The document presents a study on a wearable biosignal interface aimed at understanding bio-electric signals for device control, emphasizing various classification approaches and literature on brain-computer interfaces. It discusses system architecture, accuracy of pattern recognition, and challenges in emotion classification via EEG data. Key findings highlight the potential for improved classification with neural network architectures, but note limitations due to low accuracy from limited training data.

1. Wearable BioSignal Interface
by Thanakrit Lersmethasakul

2. Content
1. Objective
2. Literature Survey
3. System Scenario
4. Experiment

3. 1. Objective
- To study the behavior and properties of
bio-electric signals.
- 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
1.
2.
3.
4.
5.
6.
7.
Studying the Use of Fuzzy Inference Systems for Motor Imagery
Classification.
System of Communication and Control Based on the Thought.
BioSig: The Free and Open Source Software Library for Biomedical
Signal Processing.
Forearm EMG Pattern Recognition for Multifunction Myoelectric
Control System.
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 [1]
- Brain-Computer Interfaces with CFIS.
(Chiu's Fuzzy Inference System)
- 3 steps processing
(1) Clustering of training data - Subtractive clustering
algorithm.
(2) Generation of the fuzzy rules - Gaussian
membership function.
(3) Fuzzy rule optimization - Gradient Descent
Formulas

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 [1]

7. Table 2 Fuzzy rules automatically extracted by CFIS for subject [1]
Table 3 Hand-made fuzzy rules used to classify motor imagery data [1]

8. 2. System of Communication and Control Based on the Thought [2]
- Accuracy in classify 70%
- Autoregressive Adaptive Parameter Feature Extraction
- Neural Network Classifier
-
Movement patterns of left and right hand

9. Figure 1 System workflow [2]

10. 3. BioSig: The Free and Open Source Software Library for
Biomedical Signal Processing [3]
- Open source application for Biomedical Signal
Processing.
- Used in research of Neuroinformatics,
Brain-Computer Interfaces, Neurophysiology,
Psychology, Cardiovascular Systems and
Sleep Research.

11. - Data Acquisition, Artifact Processing, Quality Control,
Feature Extraction, Classification, Modeling and
Data Visualization.
- Electroencephalogram (EEG), Electrocardiogram (ECG),
Electrooculogram (EOG), Electromyogram (EMG).

12. Figure 2 Architecture of the BioSig toolbox and its elements [3]

13. Figure 4 Elements of a brain computer interface [3]

14. 4. Forearm EMG Pattern Recognition for Multifunction
Myoelectric Control System [4]
Figure 5 Multifunction myoelectric control system [4]

15. Figure 6 EMG signals from muscle movement, posture and hand gestures. [4]

16. Figure 7 Processing [4]

17. 5. EEG Based Brain Computer Interface [5]
- Test with 1 hidden layer-50 neurons, 1 hidden
layer-100 neurons, 2 hidden layers-50 neurons
and 2 hidden layers-100 neurons Neural
Network.
- 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 [5]

19. Figure 9 Nihon Kohden Neurofax-21 channel Electroencephalograph [5]

20. Figure 10 Planning for Left movement with eyes closed [5]

21. Conclusion
-
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
scalp EEG.
- 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 [6]
Table 4 Some of classifier properties [6]

23. 7. Artificial Speech Synthesizer Control by Brain-Computer
Interface[7]
- 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 [7]

25. 2. System Scenario

26. 4. Experiment
4.1 EMG Signal Classification with Autoregresive Model and
Backpropagation Neural Network.
(1) Detection
Figure 12 Cyberlink Brainfinger [8]

27. Figure 13 Middle of bicep muscle [9]

28. Figure 14 EMG signal detection by Cyberlink Brainfinger.

29. Motionless
Fold
Contract
Twist-In
Twist-Out
Sway-Front
Sway-Back

30. Fold
Contract
Twist-In
Twist-Out
Sway-Front
Sway-Back

31. (2) Feature Extraction
- AR – Coefficients with Least Mean Square
- Convergence constant (µ) = 0.001 [10]
- Autoregressive order = 4
Figure 15 Autoregressive process

32. (3) Classification
- Backpropagation Neural Network
- Learning rate = 0.5
- Error = 0.3
Figure 16 Backpropagation Neural Network process

33. Figure 17 BioSign on UMPC

34. Conclusion
- Low accuracy (Error = 0.3).
- NN structure is not good enough.
- Training data is too little.

35. 4.2 Finding appropriate NN architecture with WEKA
- Calculate and keep AR-coefficients data
Figure 18 AR-coefficients calculation with LMS method.

36. Figure 19 ARFF format file.

37. Figure 20 Preprocess on Weka.

38. Learning Rate = 0.3
Momentum = 0.2
Figure 21 Classify on Weka.

39. Table 5 7-patterns classification result.
(motionless, contract, fold, sway_back, sway_front, twist_in, twist_out)

40. Table 6 6-patterns classification result.
(contract, fold, sway_back, sway_front, twist_in, twist_out)

41. Table 7 4-patterns classification result.
(contract, fold, sway_back, twist_in)

42. Figure 22 Neural Network (28 - Hidden Layers)

43. - Portable EEG brainwave headset
- TGAM1 module, with TGAT1 ASIC
- Automatic wireless computer pairing
- Neuroscience defined EEG power
spectrum (Alpha, Beta, etc.)

44. Reference
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
4, 2007.
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
Incorporated.
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.