900. https://doi.org/10.3390/s21144900