65 results on '"Principe, José C."'
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2. BCI Signal Processing: Feature Extraction
3. Information Theory, Machine Learning, and Reproducing Kernel Hilbert Spaces
4. BMI/BCI Modeling and Signal Processing
5. Brain-Computer Interfaces
6. Neural Decoding Using Generative BMI Models
7. Foundations of Neuronal Representations
8. BMI Systems
9. Adaptive Algorithms for Point Processes
10. Introduction to Neural Interfaces
11. Input–Output BMI Models
12. Brain-Machine Interface Engineering
13. Regularization Techniques for BMI Models
14. Dynamic Vector Quantization Of Speech
15. Chapter 5 - Maximum Correntropy Criterion–Based Kernel Adaptive Filters
16. Chapter 1 - Introduction
17. CHAPTER 5: Neural Decoding Using Generative BMI Models: 5.4: PARTICLE FILTERS.
18. CHAPTER 5: Neural Decoding Using Generative BMI Models: 5.2: SEQUENTIAL ESTIMATION.
19. CHAPTER 5: Neural Decoding Using Generative BMI Models: 5.3: KALMAN FILTER.
20. CHAPTER 7: BMI Systems: 7.6: SUMMARY.
21. CHAPTER 7: BMI Systems: 7.5: FLORIDA WIRELESS IMPLANTABLE RECORDING ELECTRODES.
22. CHAPTER 7: BMI Systems: 7.4: PORTABLE DSP DESIGNS: THE NEURAL SIGNAL PROCESSOR.
23. CHAPTER 7: BMI Systems: 7.3: THE PICO SYSTEM.
24. CHAPTER 7: BMI Systems: 7.2: AMPLIFICATION.
25. CHAPTER 6: Adaptive Algorithms for Point Processes: 6.5: SUMMAYR.
26. CHAPTER 6: Adaptive Algorithms for Point Processes: 6.4: ENCODING/DECODING IN MOTOR CONTROL.
27. CHAPTER 5: Neural Decoding Using Generative BMI Models: 5.5: HIDDEN MARKOV MODELS.
28. CHAPTER 4: Regularization Techniques for BMI Models: 4.2: CHANNEL SELECTION.
29. CHAPTER 4: Regularization Techniques for BMI Models: 4.1: LEAST SQUARES AND REGULARIZATION THEORY.
30. CHAPTER 3: Input-Output BMI Models: 3.3: SUMMARY.
31. CHAPTER 4: Regularization Techniques for BMI Models: 4.4: SUMMARY.
32. CHAPTER 4: Regularization Techniques for BMI Models: 4.3: EXPERIMENTAL RESULTS.
33. CHAPTER 3: Input-Output BMI Models: 3.2: NONLINEAR MODELS.
34. CHAPTER 2: Foundations of Neuronal Representations 2.9: IMPLICATIONS FOR BMI SIGNAL PROCESSING.
35. CHAPTER 3: Input-Output BMI Models: 3.1: MULTIVARIATE LINEAR MODELS.
36. CHAPTER 2: Foundations of Neuronal Representations 2.7: METHODS OF KINEMATIC AND DYNAMIC REPRESENTATION.
37. CHAPTER 2: Foundations of Neuronal Representations 2.3: NEURAL SIGNAL ING AND ELECTRIC FIELDS OF THE BRAIN.
38. CHAPTER 1: Introduction to Neural Interfaces: 1.5: MOTOR BMIS.
39. CHAPTER 1: Introduction to Neural Interfaces: 1.4: GENERATION OF COMMUNICATION AND CONTROL SIGNALS IN THE BRAIN.
40. CHAPTER 6: Adaptive Algorithms for Point Processes: 6.2: MONTE CARLO SEQUENTIAL ESTIMATION FOR POINT PROCESSES.
41. CHAPTER 6: Adaptive Algorithms for Point Processes: 6.1: ADAPTIVE FILTERING FOR POINT PROCESSES WITH A GAUSSIAN ASSUMPTION.
42. CHAPTER 2: Foundations of Neuronal Representations 2.8: MODELING AND ASSUMPTIONS.
43. CHAPTER 2: Foundations of Neuronal Representations 2.6: NEURAL CODING AND DECODING.
44. CHAPTER 2: Foundations of Neuronal Representations 2.5: STOCHASTIC MODELING.
45. CHAPTER 1: Introduction to Neural Interfaces: 1.2: BEYOND STATE-OF-THE-ART TECHNOLOGY.
46. A Spatio-Temporal Memory Based on SOMs with Activity Diffusion
47. CHAPTER 5: Neural Decoding Using Generative BMI Models: 5.1: POPULATION VECTOR CODING.
48. CHAPTER 2: Foundations of Neuronal Representations 2.2: CONNECTIONISM.
49. CHAPTER 1: Introduction to Neural Interfaces: 1.3: COMPUTATIONAL MODELING.
50. CHAPTER 1: Introduction to Neural Interfaces: 1.1: TYPES OF BRAIN-MACHINE INTERFACES.
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