Atagun, Murat IlhanTamura, ShunsukeHirano, Yoji2026-02-032026-02-03202597830317336739783031733680https://doi.org/10.1007/978-3-031-73368-0_105https://hdl.handle.net/20.500.12428/34333Electroencephalography (EEG) and magnetoencephalography (MEG) are neurophysiological methods for recording brain electrical signals. The signals consisting of excitatory post-synaptic potentials and fields summate and propagate through brain surface and cranium and thereby captured by the EEG and MEG. The signal gets contribution from several sources and become convoluted by detached inputs. The signal could be altered by inputs according to the network and task demands. Cognitive, sensory, or motor tasks invoke associated neural networks. Stimulation characteristics determine analysis strategies. Decomposition of the EEG data has three phases including (i) preprocessing, (ii) processing, and (iii) post-processing. Within the recent years, deep learning and classification systems are added into EEG data analysis. In this chapter, we aimed to introduce methods for experimental and analytical procedures of EEG research. © 2025 Springer Nature Switzerland AG.eninfo:eu-repo/semantics/closedAccessBiomedical signal processingBrainBrain mappingClassification (of information)Data handlingDeep learningElectroencephalographyElectrophysiologyNeurophysiologyAnalysis strategiesBrain surfaceElectrical signalExcitatory post synaptic potentialsMental disordersMotor tasksNetwork demandsNeural oscillationsNeural-networksTask demandMagnetoencephalographyBook Chapter3289330610.1007/978-3-031-73368-0_1052-s2.0-105023340743N/A