Lesson 1Psychophysiology measures: heart rate variability (HRV) metrics, skin conductance response (SCR), respiratory measures, muscle EMGThis lesson presents key psychophysiological tools, showing how HRV, skin conductance, breathing patterns, and muscle activity are recorded and interpreted as signs of autonomic balance, arousal levels, and emotional or thinking states in practical research.
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 lesson covers assessing and preparing signals from physiological and brain imaging data, including filtering techniques, spotting and removing artefacts, quality checks, and creating repeatable processes to ensure clean data for analysis.
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 lesson discusses sensors and wearables for body functions, covering ECG, PPG, movement tracking, and daily monitoring, including placement tips, sampling rates, handling movement errors, battery issues, and real-life data reliability.
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 lesson introduces EEG basics, from how signals are generated in the brain, electrode setups, frequency types, response to events, to its strengths in timing and location, plus initial processing and common study designs for beginners.
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 lesson explores hormone measurements in biopsychology, focusing on collecting cortisol via saliva or blood, daily and stress rhythms, testing methods, quality standards, and making sense of results in lab or clinical situations.
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 lesson addresses ways to merge EEG, fMRI, body, or hormone data, emphasising time matching, space alignment, sync tools, and analysis methods for combining insights from different sources effectively in research.
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 lesson reviews challenges in biopsychological tools, such as indirect readings, balances between detail in time or space, intrusion levels, movement errors, body influences, and concerns over reliability and real-world applicability.
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 lesson covers fundamental stats for body signals, including summaries, time patterns, frequency breakdowns, spotting events, and managing changes over time, equipping you to analyse signals reliably and repeatably in studies.
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 lesson explains fMRI's BOLD signal basics, brain-vessel links, block or event study types, key settings, and preparation steps like correcting movement to ready data for modelling and understanding brain activity.
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 lesson introduces structure and diffusion MRI, covering grey matter shape analysis, voxel methods, tensor models for diffusion, and tracking paths, relating these to brain growth, ageing, and disease changes over time.
T1-weighted anatomy and tissue contrastGray matter morphometry measuresVoxel-based morphometry workflowsDiffusion tensor metrics: FA and MDBasics of white matter tractography
