Neuro Technology Course

Lesson 1 • Brain Anatomy and Regional Functions

Maps major brain regions to their functional roles in cognition and motor control. Establishes the anatomical vocabulary needed throughout the course.

Lesson 2 • Neurons, Synapses, and Neural Circuits

Explains how individual neurons transmit signals and form functional circuits. Provides the cellular basis for understanding neural recording and stimulation.

Lesson 3 • Neuroplasticity and Learning Mechanisms

Describes how the brain reorganizes in response to experience and intervention. Grounds later chapters on neurofeedback and rehabilitation technology.

Lesson 4 • Neurotransmitters and Neuromodulation

Covers major neurotransmitter systems and their roles in regulating brain states. Connects chemical signaling to technology-relevant outcomes like attention and plasticity.

Lesson 1 • Electroencephalography Principles and Setup

Covers EEG electrode placement, impedance management, and amplifier design. Establishes the most widely used non-invasive recording method in neurotech.

Lesson 2 • Neuroimaging Modalities Overview

Surveys fMRI, fNIRS, MEG, and PET as complementary signal sources. Highlights trade-offs in temporal and spatial resolution relevant to system design.

Lesson 3 • Artifact Sources and Mitigation Strategies

Identifies motion, EMG, EOG, and power-line artifacts in neural recordings. Teaches hardware and protocol strategies to minimize contamination before processing.

Lesson 4 • Invasive Recording Techniques

Examines intracortical electrodes, electrocorticography, and depth probes. Contrasts spatial resolution and signal fidelity with non-invasive alternatives.

Lesson 1 • Connectivity and Network Measures

Quantifies functional and effective connectivity using coherence, Granger causality, and phase metrics. Prepares students for network-level biomarker development.

Lesson 2 • Independent Component Analysis and Source Separation

Applies ICA to decompose mixed neural signals and isolate artifact components. Directly supports clean data preparation for BCI and clinical pipelines.

Lesson 3 • Digital Filtering and Preprocessing

Covers FIR and IIR filter design, re-referencing, and epoch segmentation. Forms the essential preprocessing backbone for all subsequent analyses.

Lesson 4 • Event-Related Potentials and Response Features

Extracts ERP components such as P300, N200, and error-related negativity. Connects averaged evoked responses to cognitive and clinical biomarker applications.

Lesson 1 • Model Validation and Performance Metrics

Covers cross-validation strategies, confusion matrices, and information transfer rate. Ensures rigorous evaluation suited to the low-sample nature of neural datasets.

Lesson 2 • Adaptive and Online Decoding Systems

Designs decoders that update in real time as neural signals drift. Addresses the non-stationarity challenge central to practical BCI deployment.

Lesson 3 • Feature Selection and Dimensionality Reduction

Applies PCA, CSP, and filter-based selection to high-dimensional neural features. Reduces overfitting risk and improves decoder generalization.

Lesson 4 • Classical Classification Algorithms

Implements LDA, SVM, and k-NN for discrete neural state decoding. Provides interpretable baselines before advancing to deep learning approaches.

Lesson 1 • Neurofeedback System Design

Builds closed-loop neurofeedback protocols targeting specific neural biomarkers. Connects real-time signal processing to therapeutic and performance applications.

Lesson 2 • Steady-State and Event-Driven Paradigms

Designs SSVEP and P300 speller paradigms for high-throughput communication BCIs. Compares paradigm suitability across user populations and use cases.

Lesson 3 • BCI Usability and User-Centered Design

Applies usability principles to reduce cognitive load and improve BCI adoption. Integrates human factors into hardware and software interface decisions.

Lesson 4 • Motor Imagery and Sensorimotor Paradigms

Implements motor imagery-based control using event-related desynchronization. Covers training protocols and performance optimization for motor BCIs.

Lesson 1 • Optogenetics and Emerging Stimulation Modalities

Surveys light-based and chemogenetic neural control methods at the cellular level. Positions emerging modalities relative to current clinical and research applications.

Lesson 2 • Transcranial Magnetic Stimulation Principles

Explains electromagnetic induction, coil geometry, and pulse protocols in TMS. Connects cortical excitability modulation to both research and therapeutic contexts.

Lesson 3 • Focused Ultrasound Neuromodulation

Introduces low-intensity focused ultrasound as a non-invasive deep-brain modality. Covers acoustic targeting, thermal vs. mechanical effects, and safety windows.

Lesson 4 • Transcranial Electrical Stimulation Methods

Covers tDCS, tACS, and tRNS mechanisms, electrode montages, and dosing. Establishes the most accessible non-invasive stimulation toolkit for research and clinical use.

Lesson 1 • Motor Neuroprosthetics and Limb Control

Covers neural decoding of intended movement for upper and lower limb prosthetics. Addresses decoder robustness, embodiment, and long-term implant performance.

Lesson 2 • Exoskeletons and Wearable Assistive Devices

Integrates neural control signals with powered exoskeleton and orthotic platforms. Addresses control latency, safety interlocks, and user intent detection.

Lesson 3 • Long-Term Implant Reliability and Biocompatibility

Evaluates tissue response, electrode degradation, and encapsulation over chronic timescales. Informs material selection and packaging strategies for durable implants.

Lesson 4 • Stroke and Spinal Cord Injury Rehabilitation

Applies BCI-driven motor rehabilitation protocols to stroke and spinal cord injury. Quantifies neuroplastic outcomes using clinical and neurophysiological measures.

Lesson 1 • Neural Device Safety Standards

Covers biocompatibility testing, electrical safety limits, and electromagnetic compatibility. Ensures students can specify and verify safety requirements for neural hardware.

Lesson 2 • Regulatory Classification and Approval Pathways

Maps device risk classification, premarket submission types, and clinical evidence requirements. Prepares students to navigate approval processes for neural devices globally.

Lesson 3 • Data Privacy and Neural Data Governance

Addresses neural data sensitivity, consent frameworks, and data minimization principles. Connects brain data governance to broader health data protection obligations.

Lesson 4 • Ethical Principles in Neurotechnology

Applies autonomy, beneficence, justice, and mental privacy to neurotech contexts. Grounds all subsequent regulatory and safety decisions in ethical reasoning.