MATERIALS AND METHODS: A scoping review was performed to elaborate on the research regarding resting-state EEG and task-based EEG, particularly for Go/No-go paradigms pertaining to subjects with IAD or specifically IGD. The role of EEG was identified in its diagnostic capability to identify the salient changes that occurred in the response to reward network and the executive control network, using restingstate and task-based EEG. The implication of using EEG in monitoring the therapy for IAD and IGD was also reviewed.
RESULTS: EEG generally revealed reduced beta waves and increased theta waves in addicts. IGD with depression demonstrated increased theta and decreased alpha waves. Whereas increased P300, a late cognitive ERP component, was frequently associated with impaired excessive allocation of attentional resources of the IAD towards addiction-specific cues. IGD had increased whole brain delta waves at baseline, which showed significant reduction post therapy.
CONCLUSION: EEG can identify distinct neurophysiological changes among Internet Addiction Disorder and Internet Gaming Disorder that are akin to substance abuse disorders.
METHODS: We developed a hybrid algorithm that combines features of empirical mode decomposition (EMD) with principal component analysis (PCA) to reduce the BCG artefact. The algorithm does not require extra electrocardiogram (ECG) or electrooculogram (EOG) recordings to extract the BCG artefact.
RESULTS: The method was tested with both simulated and real EEG data of 11 participants. From the simulated data, the similarity index between the extracted BCG and the simulated BCG showed the effectiveness of the proposed method in BCG removal. On the other hand, real data were recorded with two conditions, i.e. resting state (eyes closed dataset) and task influenced (event-related potentials (ERPs) dataset). Using qualitative (visual inspection) and quantitative (similarity index, improved normalized power spectrum (INPS) ratio, power spectrum, sample entropy (SE)) evaluation parameters, the assessment results showed that the proposed method can efficiently reduce the BCG artefact while preserving the neuronal signals.
COMPARISON WITH EXISTING METHODS: Compared with conventional methods, namely, average artefact subtraction (AAS), optimal basis set (OBS) and combined independent component analysis and principal component analysis (ICA-PCA), the statistical analyses of the results showed that the proposed method has better performance, and the differences were significant for all quantitative parameters except for the power and sample entropy.
CONCLUSIONS: The proposed method does not require any reference signal, prior information or assumption to extract the BCG artefact. It will be very useful in circumstances where the reference signal is not available.