1. Wearable BioSignal Interface
by Thanakrit Lersmethasakul
2. Literature Survey
3. System Scenario
3. 1. Objective
- To study the behavior and properties of
- Develop a system to identify and recognize
patterns of signals on a portable computer.
- Can be used to control the device.
4. 2. Literature Survey
Studying the Use of Fuzzy Inference Systems for Motor Imagery
System of Communication and Control Based on the Thought.
BioSig: The Free and Open Source Software Library for Biomedical
Forearm EMG Pattern Recognition for Multifunction Myoelectric
EEG Based Brain Computer Interface.
A Review of Classification Algorithms for EEG-based BrainComputer Interfaces.
Artificial Speech Synthesizer Control by Brain-Computer Interface
5. 2.1 Studying the Use of Fuzzy Inference Systems for
Motor Imagery Classification 
- Brain-Computer Interfaces with CFIS.
(Chiu's Fuzzy Inference System)
- 3 steps processing
(1) Clustering of training data - Subtractive clustering
(2) Generation of the fuzzy rules - Gaussian
(3) Fuzzy rule optimization - Gradient Descent
6. - Detection - Bipolar Electrodes
- Feature Extraction - beta and alpha bands (C3β, C3α, C4β, C4α)
- Hand-Made Fuzzy Rules (HMFR)
- Classifier Comparison - Support Vector Machine (SVM),
Multi-Layer Perceptron (MLP),
Linear Classifier (LC)
Table 1 Accuracy(%) and Mutual Information (MI) of Classifiers 
7. Table 2 Fuzzy rules automatically extracted by CFIS for subject 
Table 3 Hand-made fuzzy rules used to classify motor imagery data 
8. 2. System of Communication and Control Based on the Thought 
- Accuracy in classify 70%
- Autoregressive Adaptive Parameter Feature Extraction
- Neural Network Classifier
Movement patterns of left and right hand
9. Figure 1 System workflow 
10. 3. BioSig: The Free and Open Source Software Library for
Biomedical Signal Processing 
- Open source application for Biomedical Signal
- Used in research of Neuroinformatics,
Brain-Computer Interfaces, Neurophysiology,
Psychology, Cardiovascular Systems and
11. - Data Acquisition, Artifact Processing, Quality Control,
Feature Extraction, Classification, Modeling and
- Electroencephalogram (EEG), Electrocardiogram (ECG),
Electrooculogram (EOG), Electromyogram (EMG).
12. Figure 2 Architecture of the BioSig toolbox and its elements 
13. Figure 4 Elements of a brain computer interface 
14. 4. Forearm EMG Pattern Recognition for Multifunction
Myoelectric Control System 
Figure 5 Multifunction myoelectric control system 
15. Figure 6 EMG signals from muscle movement, posture and hand gestures. 
16. Figure 7 Processing 
17. 5. EEG Based Brain Computer Interface 
- Test with 1 hidden layer-50 neurons, 1 hidden
layer-100 neurons, 2 hidden layers-50 neurons
and 2 hidden layers-100 neurons Neural
- Left movement with eyes closed, Right
movement with eyes closed and Left movement
with eyes open are example patterns
18. Figure 8 Functional Model of a BCI System 
19. Figure 9 Nihon Kohden Neurofax-21 channel Electroencephalograph 
20. Figure 10 Planning for Left movement with eyes closed 
Movement can easily be identified and observed by EEG.
Analytical thought process is still almost impossible
to classify, since emotions can’t be predicted via
- The more the number of neurons or layers the better the
classification is but at cost of memory and processing power.
22. 6. A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces 
Table 4 Some of classifier properties 
23. 7. Artificial Speech Synthesizer Control by Brain-Computer
- Invasive EEG with Neurotrophic Electrode.
- Gyrus Precentral (a prominent structure in the parietal
lobe of the human brain).
Kalman filter-based decoder in Speech Synthesizer
24. Figure 11 Schematic of the brain-machine interface for real-time
synthetic speech production 
25. 2. System Scenario
26. 4. Experiment
4.1 EMG Signal Classification with Autoregresive Model and
Backpropagation Neural Network.
Figure 12 Cyberlink Brainfinger 
27. Figure 13 Middle of bicep muscle 
28. Figure 14 EMG signal detection by Cyberlink Brainfinger.
43. - Portable EEG brainwave headset
- TGAM1 module, with TGAT1 ASIC
- Automatic wireless computer pairing
- Neuroscience defined EEG power
spectrum (Alpha, Beta, etc.)
1. Lotte Fabien, L´ecuyer Anatole, Lamarche Fabrice and Arnaldi Bruno,
“Studying the Use of Fuzzy Inference Systems for Motor Imagery Classification.”,
IEEE Transactions on Neural Systems and Rehabilitation Engineering,
Vol.15, No.2, June 2007.
2. R.Gonzalez, "System of Communication and Control Based on the Thought", 2010.
3. Carmen Vidaurre, Tilmann H. Sander and Alois Schl, “BioSig: The Free and Open Source
Software Library for Biomedical Signal Processing”, Computational Intelligence and
Neuroscience, Volume 2011.
4. Angkoon Phinyomark, "Forearm EMG Pattern Recognition for Multifunction
Myoelectric Control System", 12th RGA-Ph.D. Congress.
5. Syed M. Saddique and Laraib Hassan Siddiqui. “EEG Based Brain Computer Interface”.
Journal of Software, vol.4, no.6, August 2009.
6. F.Lotte, M.Congedo, A.Lecuyer, F.Lamarche and B.Arnaldi. “A Review of Classification
Algorithms for EEG-based Brain-Computer Interfaces”. Journal of Neural Engineering
7. Syed M. Saddique and Laraib Hassan Siddiqui. “EEG Based Brain Computer Interface”.
Journal of Software, vol.4, no.6, August 2009.
45. 5. Cyberlink Brainfinger, http://www.brainfingers.com
6. Carlo J. De Luca,2002. ”Surface Electromyography: Detection and Recording”. by DelSys
7. Hefftner G., Zucchini W., and Jaros G.,1988. "The Eletromyogram (EMG) as a Control
Signal for Functional Neuromuscular Stimulation. Part I: Autoregressive Modeling as a
Means of EMG Signature Discrimination". IEEE Transactions on Biomedical
Engeneering, 35(34), pp.230–237.