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 indices of autonomic balance, arousal, and emotional or cognitive states in research contexts.
HRV metrics and autonomic balanceSCR amplitude, latency, and habituationRespiratory rate and depth measuresFacial and skeletal muscle EMG basicsSignal preprocessing for psychophysiologyLesson 2Signal quality, preprocessing, artefact detection and removal for physiological and neuroimaging dataThis section details signal quality assessment and preprocessing for physiological and neuroimaging data, including filtering, artefact detection, rejection versus correction, quality control metrics, and documentation for reproducible pipelines in studies.
Signal-to-noise ratio and quality checksFiltering and baseline correctionEEG and EMG artefact detectionPhysiological noise in MRI and fMRIArtefact rejection versus correctionLesson 3Peripheral autonomic sensors and wearable devices: ECG, PPG, actigraphy, ambulatory monitoring considerationsThis section focuses on peripheral and wearable sensing, including ECG, PPG, actigraphy, and ambulatory monitoring, addressing sensor placement, sampling, motion artefacts, battery limits, and ecological validity in real-world data collection scenarios.
ECG acquisition and R-peak detectionPPG signals and pulse wave analysisActigraphy and sleep–wake estimationAmbulatory monitoring design issuesMotion artefacts and adherence challengesLesson 4Electroencephalography (EEG): signal origins, frequency bands, event-related potentials (ERPs), and spatial/temporal resolutionThis section introduces EEG, covering biophysical signal origins, electrode montages, frequency bands, event-related potentials, and spatial and temporal resolution, along with basic preprocessing and common experimental paradigms used in labs.
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 covers endocrine assessment in biopsychology, emphasizing cortisol sampling from saliva or blood, diurnal and stress-related rhythms, immunoassay principles, quality control, and interpretation within experimental and clinical contexts relevant locally.
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 section covers strategies for combining EEG, fMRI, and peripheral or hormonal measures, focusing on temporal alignment, spatial correspondence, synchronization hardware, and analytic approaches for truly integrated multimodal inference 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 section examines conceptual and practical limitations of biopsychological measures, including indirectness of signals, spatial and temporal tradeoffs, invasiveness, motion and physiological confounds, and issues of reliability and ecological validity in field studies.
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 reviews essential statistics for biosignals, including descriptive metrics, time-series modeling, spectral analysis, event detection, and handling nonstationarity, preparing students to perform robust, reproducible signal analyses in their 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 section explains fMRI measurement of BOLD signals, covering neurovascular coupling, block versus event-related designs, key acquisition parameters, and standard preprocessing pipelines that prepare data for statistical modeling and interpretation in practice.
Neurovascular coupling and BOLD contrastBlock versus event-related paradigmsRepetition time, resolution, and coveragePreprocessing: motion and slice timingSpatial smoothing and normalisationLesson 10Structural MRI and diffusion MRI (DTI): gray matter morphometry, voxel-based morphometry, white matter tractography basicsThis section introduces structural and diffusion MRI, describing gray matter morphometry, voxel-based morphometry, diffusion tensor modeling, and tractography, and how these measures relate to brain development, ageing, and pathology in diverse populations.
T1-weighted anatomy and tissue contrastGray matter morphometry measuresVoxel-based morphometry workflowsDiffusion tensor metrics: FA and MDBasics of white matter tractography
