Lesson 1Psychophysiology measures: heart rate variability (HRV) metrics, skin conductance response (SCR), respiratory measures, muscle EMGThis part introduces key body-mind measures, explaining how HRV, skin response, breathing, and muscle activity are recorded, handled, and read as signs of body balance, alertness, and feeling or thinking states in real-life applications.
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 pre-handling for body and brain imaging data, including filters, spotting errors, rejecting or fixing, quality checks, and records for repeatable processes in research.
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 outer body sensors and wearables, including heart tracing, pulse light, movement tracking, and walking monitoring, addressing placement, sampling, movement errors, battery limits, and real-world truth in data gathering.
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 brain wave recording, covering body signal starts, electrode setups, wave bands, event potentials, and space-time detail, with basic pre-handling and common test setups for studies.
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, stressing cortisol from spit or blood, daily and stress rhythms, test principles, quality checks, and reading in test and health settings 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 part covers ways to mix brain wave, imaging, and body or hormone measures, focusing on time matching, space links, sync tools, and analysis for full mixed understanding 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 part checks idea and real limits of biopsychology measures, including indirect signals, space-time trades, intrusion, movement and body mixes, and issues of trust and real-world 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-line modeling, wave analysis, event spotting, and handling steady changes, readying students 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 brain imaging for blood flow signals, covering vessel-brain links, block vs event setups, key gather params, and standard pre-handling for stats modeling and reading.
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 spread MRI, describing gray matter shape measures, voxel shape work, spread tensor models, and path tracing, and how they tie to brain growth, aging, and illness.
T1-weighted anatomy and tissue contrastGray matter morphometry measuresVoxel-based morphometry workflowsDiffusion tensor metrics: FA and MDBasics of white matter tractography
