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, and EMG are recorded, processed, and interpreted as indices of autonomic balance, arousal, and emotional or cognitive states wey matter.
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 dataDis section detail signal quality assessment and preprocessing for physiological and neuroimaging data, including filtering, artifact detection, rejection versus correction, quality control metrics, and documentation for reproducible pipelines wey work well.
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 considerationsDis section focus on peripheral and wearable sensing, including ECG, PPG, actigraphy, and ambulatory monitoring, addressing sensor placement, sampling, motion artifacts, battery limits, and ecological validity in real-world data collection wey fit Liberia.
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 resolutionDis section introduce 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 wey easy to follow.
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 interpretationDis section cover 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 wey relevant.
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 issuesDis section cover 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 wey make sense.
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)Dis section examine 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 de field.
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 metricsDis section review 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 wey dey useful.
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 stepsDis section explain fMRI measurement of BOLD signals, covering neurovascular coupling, block versus event-related designs, key acquisition parameters, and standard preprocessing pipelines dat prepare data for statistical modeling and interpretation easily.
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 basicsDis section introduce structural and diffusion MRI, describing gray matter morphometry, voxel-based morphometry, diffusion tensor modeling, and tractography, and how dese measures relate to brain development, aging, and pathology in real terms.
T1-weighted anatomy and tissue contrastGray matter morphometry measuresVoxel-based morphometry workflowsDiffusion tensor metrics: FA and MDBasics of white matter tractography
