Lesson 1Psychophysiology measures: heart rate variability (HRV) metrics, skin conductance response (SCR), respiratory measures, muscle EMGThis section introduces key psychophysiological measures, explaining how HRV, skin conductance, respiration, and EMG are recorded, processed, and interpreted as indicators of autonomic balance, arousal, and emotional or cognitive 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 section covers signal quality checks and preprocessing for physiological and neuroimaging data, including filtering, artefact detection, rejection versus correction, quality metrics, and documentation for reproducible workflows.
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 section discusses peripheral and wearable sensors, covering ECG, PPG, actigraphy, and ambulatory monitoring, including sensor placement, sampling rates, motion artefacts, battery constraints, and real-world data validity.
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 section introduces EEG, explaining biophysical signal sources, electrode setups, frequency bands, event-related potentials, spatial and temporal resolution, plus basic preprocessing and common experimental setups.
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 section explores endocrine assessment in biopsychology, focusing on cortisol sampling from saliva or blood, daily and stress rhythms, immunoassay methods, quality checks, and interpretation in experimental and clinical 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—synchronisation and alignment issuesThis section outlines strategies for integrating EEG, fMRI, peripheral, or hormonal measures, emphasising temporal alignment, spatial matching, synchronisation tools, and analysis methods for robust multimodal insights.
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 section reviews conceptual and practical limits of biopsychological measures, such as indirect signals, spatial-temporal trade-offs, invasiveness, motion and physiological confounds, plus reliability and real-world validity issues.
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 section covers key statistics for biosignals, including descriptive metrics, time-series models, spectral analysis, event detection, and non-stationarity handling, equipping learners for reliable, reproducible signal processing.
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 section explains fMRI BOLD signal measurement, covering neurovascular coupling, block vs event-related designs, acquisition parameters, and standard preprocessing for statistical modelling and interpretation.
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 section introduces structural and diffusion MRI, covering grey matter morphometry, voxel-based morphometry, diffusion tensor models, and tractography, linking these to brain development, ageing, and disorders.
T1-weighted anatomy and tissue contrastGray matter morphometry measuresVoxel-based morphometry workflowsDiffusion tensor metrics: FA and MDBasics of white matter tractography
