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BRAIN-COMPUTER INTERFACE (BCI) CURRICULUM — TOP UNIVERSITIES

[1] MIT — MASSACHUSETTS INSTITUTE OF TECHNOLOGY

Course Code: 6.x / HST.x (varies by term)
Program: Brain-Computer Interfaces (Introduction)

Syllabus Link: https://ocw.mit.edu/courses/

TOPICS COVERED:
• Neurobiology of brain signals (EEG, ECoG, MEG, fNIRS)
• Signal acquisition hardware & safety standards
• Pre-processing: filtering, artifact detection, baseline correction
• Feature extraction: PSD, CSP, time-frequency analysis
• ML classifiers: LDA, SVM, deep neural networks, RL
• Real-time closed-loop BCI architecture
• Applications: spellers, speech synthesis, prosthetics, VR/AR
• Ethics, privacy, data security, equitable access
• Capstone project: design, program, evaluate BCI prototype

ACCESS: OpenCourseWare (free, public)
PREREQUISITES: Linear algebra, Python/MATLAB, basic neuroscience

[2] STANFORD UNIVERSITY

Course Code: PSYCH 287
Title: Brain Machine Interfaces: Science, Technology, and Application

Syllabus Link: https://explorecourses.stanford.edu/search?q=PSYCH+287

TOPICS COVERED:
• BCI history and terminology overview
• Neural signal acquisition (micro-electrode arrays, ECoG, EEG, MEG)
• Preprocessing & feature extraction (filtering, artifact rejection)
• Decoding algorithms: linear classifiers, Kalman filters, deep nets
• Closed-loop control & feedback design
• Applications: motor prosthetics, speech synthesis, locked-in syndrome
• System integration, safety, regulatory issues
• Societal impact & ethical considerations

ASSESSMENT: Problem sets, midterm, semester project, final exam
PREREQUISITES: Programming (Python/MATLAB), linear algebra, probability,
basic neuroscience or physiology

[3] HARVARD UNIVERSITY

Course Code: Neuro 120 / BME Electives
Program: Neural Engineering & BCI Track

Syllabus Link: https://www.mcb.harvard.edu/undergraduate/neuroscience/neuro-courses

TOPICS COVERED:
• Human neurophysiology & brain stimulation
• Spinal cord & peripheral nerve interfaces
• Signal acquisition & processing
• Decoding techniques based on machine learning
• Noninvasive/invasive brain mapping
• Neural interfaces & neural prosthetics
• Future interfaces: nanotechnology, optogenetics

ACCESS: Through Harvard Faculty of Arts & Sciences
PREREQUISITES: Molecular & Cellular Biology core, engineering background

[4] UC BERKELEY — UNIVERSITY OF CALIFORNIA, BERKELEY

Course Code: EE 290P-001
Title: Brain-Machine Interface System

Syllabus Link: https://classes.berkeley.edu/

TOPICS COVERED:
• Hardware: EEG/EMG/ECoG acquisition boards, amplifiers, wireless
• Signal processing: filtering, artifact rejection, feature extraction
• ML classifiers: LDA, SVM, deep nets for intent decoding
• Closed-loop control of robotic manipulators/virtual avatars
• Safety, ethics, regulatory considerations
• Tools: MATLAB/Simulink, Python, LabVIEW, BCI2000, OpenBCI
• Emerging modalities: fNIRS, invasive neural implants

ASSESSMENT: Problem sets, literature review, midterm, final project
PREREQUISITES: Signals & systems, programming, electronics basics

[5] PRINCETON UNIVERSITY

Course Code: NEU 4xx / BME Electives
Program: Neural Engineering & BCI

Syllabus Link: https://princeton.simplesyllabus.com

TOPICS COVERED:
• Neural coding & motor cortex physiology
• Invasive & non-invasive recording methods
• Signal processing pipelines for neural data
• Machine learning for neural decoding
• Closed-loop BCI system design
• Clinical applications: paralysis, communication, rehabilitation

ACCESS: Simple Syllabus Portal (requires login)
PREREQUISITES: Neuroscience or biomedical engineering background

[6] CALTECH — CALIFORNIA INSTITUTE OF TECHNOLOGY

Course Code: Bi 1xx / Be 1xx (varies)
Program: Neural Engineering & Systems Neuroscience

Syllabus Link: https://catalog.caltech.edu/current

TOPICS COVERED:
• Biophysical foundations of neural signals
• Electrophysiology & imaging techniques
• Computational modeling of neural systems
• Neural interface design & implementation
• Real-time decoding & control systems

ACCESS: Caltech Course Catalog
PREREQUISITES: Strong math/physics background, biology fundamentals

[7] YALE UNIVERSITY

Course Code: NSC 2xx / BME Electives
Program: Systems Neuroscience & Neural Interfaces

Syllabus Link: https://courses.yale.edu/

TOPICS COVERED:
• Neural systems organization
• Sensory & motor processing
• Recording & stimulation techniques
• Computational neuroscience methods
• Brain-machine interface principles
• Clinical neurotechnology applications

ACCESS: Yale Course Search / Blue Book
PREREQUISITES: Introductory neuroscience course

[8] IMPERIAL COLLEGE LONDON

Course Code: BIOE70011
Title: Brain Machine Interfaces

Syllabus Link: https://www.imperial.ac.uk/engineering/departments/computing/

TOPICS COVERED:
• Neurophysiological basis of neural signals
• Hardware for neural acquisition (EEG, ECoG, invasive)
• Filtering, artifact removal, feature extraction
• Dimensionality reduction techniques
• Machine learning for classification/regression
• BCI applications: communication, assistive control, neuro-rehabilitation
• System integration & real-time implementation
• Experimental design, ethics, regulatory considerations

ASSESSMENT: Labs, coursework, final project
PREREQUISITES: Bioengineering or related engineering background

[9] ETH ZURICH

Course Code: Neural Systems and Computation (Master's)
Program: Neurotechnology BCI Specialization

Syllabus Link: https://neurotechnology.ethz.ch/education.html

TOPICS COVERED:
• Neural information processing fundamentals
• Neurophysiology of brain rhythms
• Signal acquisition (EEG, MEG, invasive arrays)
• Preprocessing & feature extraction
• ML methods for decoding neural activity
• Control algorithms for closed-loop BCI
• Neuro-ethics, safety, regulatory aspects
• Clinical & non-clinical applications
• Optogenetics, wireless implantable devices
• Practical labs: data acquisition, real-time processing
• Software: OpenBCI, BCI2000
• Capstone: design, implement, evaluate BCI system

ACCESS: Master's Programme in Neural Systems and Computation
PREREQUISITES: Bachelor's in engineering, neuroscience, or related field

[10] CARNEGIE MELLON UNIVERSITY (CMU)

Course Code: BMD 42665
Title: Brain-Computer Interface

Syllabus Link: https://www.cmu.edu/bme/

TOPICS COVERED:
• Neurophysiology & signal acquisition (EEG, ECoG, intracortical)
• Preprocessing, feature extraction, ML classification
• Real-time system integration
• Neural decoding algorithms
• Closed-loop neural modulation
• Applications: neuroprosthetics, locked-in communication
• Ethical-legal considerations

ASSESSMENT: Problem sets, lab reports, midterm, final project
PREREQUISITES: Biomedical engineering or related background

[11] JOHNS HOPKINS UNIVERSITY

Course Code: 585.783
Title: Introduction to Brain-Computer Interfaces

Syllabus Link: https://ep.jhu.edu/courses/585783-introduction-to-brain-computer-interfaces

TOPICS COVERED:
• Neurophysiology & sensor technologies
• EEG, ECoG, invasive recording hardware
• Signal processing pipelines
• ML & decoding algorithms
• Prototype applications: communication spellers, prosthetic control
• Neurorehabilitation tools
• Ethical, regulatory, translational considerations
• Weekly topics: real-time implementation, user-centered design

FORMAT: 2x 75-min lectures + hands-on lab per week
CREDITS: 3 credits (graduate level)
PREREQUISITES: Signals & systems, Python/MATLAB

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KEY TOOLS & RESOURCES

OPEN-SOURCE SOFTWARE:
• OpenBCI — Hardware + software for DIY BCI
• BCI2000 — General-purpose BCI system
• BCILAB — MATLAB toolbox for BCI research
• Lab Streaming Layer (LSL) — Time-synced data streaming
• MNE-Python — MEG/EEG analysis in Python
• PyNN — Neural simulation framework

DATASETS:
• PhysioNet — Neural & physiological datasets
• OpenNeuro — Shared brain imaging data
• BNCI Horizon 2020 — BCI benchmark datasets

RECOMMENDED TEXTBOOKS:
• "Brain-Computer Interfaces" by Wolpaw & Wolpaw
• "Principles of Neural Science" by Kandel et al.
• "Neural Engineering" by He & Li

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GENERAL CURRICULUM STRUCTURE

SEMESTER 1 — FOUNDATIONS:
• Neurobiology & neural signal generation
• Analog/digital signal processing fundamentals
• Python/MATLAB for data analysis
• Ethics of neurotechnology

SEMESTER 2 — SIGNAL PROCESSING & DECODING:
• EEG/ECoG/LFP signal acquisition
• Filtering, artifact removal, normalization
• Feature extraction (time, frequency, space domains)
• Classification algorithms (LDA, SVM, CNN, RNN)

SEMESTER 3 — SYSTEM INTEGRATION & APPLICATIONS:
• Real-time processing pipelines
• Closed-loop feedback design
• Hardware interfacing (amplifiers, electrodes)
• User testing & experimental design

SEMESTER 4 — CAPSTONE PROJECT:
• Team-based BCI system design
• Implementation & evaluation
• Technical report & presentation
• Publication/patent consideration (optional)

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SYLLABI ACCESS LINKS SUMMARY

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