# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 4 Results
### SHAP-Based Feature Importance  
To interpret the model's predictions, SHAP were applied, providing insights into the relative importance of individual EEG features. As illustrated in Figure 22, the SHAP summary plot displays the contribution of each of the 19 EEG channels to the final prediction of the trained CNN-LSTM hybrid model. The names of the channels are plotted on the vertical axis, and the SHAP values that reflect the contribution of each feature to the model's decision-making are plotted on the horizontal axis. A color gradient is utilized to display the feature value range, from low (blue) to high (red). It is noteworthy that the underlying fundamental features were extracted from electrodes positioned over the frontal and central regions, in line with current neuroscientific research that implicates these areas in disrupted attention and executive function in individuals diagnosed with ADHD. The level of interpretability means that the model's findings have a foundation in neurophysiological mechanisms, and as such, they are more trustworthy to implement in clinical diagnostic practice. The horizontal dot plots displayed in the SHAP summary plot represent the distribution of SHAP values attributed to each sample in the dataset. The distribution of points along the \(X\)-axis illustrates that changes in the value of a given EEG channel impact the distribution of the model's probability of predicting a sample as representing ADHD. A point's distance from the center represents the size of the feature's influence. For instance, large values of amplitude in the Fp1 channel (red dots on the right) have the effect of inclining the model towards the prediction of \"ADHD\", while small values have the opposite effect. The same pattern is seen in the F8, P4, Fp2, and C4 channels, which are indicated as some of the most significant features based on importance. Figure 22: SHAP analysis of feature importance in hybrid CNN-LSTM model. ADHD is characterized by strong SHAP values, and the presence of channels in the frontal and temporo-occipital areas suggests that these brain regions are responsible for controlling attention and executive functions, which is consistent with established neurophysiological features. Figure 23 illustrates the SHAP value distribution for EEG features used by the regularized hybrid CNN-GRU-LSTM model. ADHD neurophysiology patterns are most characterized by the dominant channels, including Fp1, F8, F4, C3, and P7, which are predominantly located in the frontal (and temporal) regions. ADHD diagnosis can be influenced by the magnitude of SHAP changes in the Fp1 channel, which is both positive and negative. The direction of the deviation affects the likelihood of ADHD being detected. Such behaviors demonstrate the intricate nature of neural network decision-making through frontal brain activity. In contrast to the baseline architecture, the SHAP values in this model have a wider range of approximately \(-10\) to \(+10\). It is noteworthy that the results show more equal use of features and less attention given to individual channels. Such a pattern is likely the result of incorporating L2 regularization and batch normalization, which promote more stable feature contributions and enhance the overall robustness of the model. The SHAP analysis reveals significant variations in feature robustness and interpretability between the baseline CNN-LSTM model and the enhanced CNN-GRU-LSTM architecture with regularization. In the baseline model, SHAP values were highly dispersed (reaching up to 25), indicating a greater reliance on individual, potentially noisy features and a lack of consistent focus across the EEG signal. In contrast, the proposed model exhibited a more compact and focused SHAP value distribution (within a range of 10), reflecting improved generalization and reduced overfitting.