# EEG Signals Based Internet Addiction Diagnosis Using Convolutional Neural Networks
## 3 Results and Discussion  
Three experiments (based on raw data, full spectral power, and alpha-beta-gamma spectral power) were conducted to evaluate the performance of the proposed CNN architecture on the IA diagnosis. In the proposed CNN structure, the independent validation method was applied to ensure the objectivity of the model verification. A detailed representation of the data used for the proposed CNN model is shown in Figure 5.  
The EEG session from LEMON datasets is recorded after the Multi 8/2 Drogen-Tauchtest drug strip test. Thus, a five-layer CNN structure was conducted to dig more features, reduce the impact of other addictions, and improve the accuracy of the proposed classification algorithm. Forty-nine subjects were divided into 5 groups, and each group contains 10 subjects except one group that contains 9 subjects. We run the CNN model and randomly divide these five groups as one testing set, one validation set, and three training sets. For the input signals used in the model, the EC-EEG dataset was chosen as the raw data to avoid the influence of insight activity on the EEG data. The total length of EEG time-domain signal from everyone is 8 min. To increase the number of training samples, an 8-s sliding window with a 2-s overlap was used to augment the data. Figure 4: Activation functions of Sigmoid, Tanh, and ReLU. Figure 3: Structure of the proposed CNN Model. Two-dimension input samples with a dimension of 38 \(\times\) 2000 were obtained through the data augmentation process, where 38 is the channel number and 2000 is sampled data points over 8 s. After applying FFT to raw data and corresponding amplitude processing, the signal amplitude for each frequency was obtained, which was squared to express the power spectral density. The processing of FFT is described in Figure 6. The dimension of this kind of input is 38 \(\times\) 1000 two-dimension, one is 38 channels and the other is a 1000 frequency matrix. The alpha, beta, and gamma bands that may be affected by IA were then filtered according to the conclusions drawn from previous literature reviews [3; 11; 12], and this two-dimension input signal has 38 channels and 336 frequency matrices. The time-domain curve corresponding to the EEG raw data, the FFT-processing power spectrum, and the filtered alpha-beta-gamma power spectrum of an individual are shown in Figure 7. As in the figure, although the FFT-processed waveform is more clearly displayed in the frequency band than the original time-domain waveform, it is still difficult to identify features related to IA diagnosis through the human eyes. This also verifies the need for tools that are more objective and capable of extracting features. To assess the performance of the proposed model, sensitivity, precision, specificity, and accuracy are used as the evaluation index for these three types of results (obtained from raw data, full spectral power after FFT, and filtered alpha-beta-gamma spectral power). These indexes are calculated by true positive (_TP_), true negative (_TN_), false positive (_FP_), and false negative (_FN_) according to Equations (9)-(12), and the results are listed in Table 1.  
\[\text{sensitivity}=\frac{TP}{TP+FN} \tag{9}\]  
Figure 5: Illustration of the data used in the proposed model. Figure 6: The process of obtaining the frequency domain signal. \[\text{precision}=\frac{TP}{TP+FP} \tag{10}\] Figure 4:0 (11) Figure 4:1  
Alpha-beta-gamma spectral power is a previously summarized biomarker related to IA, which contains higher-density useful information for IA diagnosis. As illustrated in Table 1, the results of full spectral power are better than alpha-beta-gamma spectral power in sensitivity, precision, specificity, and accuracy. It verifies the superiority of this CNN model in the feature extraction and proves that the IA diagnostic method based on this model does not require EEG biomarkers, and the extra filtering process based on previous biometrics is useless to our algorithm.