Using Autoregressive Models of Wavelet Bases in the Design of Mental Task-Based BCIs ( Brain-Computer Interface Systems - Recent Progress and Future Prospects )

Publication series ： Brain-Computer Interface Systems - Recent Progress and Future Prospects

Author： Farshad Faradji Farhad Faradji Rabab K. Ward and Gary E. Birch  

Publisher： IntechOpen‎

Publication year： 2013

E-ISBN: INT5069655803

P-ISBN(Paperback): 9789535111344

P-ISBN(Hardback):  9789535111344

Subject： TP39 computer application

Keyword： 计算机的应用

Language： ENG

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Using Autoregressive Models of Wavelet Bases in the Design of Mental Task-Based BCIs

Chapter

Chapter 6 Using Autoregressive Models of Wavelet Bases in the Design of Mental Task-Based BCIs

1. Introduction

1.1. Wavelet packet analysis

1.2. Brain–computer interfaces

1.3. Autoregressive modeling

1.4. Quadratic discriminant analysis

2. Methods

2.1. Data

2.2. Procedure

2.2.1. The design of BCI systems

2.2.2. Training, cross-validation, and testing

2.2.3. Different wavelet bases

2.2.4. The relationship between the packet length and the AR order

2.2.5. Different wavelets

2.2.6. The proposed method to find the best wavelet basis

2.2.7. Selecting the best wavelet and the best wavelet basis

2.2.8. Optimizing the AR model order

2.2.9. Testing the system

3. Results

4. Discussion

4.1. Best wavelet bases

4.2. Most discriminatory tasks

4.3. Selected wavelets

4.4. Average system performance

4.5. Comparison of different wavelet bases

5. Conclusion

Author details

References

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