# EEG Signals Based Internet Addiction Diagnosis Using Convolutional Neural Networks
###### Abstract  
Internet addiction (IA), as a new and often unrecognized psychosocial disorder, endangers people's health and their lives. However, the common biometric analysis based on the combination of EEG signals and results of questionnaires is not quantitative, and thus difficult to ensure a specific biomarker. This work aims to develop a deep learning algorithm (no need to identify biomarkers) used for diagnosing IA and evaluating therapy efficacy. Herein, a five-layer CNN model combined with a fast Fourier transform is proposed to diagnose IA quantitatively. This algorithm is validated in the Lemon dataset by using it to process raw data, full spectral power, and alpha-beta-gamma spectral power (related to IA). In contrast to alpha-beta-gamma spectral power, the results based on full spectral power show better performance (87.59% accuracy, 88.80% sensitivity, and 86.41% specificity), which confirms that the proposed algorithm can diagnose IA without biomarkers. In addition, this proposed CNN model presents obvious advantages in processing raw data, achieving 81.1% accuracy. Such results verify that this method can contribute to the reduction of diagnosis time and be potentially used in real-time health monitoring systems. This work provides a quantitative approach to diagnose IA and evaluate therapy efficacy, as a general strategy, and can be widely used in other disorder diagnoses that affect EEG signals, such as psychiatric disorders, substance dependence, and depression. t 2022 2022 2022 2022 2022 202  
However, the obstacle to obtain specific EEG biomarkers for IA is that the frequency bands screened by numerous research teams are different. For example, Both [PERSON] et al. [3] and [PERSON] et al. [11] found that IA patients have a lower beta band, but they have divergence in the gamma band. [PERSON] et al. [12] provided another correlation verification result, i.e., the power of mid-alpha and high-beta bands are reduced by IA. In addition, traditional biostatistical analysis can only explain which frequency bands of EEG signals are related to IA, but cannot measure the effectiveness of these biomarkers quantitatively in the addiction diagnosis. Therefore, an effective method to obtain objective features is urgently needed. With the development of advanced algorithms, a deep neural network (DNN) has been developed for effective EEG analysis, especially for feature extraction and classification [13; 14; 15]. Compared with other DNN methods, convolutional neural network (CNN) has been proven effective for biomarker classification and widely used to process EEG signals in many kinds of fields [16; 17; 18; 19; 20; 21; 22; 23], but there are fewer investigations in the field of IA diagnosis. Although some algorithms for IA, such as support vector machine (SVM) [12] and random forest (RF) [24], have demonstrated good performance in preprocessed data, they cannot achieve high accuracy in processing raw data. Thus, considering the time-consuming process of obtaining preprocessed data, these algorithms are obviously not meet the requirement of the efficient and time-saving diagnostics. To solve these limitations, this work is aimed to develop an algorithm that has higher accuracy and a stronger ability to automatically discover hidden features from a huge number of samples and avoid the complicated process of manually extracting features. In this work, a CNN combined with the fast Fourier transform (FFT) algorithm is proposed to analyze three kinds of EEG data (raw data, full spectral power, and alpha-beta-gamma spectral power) and diagnose IA. Among them, alpha-beta-gamma spectral power is based on the frequency bands associated with IA summarized from previous studies. Compared with the traditional questionnaire analysis [25] and biostatistics method [26], we have obtained more convincing quantitative results, and the accuracy is higher than the current SVM algorithm used for diagnosing IA [27].