Lesson 1Psychophysiology measures: heart rate variability (HRV) metrics, skin conductance response (SCR), respiratory measures, muscle EMGDis section introduce key psychophysiological measures, explaining how HRV, skin conductance, respiration, an EMG are recorded, processed, an interpreted as indices of autonomic balance, arousal, an emotional or cognitive states.
HRV metrics an autonomic balanceSCR amplitude, latency, an habituationRespiratory rate an depth measuresFacial an skeletal muscle EMG basicsSignal preprocessing for psychophysiologyLesson 2Signal quality, preprocessing, artifact detection an removal for physiological an neuroimaging dataDis section detail signal quality assessment an preprocessing for physiological an neuroimaging data, including filtering, artifact detection, rejection versus correction, quality control metrics, an documentation for reproducible pipelines.
Signal-to-noise ratio an quality checksFiltering an baseline correctionEEG an EMG artifact detectionPhysiological noise in MRI an fMRIArtifact rejection versus correctionLesson 3Peripheral autonomic sensors an wearable devices: ECG, PPG, actigraphy, ambulatory monitoring considerationsDis section focus pon peripheral an wearable sensing, including ECG, PPG, actigraphy, an ambulatory monitoring, addressing sensor placement, sampling, motion artifacts, battery limits, an ecological validity in real-world data collection.
ECG acquisition an R-peak detectionPPG signals an pulse wave analysisActigraphy an sleep–wake estimationAmbulatory monitoring design issuesMotion artifacts an adherence challengesLesson 4Electroencephalography (EEG): signal origins, frequency bands, event-related potentials (ERPs), an spatial/temporal resolutionDis section introduce EEG, covering biophysical signal origins, electrode montages, frequency bands, event-related potentials, an spatial an temporal resolution, along wid basic preprocessing an common experimental paradigms.
Cortical generators of EEG signalsElectrode placement an montagesCanonical EEG frequency bandsERP components an cognitive tasksEEG spatial an temporal resolutionLesson 5Endocrine measures: cortisol sampling (saliva, blood), diurnal rhythms, immunoassay basics an interpretationDis section cover endocrine assessment in biopsychology, emphasizing cortisol sampling from saliva or blood, diurnal an stress-related rhythms, immunoassay principles, quality control, an interpretation within experimental an clinical contexts.
Cortisol physiology an stress responseSaliva versus blood sampling protocolsDiurnal an ultradian cortisol rhythmsImmunoassay principles an standardsInterpreting cortisol in contextLesson 6Multimodal integration: combining EEG + psychophysiology or fMRI + cortisol—synchronization an alignment issuesDis section cover strategies for combining EEG, fMRI, an peripheral or hormonal measures, focusing pon temporal alignment, spatial correspondence, synchronization hardware, an analytic approaches for truly integrated multimodal inference.
Rationale for multimodal measurementEEG plus autonomic signals integrationfMRI wid cortisol or hormonesTemporal synchronization an triggersCoregistration an data fusion methodsLesson 7Limitations an confounds of measurement methods (e.g., indirect measures, spatial/temporal tradeoffs, invasiveness)Dis section examine conceptual an practical limitations of biopsychological measures, including indirectness of signals, spatial an temporal tradeoffs, invasiveness, motion an physiological confounds, an issues of reliability an ecological validity.
Indirect neural an physiological indicesSpatial versus temporal resolution tradeoffsInvasiveness, burden, an safety issuesMotion, respiration, an cardiac confoundsReliability, validity, an generalizabilityLesson 8Basic statistics for biosignals: time-series analysis, spectral analysis, event detection, an summary metricsDis section review essential statistics for biosignals, including descriptive metrics, time-series modeling, spectral analysis, event detection, an handling nonstationarity, preparing students to perform robust, reproducible signal analyses.
Descriptive an summary signal metricsTime-domain models for biosignalsSpectral an time–frequency analysisEvent detection an peak pickingHandling nonstationarity an trendsLesson 9Functional magnetic resonance imaging (fMRI): BOLD physiology, experimental designs (block vs event-related), preprocessing stepsDis section explain fMRI measurement of BOLD signals, covering neurovascular coupling, block versus event-related designs, key acquisition parameters, an standard preprocessing pipelines weh prepare data for statistical modeling an interpretation.
Neurovascular coupling an BOLD contrastBlock versus event-related paradigmsRepetition time, resolution, an coveragePreprocessing: motion an slice timingSpatial smoothing an normalizationLesson 10Structural MRI an diffusion MRI (DTI): gray matter morphometry, voxel-based morphometry, white matter tractography basicsDis section introduce structural an diffusion MRI, describing gray matter morphometry, voxel-based morphometry, diffusion tensor modeling, an tractography, an how dese measures relate to brain development, aging, an pathology.
T1-weighted anatomy an tissue contrastGray matter morphometry measuresVoxel-based morphometry workflowsDiffusion tensor metrics: FA an MDBasics of white matter tractography
