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In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

Difficulty level: Intermediate
Duration: 8:51
Speaker: : Mike X. Cohen

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

Difficulty level: Intermediate
Duration: 12:16
Speaker: : Mike X. Cohen

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

Difficulty level: Intermediate
Duration: 13:39
Speaker: : Mike X. Cohen

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

Difficulty level: Intermediate
Duration: 12:34
Speaker: : Mike X. Cohen

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis

Difficulty level: Intermediate
Duration: 9:10
Speaker: : Mike X. Cohen

 

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

Difficulty level: Intermediate
Duration: 13:23
Speaker: : Mike X. Cohen

In this module, you will work with human EEG data recorded during a steady-state visual evoked potential study (SSVEP, aka flicker). You will learn about spectral analysis, alpha activity, and topographical mapping. The MATLAB code introduces functions, sorting, and correlation analysis.

Difficulty level: Intermediate
Duration: 12:36
Speaker: : Mike X. Cohen

This lecture and tutorial focuses on measuring human functional brain networks. The lecture and tutorial were part of the 2019 Neurohackademy, a 2-week hands-on summer institute in neuroimaging and data science held at the University of Washington eScience Institute.

Difficulty level: Intermediate
Duration: 50:44
Speaker: : Caterina Gratton
Course:

 

Panel discussion by leading scientists, engineers and philosophers discuss what brain-computer interfaces are and the unique scientific and ethical challenges they pose. hosted by Lynne Malcolm from ABC Radio National's All in the Mind program and features:

  • Dr Hannah Maslen, Deputy Director, Oxford Uehiro Centre for Practical Ethics, University of Oxford
  • Prof. Eric Racine, Director, Pragmatic Health Ethics Research Unity, Montreal Institute of Clinical Research
  • Prof Jeffrey Rosenfeld, Director, Monash Institute of Medical Engineering, Monash University
  • Dr Isabell Kiral-Kornek, AI and Life Sciences Researcher, IBM Research
  • A/Prof Adrian Carter, Neuroethics Program Coordinator, ARC Centre of Excellence for Integrative Brain Function

 

Difficulty level: Intermediate
Duration: 1:14:34

This panel discussion covers how energy based models are used and is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this course include: Introduction to Deep LearningParameter sharing, and Introduction to Data Science or a Graduate Level Machine Learning.

Difficulty level: Intermediate
Duration: 10:42