1. Learning to Classify Biomedical Signals
Miroslav Kubat,Irena Koprinska and Gert Pfurtscheller

2. Contents Two Medical Domain
 Decision-Tree Based Initialization of Neural Networks
 Tree-Based Initialization of RBF Networks
 Experiments
 Discussion

3. Two Medical Domains Sleep Classification
Hypnogram : Horizontal axis~Time
Vertical axis~ different sleep state
To draw a hypnogram:
EEG(electroencephalogram) ~ brain activities
EOG(electrooculogram) ~ eye movements
EMG(electromyogram) ~ muscle contractions

5. Two Medical Domains Brain-Computer Interface
Recognition of Motor Commands From EEG Signals

7. Problem The complicated nature of domains
 impossible to use symbolic machine learning tech. such as rule or decision trees.
Multilayer neural network
 sensitive to proper initialization of topology and weight.

8. Decision-Tree Based Initialization of Neural Networks
General Idea of TBNN

10. Decision-Tree Based Initialization of Neural Networks
Initialization of Weights and Full interconnection of Adjacent Layers
OR-neuron

11. AND -neuron

12. Decision-Tree Based Initialization of Neural Networks
Softening Intervals and Neural-Network Tuning

13. Tree-Based Initialization of RBF Networks
RBF Networks and their Parameters

15. Tree-Based Initialization of RBF Networks
Decision Tree Based Parameter Setting

17. Experiment Data set  domain 1 ~ 8 data files 770~990 examples
15 attributes
domain 2 ~ 3 data files
150~250 examples
11 attributes ~ 44 attributes

18. Experiments
Results

20. Result and Discussion First domain(sleep state)
 The accuracy achieved by TBNN and TB-RBF is not worse that that of human experts
Second domain(brain computer interface)
The utility is obvious because the patterns of desynchronization of EEG are difficult of describe by rules, and learning appears to be only way to accomplish the task.