Канал: Brain-Computer Interfaces (EEG, MEG ...)

A quick intro to the Buffer BCI toolbox

A quick intro to the Buffer BCI toolbox

Seting up the g.USBAMP (g.tec) with the MindAffect Open Source BCI.

Seting up the g.USBAMP (g.tec) with the MindAffect Open Source BCI.

SSVEP - BCI demo Using OpenBCI Cython and Mindaffect

SSVEP - BCI demo Using OpenBCI Cython and Mindaffect

PyTorch Crash Course

PyTorch Crash Course

Importing the BCI Competition IV dataset into MNE-Python

Importing the BCI Competition IV dataset into MNE-Python

How to import EEG Matlab data into MNE-Python

How to import EEG Matlab data into MNE-Python

Berdakh Abibullaev Live Stream

Berdakh Abibullaev Live Stream

MNE for EEG/MEG signal analysis (Q&A session)

MNE for EEG/MEG signal analysis (Q&A session)

Lecture 12: The 1's & 2's complement/ binary addition & subtraction

Lecture 12: The 1's & 2's complement/ binary addition & subtraction

Lecture 11: Arithmetic Functions [Iterative Arrays/ Half adders/ Full adders]

Lecture 11: Arithmetic Functions [Iterative Arrays/ Half adders/ Full adders]

ROBT206: Lecture 8 - Additional Gates and Circuits (NAND, NOR gates, 3 state buffer)

ROBT206: Lecture 8 - Additional Gates and Circuits (NAND, NOR gates, 3 state buffer)

ROBT206: Lecture 7 - Karnaugh Map (don't cares, prime implicants)

ROBT206: Lecture 7 - Karnaugh Map (don't cares, prime implicants)

ROBT206: Lecture 6 (part 2) - Four variable Karnaugh map examples

ROBT206: Lecture 6 (part 2) - Four variable Karnaugh map examples

ROBT206: Lecture 6 (part 1) - Circuit Optimization - Karnaugh maps

ROBT206: Lecture 6 (part 1) - Circuit Optimization - Karnaugh maps

ROBT206 Microcontrollers - Lecture 5: Canonical Forms of Boolean Expressions

ROBT206 Microcontrollers - Lecture 5: Canonical Forms of Boolean Expressions

ROBT206 - Microcontrollers - Lecture 4: Gate Circuits and Boolean Equations

ROBT206 - Microcontrollers - Lecture 4: Gate Circuits and Boolean Equations

A real-time motor-imagery Brain-Computer Interfaces demonstration.

A real-time motor-imagery Brain-Computer Interfaces demonstration.

FieldTrip Toolbox - ERP visualization tutorial (silent)

FieldTrip Toolbox - ERP visualization tutorial (silent)

PyTorch for Brain-Computer Interfaces

PyTorch for Brain-Computer Interfaces

What is PyTorch? [part #1]

What is PyTorch? [part #1]

Lecture 21: Introduction to Deep Learning and Autoencoders

Lecture 21: Introduction to Deep Learning and Autoencoders

Lecture 20: Neural Networks

Lecture 20: Neural Networks

Lecture 19: Soft-Margin Support-Vector-Machines

Lecture 19: Soft-Margin Support-Vector-Machines

Lecture18: Kernel Support-Vector Machines

Lecture18: Kernel Support-Vector Machines

L18 : The analysis and classification of Motor-Imagery EEG data (BCI competition IV)

L18 : The analysis and classification of Motor-Imagery EEG data (BCI competition IV)

Lecture 17: Dual Support-Vector Machines

Lecture 17: Dual Support-Vector Machines

Lecture 16: Linear Support-Vector Machines

Lecture 16: Linear Support-Vector Machines

L15: The scikit-learn: machine learning model selection in Python (part 3)

L15: The scikit-learn: machine learning model selection in Python (part 3)

L7: Event-Related Potentials - multivariate features...

L7: Event-Related Potentials - multivariate features...

L6: Event-Related Potentials - EEG

L6: Event-Related Potentials - EEG

L5: Introduction EEG generation mechanism and the EPSP & IPSP process

L5: Introduction EEG generation mechanism and the EPSP & IPSP process

L4: Human Brain Structure and Function.

L4: Human Brain Structure and Function.

The scikit-learn: machine learning in Python (part 2)

The scikit-learn: machine learning in Python (part 2)

The scikit-learn: machine learning in Python (part 1)

The scikit-learn: machine learning in Python (part 1)

Lecture 11: Regularization

Lecture 11: Regularization

Lecture 12: Validation

Lecture 12: Validation

A Gentle Introduction to Brain-Computer Interfaces

A Gentle Introduction to Brain-Computer Interfaces

L10: Overview of artifact and noise detection in EEG/MEG

L10: Overview of artifact and noise detection in EEG/MEG

Lecture 10: Hazard of Overfitting

Lecture 10: Hazard of Overfitting

Lecture 9: Linear Models for Classification

Lecture 9: Linear Models for Classification

L9: MNE tutorial part #2 - EEG/ERP Visualization and Time-Frequency Analysis

L9: MNE tutorial part #2 - EEG/ERP Visualization and Time-Frequency Analysis

L8: MNE tutorial Part #1 - Load and Segment continuous EEG data

L8: MNE tutorial Part #1 - Load and Segment continuous EEG data

Lecture 8: Logistic Regression

Lecture 8: Logistic Regression

Lecture 7: Linear Regression

Lecture 7: Linear Regression

L7: ERP multivariate features

L7: ERP multivariate features

L6: Event-Related Potentials

L6: Event-Related Potentials

Lecture 6: Noise and Error

Lecture 6: Noise and Error

Sylvan beach, Texas

Sylvan beach, Texas

Lecture 5: Training vs testing & The theory of generalization.

Lecture 5: Training vs testing & The theory of generalization.

Lecture 4: The Feasibility of (Machine) Learning

Lecture 4: The Feasibility of (Machine) Learning

L3: BCI decoding challenges

L3: BCI decoding challenges

Lecture 3: The types of learning - Supervised and Unsupervised learning

Lecture 3: The types of learning - Supervised and Unsupervised learning

L2: How does a BCI work?

L2: How does a BCI work?

Lecture 2: Learning to answer Yes/No

Lecture 2: Learning to answer Yes/No

L1: Introduction to Brain-Computer Interfaces

L1: Introduction to Brain-Computer Interfaces

Lecture 1 - The Learning Problem

Lecture 1 - The Learning Problem

Introduction to Motor-Imagery based Brain-Computer Interfaces

Introduction to Motor-Imagery based Brain-Computer Interfaces

MNE-Python Tutorial for EEG and MEG data analysis and visualization.

MNE-Python Tutorial for EEG and MEG data analysis and visualization.