Lesson 1Psychophysiology measures: heart rate variability (HRV) metrics, skin conductance response (SCR), respiratory measures, muscle EMGThis part introduces key body measures in psychophysiology, showing how HRV, skin conductance, breathing, and muscle activity are recorded, processed, and read as signs of body balance, alertness, and emotional or thinking states.
HRV metrics and autonomic balanceSCR amplitude, latency, and habituationRespiratory rate and depth measuresFacial and skeletal muscle EMG basicsSignal preprocessing for psychophysiologyLesson 2Signal quality, preprocessing, artifact detection and removal for physiological and neuroimaging dataThis part details checking signal quality and preparing data for body and brain scans, including filtering, spotting errors, rejecting or fixing them, quality checks, and recording steps for reliable processes that can be repeated.
Signal-to-noise ratio and quality checksFiltering and baseline correctionEEG and EMG artifact detectionPhysiological noise in MRI and fMRIArtifact rejection versus correctionLesson 3Peripheral autonomic sensors and wearable devices: ECG, PPG, actigraphy, ambulatory monitoring considerationsThis part focuses on body sensors and wearable gadgets, including ECG, PPG, actigraphy, and daily monitoring, covering where to place them, sampling rates, movement errors, battery limits, and real-life usefulness in gathering data.
ECG acquisition and R-peak detectionPPG signals and pulse wave analysisActigraphy and sleep–wake estimationAmbulatory monitoring design issuesMotion artifacts and adherence challengesLesson 4Electroencephalography (EEG): signal origins, frequency bands, event-related potentials (ERPs), and spatial/temporal resolutionThis part introduces EEG, covering where signals come from, electrode setups, frequency types, event-related potentials, and space and time details, along with basic preparation and common experiment types.
Cortical generators of EEG signalsElectrode placement and montagesCanonical EEG frequency bandsERP components and cognitive tasksEEG spatial and temporal resolutionLesson 5Endocrine measures: cortisol sampling (saliva, blood), diurnal rhythms, immunoassay basics and interpretationThis part covers hormone checks in biopsychology, focusing on cortisol from saliva or blood, daily and stress rhythms, test principles, quality control, and understanding results in experiments and health settings.
Cortisol physiology and stress responseSaliva versus blood sampling protocolsDiurnal and ultradian cortisol rhythmsImmunoassay principles and standardsInterpreting cortisol in contextLesson 6Multimodal integration: combining EEG + psychophysiology or fMRI + cortisol—synchronization and alignment issuesThis part covers ways to mix EEG, fMRI, and body or hormone measures, focusing on time matching, space links, sync tools, and analysis methods for proper combined understanding from multiple sources.
Rationale for multimodal measurementEEG plus autonomic signals integrationfMRI with cortisol or hormonesTemporal synchronization and triggersCoregistration and data fusion methodsLesson 7Limitations and confounds of measurement methods (e.g., indirect measures, spatial/temporal tradeoffs, invasiveness)This part looks at limits and problems in biopsychology measures, like indirect signals, space-time choices, body invasion, movement and body confuses, and issues with reliability and real-life fit.
Indirect neural and physiological indicesSpatial versus temporal resolution tradeoffsInvasiveness, burden, and safety issuesMotion, respiration, and cardiac confoundsReliability, validity, and generalizabilityLesson 8Basic statistics for biosignals: time-series analysis, spectral analysis, event detection, and summary metricsThis part reviews key stats for body signals, including basic measures, time-series models, frequency analysis, event spotting, and handling changing patterns, getting students ready for strong, repeatable signal work.
Descriptive and summary signal metricsTime-domain models for biosignalsSpectral and time–frequency analysisEvent detection and peak pickingHandling nonstationarity and trendsLesson 9Functional magnetic resonance imaging (fMRI): BOLD physiology, experimental designs (block vs event-related), preprocessing stepsThis part explains fMRI for measuring BOLD signals, covering brain-blood links, block vs event designs, main settings, and standard prep steps to ready data for stats and understanding.
Neurovascular coupling and BOLD contrastBlock versus event-related paradigmsRepetition time, resolution, and coveragePreprocessing: motion and slice timingSpatial smoothing and normalizationLesson 10Structural MRI and diffusion MRI (DTI): gray matter morphometry, voxel-based morphometry, white matter tractography basicsThis part introduces structure and diffusion MRI, describing gray matter shape measures, voxel methods, diffusion modeling, and path tracking, and how they relate to brain growth, ageing, and illness.
T1-weighted anatomy and tissue contrastGray matter morphometry measuresVoxel-based morphometry workflowsDiffusion tensor metrics: FA and MDBasics of white matter tractography
