Media Summary: This is part two of a three-part lecture series I taught in a masters-level This video lesson is part of a complete course on This is part one of a three-part lecture series I taught in a masters-level

Neuroscience Source Separation 2b Spatial - Detailed Analysis & Overview

This is part two of a three-part lecture series I taught in a masters-level This video lesson is part of a complete course on This is part one of a three-part lecture series I taught in a masters-level This is part three of a three-part lecture series I taught in a masters-level Frequency ranges are typically defined by arbitrary integer boundaries (e.g., 4-8 Hz or 8-12 Hz). In this talk, I present a method for ... Presented at the Southern Ontario Numerical Analysis Day (SONAD 2025) Yasaman Torabi, PhD Candidate Department of ...

Prof. Nachum Ulanovsky (Weizmann Institute of Science, Israel) on " Slowest Spectral Submanifold (SSM) explaining the dynamics of the slow dynamics of the burn length period of a ... Title: How does a neuroscientist view signals and noise in MEG recordings? Where: Chalmers University of Technology, ...

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Neuroscience source separation 2b: Spatial separation in MATLAB
Neuroscience source separation 2a: Spatial separation
Neuroscience as source separation
Neuroscience source separation 1a: Spectral separation
Neuroscience source separation 3a: Multivariate cross-frequency coupling
Neuroscience source separation 3b: Multivariate cross-frequency coupling in MATLAB
Neuroscience source separation 1b: Spectral separation in MATLAB
Identifying empirical frequency boundaries in multichannel data
Blind Source Separation in Biomedical Signals Using Variational Methods
Nachum Ulanovsky on Spatial cells in the hippocampal formation - PART I
Spectral Submanifolds in Neuroscience: Reduced order model for context-dependent decision making.
NatMEG lecture: How does a neuroscientist view signals and noise in MEG recordings by Ritta Hari
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Neuroscience source separation 2b: Spatial separation in MATLAB

Neuroscience source separation 2b: Spatial separation in MATLAB

This is part two of a three-part lecture series I taught in a masters-level

Neuroscience source separation 2a: Spatial separation

Neuroscience source separation 2a: Spatial separation

This is part two of a three-part lecture series I taught in a masters-level

Neuroscience as source separation

Neuroscience as source separation

This video lesson is part of a complete course on

Neuroscience source separation 1a: Spectral separation

Neuroscience source separation 1a: Spectral separation

This is part one of a three-part lecture series I taught in a masters-level

Neuroscience source separation 3a: Multivariate cross-frequency coupling

Neuroscience source separation 3a: Multivariate cross-frequency coupling

This is part three of a three-part lecture series I taught in a masters-level

Neuroscience source separation 3b: Multivariate cross-frequency coupling in MATLAB

Neuroscience source separation 3b: Multivariate cross-frequency coupling in MATLAB

This is part three of a three-part lecture series I taught in a masters-level

Neuroscience source separation 1b: Spectral separation in MATLAB

Neuroscience source separation 1b: Spectral separation in MATLAB

This is part one of a three-part lecture series I taught in a masters-level

Identifying empirical frequency boundaries in multichannel data

Identifying empirical frequency boundaries in multichannel data

Frequency ranges are typically defined by arbitrary integer boundaries (e.g., 4-8 Hz or 8-12 Hz). In this talk, I present a method for ...

Blind Source Separation in Biomedical Signals Using Variational Methods

Blind Source Separation in Biomedical Signals Using Variational Methods

Presented at the Southern Ontario Numerical Analysis Day (SONAD 2025) Yasaman Torabi, PhD Candidate Department of ...

Nachum Ulanovsky on Spatial cells in the hippocampal formation - PART I

Nachum Ulanovsky on Spatial cells in the hippocampal formation - PART I

Prof. Nachum Ulanovsky (Weizmann Institute of Science, Israel) on "

Spectral Submanifolds in Neuroscience: Reduced order model for context-dependent decision making.

Spectral Submanifolds in Neuroscience: Reduced order model for context-dependent decision making.

Slowest Spectral Submanifold (SSM) explaining the dynamics of the slow dynamics of the burn length period of a ...

NatMEG lecture: How does a neuroscientist view signals and noise in MEG recordings by Ritta Hari

NatMEG lecture: How does a neuroscientist view signals and noise in MEG recordings by Ritta Hari

Title: How does a neuroscientist view signals and noise in MEG recordings? Where: Chalmers University of Technology, ...

Deep clustering: discriminative embeddings for source separation

Deep clustering: discriminative embeddings for source separation

We address the problem of acoustic