Schizophrenia is a complex and devastating mental disorder that influences how one behaves, feels, and thinks. It affects a little less than 1 % of the world population and it is understood to be partly genetically mediated. Several genetic risk factors of schizophrenia have been identified. However, efforts to functionally characterize the genetic risk of schizophrenia have not been successful. To link the genetic risk to schizophrenia phenotype, schizophrenia research has turned to quantifiable stable trait markers with a clear genetic connection, so-called endo-phenotypes. Endophenotypes research in schizophrenia has heavily relied on the use of electroencephalogram (EEG) to identify endophenotypes and to better understand the neural mechanisms of neuropsychological candidate endophenotypes for schizophrenia. Here, I used high-density EEG to study two promising candidate endophenotypes of schizophrenia: visual backward masking (VBM) and EEG microstates. To cope with the heterogeneity of schizophrenia, I analyzed data from more than 300 participants (including schizophrenia patients, their unaffected siblings, healthy students scoring either high or low in schizotypal traits, patients with a first episode of psychosis, and healthy controls) performing a masking task or at rest. To deal with undesired signals that may affect the measurements and change the EEG signals of interest in a time-efficient manner and to minimize experts' subjectivity, I first proposed and validated an automatic pre-processing pipeline. This pipeline was subsequently used to pre-process all the analyzed EEG data. In a VBM study, performance of siblings was in between the ones of patients and controls (endophenotype concept). In patients, masking deficits were associated with decreased EEG amplitudes. In siblings, contrary to expectation, EEG amplitudes were even higher than in controls, which I interpret as a compensation signal. Based on EEG source localization, I propose that siblings over-activate a network of brain areas, including the right insula as a key player, to compensate for the deficits. In an EEG microstates study, schizophrenia patients, their siblings, and patients with a first episode of psychosis showed abnormal microstate dynamics compared to controls (endophenotype concept). Interestingly, siblings showed atypical dynamics of a particular microstate compared to patients, which I interpret also as a compensation signal. A similar result was also found in high schizotypes. My findings suggest that, even if there are genetic risks for developing schizophrenia, the brain is somehow capable of compensating for them. A better understanding of these compensation mechanisms and their commonalities might help to explain why some siblings develop schizophrenia, while others do not, which might open new avenues for characterization of schizophrenia and possible treatments of the disorder.