Analyzing Electroencephalograms Using Cloud
Computing Techniques
Kathleen Ericson
Shrideep Pallickara
Charles W. Anderson
Colorado State University
December 1, 2010
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Outline
1
Background
BCI
Gathering EEG
Artificial Neural Networks
2
Benefits of the cloud
3
Approach
4
Frameworks
5
Network Setup
6
Results
Basic Tests
Granules Stress Tests
7
Conclusions and Future Work
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
2/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
BCI
Gathering EEG
Artificial Neural Networks
Outline
1
Background
BCI
Gathering EEG
Artificial Neural Networks
2
Benefits of the cloud
3
Approach
4
Frameworks
5
Network Setup
6
Results
Basic Tests
Granules Stress Tests
7
Conclusions and Future Work
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
3/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
BCI
Gathering EEG
Artificial Neural Networks
Brain Computer Interfaces (BCIs)
Allows users who have lost voluntary motor control to interact with
a computer
BCIs work by analyzing electroencephelograms (EEGs) to interpret
the users intent
EEG signals are gathered in a non-invasive method
Typing interface (Doug Hains, Elliott Forney)
Weelchair (Millan)
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
4/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
BCI
Gathering EEG
Artificial Neural Networks
Gathering EEG data
Non–invasive methods
User wears a cap which holds electrodes to the scalp
Electrode placement followed the international 10-20 system of
electrode placement
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
5/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
BCI
Gathering EEG
Artificial Neural Networks
Artificial Neural Networks
Number of input and output nodes are defined by the data
Number of hidden units can vary
More hidden units can model more complex data
More hidden units take longer to train
Weights are added between input and hidden and hidden and output
layers
0
1
2
3
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
6/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Outline
1
Background
BCI
Gathering EEG
Artificial Neural Networks
2
Benefits of the cloud
3
Approach
4
Frameworks
5
Network Setup
6
Results
Basic Tests
Granules Stress Tests
7
Conclusions and Future Work
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
7/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Benefits of the cloud
Current BCI applications are limited
All computation happens with the user
Mobile BCI applications (such as a wheelchair) are tied to a laptop
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
8/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Benefits of the cloud
Current BCI applications are limited
All computation happens with the user
Mobile BCI applications (such as a wheelchair) are tied to a laptop
A single user is classified by a single machine
A dedicated machine for a single user is under–utilized
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
8/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Benefits of the cloud
Current BCI applications are limited
All computation happens with the user
Mobile BCI applications (such as a wheelchair) are tied to a laptop
A single user is classified by a single machine
A dedicated machine for a single user is under–utilized
Computing capabilities are limited
NN complexity is limited by what can be trained on a laptop
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
8/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Benefits of the cloud
Multiple users can access the same cloud
Aggregation of data
More data leads to better trained neural networks
Cloud servers are separate from the users
Users not limited to the computational power of laptops
Possibility for massive scaling
Thousands of users can be supported simultaneously
Complex pipelines for classification can be developed
Computations can be chained through MapReduce or graph-based
paradigms
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
9/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Outline
1
Background
BCI
Gathering EEG
Artificial Neural Networks
2
Benefits of the cloud
3
Approach
4
Frameworks
5
Network Setup
6
Results
Basic Tests
Granules Stress Tests
7
Conclusions and Future Work
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
10/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Approach
R backend
Optimized for matrix multiplication
Existing code available for EEG manipulation, as well as neural
network code
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
11/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Approach
R backend
Optimized for matrix multiplication
Existing code available for EEG manipulation, as well as neural
network code
Group of experts approach
Fits the map reduce framework – mappers classify, reducer produces
expert opinion
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
11/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Approach
R backend
Optimized for matrix multiplication
Existing code available for EEG manipulation, as well as neural
network code
Group of experts approach
Fits the map reduce framework – mappers classify, reducer produces
expert opinion
3 sets of experiments:
Baseline times in R
Cloud communication overhead with Snowfall
Cloud and bridge communication overhead with Granules and JRI
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
11/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Outline
1
Background
BCI
Gathering EEG
Artificial Neural Networks
2
Benefits of the cloud
3
Approach
4
Frameworks
5
Network Setup
6
Results
Basic Tests
Granules Stress Tests
7
Conclusions and Future Work
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
12/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Frameworks Used
Snowfall
Parallel computing package for R
Builds on the Snow package
Executes sequential code on multiple machines simultaneously
Does not require strong parallel computing background
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
13/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Frameworks Used
Snowfall
Parallel computing package for R
Builds on the Snow package
Executes sequential code on multiple machines simultaneously
Does not require strong parallel computing background
Granules
Lightweight cloud computing runtime
Java based
Allows user to specify run semantics – can enter a dormant state
while waiting for more data to become available
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
13/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Frameworks Used
Snowfall
Parallel computing package for R
Builds on the Snow package
Executes sequential code on multiple machines simultaneously
Does not require strong parallel computing background
Granules
Lightweight cloud computing runtime
Java based
Allows user to specify run semantics – can enter a dormant state
while waiting for more data to become available
JRI
Java R Interface
Allows R computations to be run through Java
Communication is string–based
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
13/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Outline
1
Background
BCI
Gathering EEG
Artificial Neural Networks
2
Benefits of the cloud
3
Approach
4
Frameworks
5
Network Setup
6
Results
Basic Tests
Granules Stress Tests
7
Conclusions and Future Work
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
14/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Snowfall Network Setup
Cloud
Input
Results
Source
Nodes
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
15/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Granules Network Setup
Mappers
Input
Resource
User
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Results
Reducer
Analyzing EEG Using Cloud Computing Techniques
16/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Outline
1
Background
BCI
Gathering EEG
Artificial Neural Networks
2
Benefits of the cloud
3
Approach
4
Frameworks
5
Network Setup
6
Results
Basic Tests
Granules Stress Tests
7
Conclusions and Future Work
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
17/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Baseline Results
Table: Loading a single training set (200MB) in ms
Mean(ms)
6581.602
Min(ms)
6439.742
Max(ms)
6822.34
SD(ms)
101.3716
Table: Training a neural network from 1 training set in ms
Mean(ms)
194463.7
Min(ms)
192433.3
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Max(ms)
197094.9
SD(ms)
1300.87
Analyzing EEG Using Cloud Computing Techniques
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Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Baseline Results
Table: Classification times with 1 neural net in ms
Stream Time
5s
1s
250ms
Mean(ms)
23.0432
5.28194
1.710529
Min(ms)
22.17889
4.909039
1.673937
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Max(ms)
23.56791
11.16085
1.926184
SD(ms)
0.4734237
0.8568976
0.03777157
Analyzing EEG Using Cloud Computing Techniques
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Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Snowfall and Granules Training Comparisons
NNs
Training Sets
1
1
3
1
1
4
3
4
Approach
Snowfall
Granules
Snowfall
Granules
Snowfall
Granules
Snowfall
Granules
Mean(ms)
409860.5
313304.2
462626.2
675968.8
1001631
1933540
988410.4
1964255.0
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Min(ms)
403364.3
306402.0
401475.9
610550.0
971224
1782531
964499
1779853
Max(ms)
419965.7
329149.0
483235.9
772679.0
1020680
2057664
1023549
2131574
Analyzing EEG Using Cloud Computing Techniques
SD(ms)
4216.875
5141.031
23512.64
52823.21
17743.27
110686.70
17513.37
136452.10
20/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Classification Times
Method
Snowfall
Granules
Snowfall
Granules
Snowfall
Granules
Stream Time
5s
1s
250ms
Mean(ms)
8884.60
141.69
5825.71
93.16
2831.32
87.25
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Min(ms)
8797.745
136.42
5815.38
47.51
2830.38
48.57
Max(ms)
9069.47
266.63
5857.98
492.68
2849.83
92.67
SD(ms)
85.82
12.75
10.09
32.13
2.03
4.49
Analyzing EEG Using Cloud Computing Techniques
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Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Maximum Supported Users on a Single Machine
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
22/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Scaling to multiple machines
Gathered statistics for classification on 5 and 10 machines
Each machine supported 15 users
While 17 users per 8-core machine could be supported, the network
was swamped with 150 simultaneous users
12MB/s
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
23/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Scaling to multiple machines
Gathered statistics for classification on 5 and 10 machines
Each machine supported 15 users
While 17 users per 8-core machine could be supported, the network
was swamped with 150 simultaneous users
12MB/s
1GB/83s
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
23/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Scaling to multiple machines
Gathered statistics for classification on 5 and 10 machines
Each machine supported 15 users
While 17 users per 8-core machine could be supported, the network
was swamped with 150 simultaneous users
12MB/s
1GB/83s
1TB/23h
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
23/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Scaling to multiple machines
Gathered statistics for classification on 5 and 10 machines
Each machine supported 15 users
While 17 users per 8-core machine could be supported, the network
was swamped with 150 simultaneous users
12MB/s
75 Users
150 Users
1GB/83s
Mean(ms)
64.33
69.81
1TB/23h
Min(ms)
21.69
22.01
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Max(ms)
268.30
352.82
SD(ms)
20.51
22.49
Analyzing EEG Using Cloud Computing Techniques
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Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Stress Histograms – 75 users
10000
0
5000
Frequency
15000
Communications Overheads with 75 Concurrent Users
0
50
100
150
200
250
Classification Times (ms)
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
24/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Basic Tests
Granules Stress Tests
Stress Histograms – 150 users
30000
20000
0
10000
Frequency
40000
Communications Overheads with 150 Concurrent Users
0
50
100
150
200
250
Classification Times (ms)
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
25/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Outline
1
Background
BCI
Gathering EEG
Artificial Neural Networks
2
Benefits of the cloud
3
Approach
4
Frameworks
5
Network Setup
6
Results
Basic Tests
Granules Stress Tests
7
Conclusions and Future Work
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
26/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Conclusions
Granules is a viable option for real-time EEG classification in the
cloud
While a pure R implementation can train a network more quickly,
there is no native R support for continuous streaming data
JRI carries a heavy overhead for communications
Compression is needed to scale further
With 150 users, we are processing 1GB of EEG signals every 83
seconds
At this rate, over 1TB of data is processed in a day
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
27/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Future Work
Develop a byte-based Granules Bridge for R
Implement an online learning algorithm
Implement compression
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
28/29
Background
Benefits of the cloud
Approach
Frameworks
Network Setup
Results
Conclusions and Future Work
Questions
Questions?
ericson@cs.colostate.edu
CSU – K. Ericson, S. Pallickara, C. W. Anderson
Analyzing EEG Using Cloud Computing Techniques
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