ML Visuals by dair.ai

ML Visuals - over 100 fully customizable figures (all open community contributions) that you can freely use for your next machine learning or deep learning paper, blog post, thesis, or presentation

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ML Visuals by dair.ai

ML Visuals - over 100 fully customizable figures (all open community contributions) that you can freely use for your next machine learning or deep learning paper, blog post, thesis, or presentation

machine learning, programming, graphics, illustration, clipart

ML Visuals

By dair.ai

https://github.com/dair-ai/ml-visuals

ML Visuals is a new collaborative effort to help the machine learning community in improving science communication by using more professional, compelling and adequate visuals and figures. You are free to use the visuals in your presentations or blog posts. You don’t need to ask permission to use any of the visuals but it will be nice if you can provide credit to the designer/author (author information found in the slide notes).

This is a project made by the dair.ai community and maintained in this GitHub repo. Our community members will continue to add more common figures and basic elements in upcoming versions. Think of this as free and open artefacts and templates which you can freely and easily download, copy, distribute, reuse and customize to your own needs. I am maintaining this set of slides on a bi-weekly basis where I will regularly organize and keep the slides clean from spam or unfinished content.

IMPORTANT NOTE: Please don’t request editing permission if you do not plan to add anything to the slides. If you want to edit the slides for your own purposes, just make a copy of the slides.

Contributing: To add your own custom figures, simply add a new slide and reuse any of the basic visual components (remember to request edit permissions). We encourage authors/designers to add their visuals here and allow others to reuse them. Make sure to include your author information (in the notes section of the slide) so that others can provide the proper credit if they use the visuals elsewhere (e.g. blog/presentations). Add your name and email just in case someone has any questions related to the figures you added. Also, provide a short description of your visual to help the user understand what it is about and how they can use it. If you need "Edit" permission, just click on the "request edit access" option under the "view only" toolbar above or send me an email at [email protected] If you have editing rights, please make sure not to delete any of the work that other authors have added. You can try to improve it by creating a new slide and adding that improved version (that’s actually encouraged).

Downloading a figure from any of the slides is easy. Just click on File→Download→(choose your format).

If you need help with customizing a figure or have an idea of something that could be valuable to others, we can help. Just open an issue here and we will do our best to come up with the visual. Thanks.

Feel free to ask us anything related to this project in our Slack group.

BCI的应用背景

应用挑战

高变异性

其他方法的不足(链状,单维度,手工设计特征)

提出方法解决问题(多维度,端到端,自动学习)

数据集及问题概述(Visual Stimulus)

���成EEG(时域、频域)地形图

DSNN的提出(多维度)

结果

与传统方法、最新深度学习方法比较(准确率)

分析CNN的工作原理(反卷积)

分析Attention value与ERP和ERD/ERS关系

讨论

DSNN的优越性(SS,TS)

Subject-dependent场景下的结果(Subject-Independent的困难以及必要性)

端到端框架的实现

对image-based方法的分析

Learning Spatial-Spectral-Temporal EEG Representations with Dual-Stream Neural Networks for Motor Imagery

WEIJIAN MAI

For

Representation

EEG-Based BCI的应用背景

运动康复

情感识别

机器人

脑控轮椅

基于EEG的脑机接口能够使用户通过大脑与外界进行交流,最初用于运动性残疾患者的运动康复,近年出现了对于普通用户的情感脑机接口

BCI的应用挑战

High Variation

Intra-Subject

Inter-Subject

Subject-

Independent

(挑战)分类的本质:找到大脑活动与EEG的对应关系,不变表示

BCI的应用挑战

(挑战)目前方法的不足:传统方法(依赖hand-craft features), SOTA方法(单维度),难以适用于Subject-Independent场景

BCI的应用背景

提出的方法:Multi-dimension, End-to-End

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

BCI的应用背景

0~1s

Rest

Rest

ERP

PSD

T0

T1/T2

Motor Imagery

Time

T0

T1/T2

1~3s

Visual

Stimulus

Visual

Stimulus

Rest

Motor Imagery

Rest

Time

4-sec

Visual

Stimulus

Visual

Stimulus

An EEG Trial

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Spectral-Stream

Spatial-Stream

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Spectral-Stream

Spatial-Stream

EEG of all channels in time domain

(0-1s, downsampling to 100Hz)

EEG of all channels in frequency domain

(0-50Hz, average of 0-4s (one trial))

Spatial Topographic Maps

(25 frames)

Spectral Topographic Maps

(25 frames)

(a) EEG Topographic Maps Generation

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Spectral-Stream

Temporal-Stream

EEG of all channels in time domain

(0-1s, downsampling to 100Hz)

EEG of all channels in frequency domain

(0-50Hz, average of 0-4s (one trial))

Spatial Topographic Maps

(25 frames)

Spectral Topographic Maps

(25 frames)

EEG Topographic Maps Generation

(b) Spatial-Spectral-Temporal Feature Learning

BiLSTM

CNN

ANN

FC

Layer

Softmax

Layer

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Spectral-Stream

EEG of all channels in time domain

(0-1s, downsampling to 100Hz)

EEG of all channels in frequency domain

(0-50Hz, average of 0-4s (one trial))

Spatial Topographic Maps

(25 frames)

Spectral Topographic Maps

(25 frames)

(a) EEG Topographic Maps Generation

(b) Spatial-Spectral-Temporal Feature Learning

Bi-LSTM

CNN

ANN

FC

Layer

Softmax

Layer

Temporal-Stream

0ms

40ms

80ms

960ms

0Hz

2Hz

4Hz

48Hz

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Spectral-Stream

Amplitude of all channels in time domain

(0-1s, downsampling to 100Hz)

PSD of all channels in frequency domain

(0-50Hz, average of 0-4s (one trial))

Spatial Topographic Maps

(25 frames)

Spectral Topographic Maps

(25 frames)

(a) EEG Topographic Maps Generation

(b) Spatial-Spectral-Temporal Feature Learning

Bi-LSTM

CNN

ANN

FC

Layer

Softmax

Layer

Temporal-Stream

Bicubic

Amplitude Distribution

(Interval: 40ms)

20ms

60ms

100ms

940ms

980ms

Power Distribution

(Interval: 2Hz)

1Hz

3Hz

5Hz

47Hz

49Hz

Spectral EEG Topographic Maps

(25 frames)

Temporal EEG Topographic Maps

(25 frames)

EEG of all channels in time domain (0-1s, downsampling to 100Hz)

EEG of all channels in frequency domain (0-50Hz, average of 0-4s (one trial))

Bicubic

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Spectral-Stream

Amplitude of all channels in time domain

(0-1s, downsampling to 100Hz)

PSD of all channels in frequency domain

(0-50Hz, average of 0-4s (one trial))

Spatial Topographic Maps

(25 frames)

Spectral Topographic Maps

(25 frames)

(a) EEG Topographic Maps Generation

(b) DSNN for Spatial-Spectral-Temporal Representations Learning

RNN

CNN

ANN

Softmax

Layer

Temporal-Stream

Bicubic

Bicubic

C

40ms

80ms

120ms

1000ms

2Hz

4Hz

6Hz

50Hz

(c) MI Classification

C

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Spectral-Stream

Amplitude of all channels in time domain

(0-1s, downsampling to 100Hz)

PSD of all channels in frequency domain

(0-50Hz, average of 0-4s (one trial))

Spatial Topographic Maps

(50 frames)

Spectral Topographic Maps

(50 frames)

(a) EEG Topographic Maps Generation

(b) DSNN for Spatial-Spectral-Temporal Representations Learning

RNN

CNN

ANN

Softmax

Layer

Temporal-Stream

Bicubic

Bicubic

C

20ms

40ms

60ms

1000ms

1Hz

2Hz

3Hz

50Hz

(c) MI Classification

head-like

topographic map

sensor positions

box-like

topographic map

head-like

topographic map

sensor positions

box-like

topographic map

(32 x 32)

Right Fist

Left Fist

Both Hands

Both

Feet

Right Fist

Left Fist

Both Hands

Both

Feet

Time

P7

P6

P5

P5

Spectral-Stream

Temporal-Stream

Temporal-Stream

Spectral-Stream

Temporal-Stream

(a)Spectral-Stream

(b)Temporal-Stream

Kernel #3

Kernel #7

Kernel #8

Kernel #15

Kernel #23

Kernel #35

Kernel #46

Kernel #63

Kernel #5

Kernel #13

Kernel #25

Kernel #35

Kernel #47

Kernel #51

Kernel #53

Kernel #64

C4

Cz

C3

Fz

CPz

Pz

Cz

(a)Spectral-Stream

(b)Temporal-Stream

Kernel #3

Kernel #7

Kernel #8

Kernel #15

Kernel #23

Kernel #35

Kernel #46

Kernel #63

Kernel #5

Kernel #13

Kernel #25

Kernel #35

Kernel #47

Kernel #51

Kernel #53

Kernel #64

C4

C4

C3

Cz

Subject #10

Subject #12

Subject #3

Subject #10

Subject #12

Subject #3

Attention Matrix

ERP

Attention Matrix

PSD

Attention Matrix

ERP

Attention Matrix

PSD

Test sample #12

Test sample #27

0

25

1

Test sample #12

Test sample #27

Test sample #12

Test sample #27

(a) Temporal

Test sample #12

Test sample #27

(a)

(b)

(c)

Test Sample #3

(a)

(b)

(c)

Test Sample #3

(a)

(b)

(c)

Test Sample #7

(a)

(b)

(c)

Test Sample #3

(a)

(b)

(c)

Test Sample #7

(a)Test Sample #3

(b)Test Sample #7

Note:

These visuals can represent a multi-directional array (3D) input, tensor, etc.

Author: Elvis Saravia ([email protected])

Note:

These visuals can represent a multi-directional array (3D) input, tensor, etc.

Author: Elvis Saravia ([email protected])

Note:

These visuals can represent a multi-directional array (3D) input, tensor, etc.

Author: Elvis Saravia ([email protected])

Note:

These visuals can represent a multi-directional array (3D) input, tensor, etc.

Author: Elvis Saravia ([email protected])

Note:

These visuals can represent a multi-directional array (3D) input, tensor, etc.

Author: Elvis Saravia ([email protected])

ERSP

Time

Frequency

Delta

Theta

Alpha

Beta

Gamma

ERSP:Event-Related Spectral Power

Note:

These visuals can represent a multi-directional array (3D) input, tensor, etc.

Author: Elvis Saravia ([email protected])

Basic components

Note:

Basic components go here.

Note:

This is a simple round rectangle that can represent some process, operation, or transformation

Author: Elvis Saravia ([email protected])

Note:

This can be used to represent a vector. If you want to edit the distance between each element, you can “ungroup” the shapes and then modify the distance between the individual lines.

Author: Elvis Saravia ([email protected])

Symbolizing an embedding

Note:

Can represent a neuron or some arbitrary operation

Author: Elvis Saravia ([email protected])

Softmax

Convolve

Sharpen

Note:

These visuals could represent transformations (left) or operations (right)

The visuals on the right use the Math Equations Add on for Google Slides.

If you click on the e

ML Visuals by dair.ai
Info
Tags Machine learning, Programming, Graphics, Illustration, Clipart
Type Google Slide
Published 28/07/2021, 02:20:59

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