Summary ✅ Introduction ✅ Field Programmable Gate Arrays (FPGAs) ✅ Implementation of a Classification System of EEG Signals Based on FPGA ✅ More FPGA projects ✅ On going Jobs ✅ Questions

1. Clasificación de señales de
Electroencefalografía (EEG) con redes
neuronales en FPGA
YP Puerto Rico and Caribbean
Virtual Summit 2020
Víctor Asanza

2. Clasificación de señales de Electroencefalografía (EEG)
con redes neuronales en FPGA
Summary
• Introduction
• Field Programmable Gate Arrays (FPGAs)
• Implementation of a Classification System of EEG Signals Based on FPGA
• More FPGA projects
• On going Jobs
• Questions

3. Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG
signal clustering for motor and imaginary motor tasks on
hands and feet. In Ecuador Technical Chapters Meeting
(ETCM), 2017 IEEE (pp. 1-5). IEEE.
Distribution of 64 surface electrodes (10/10)64 surface EEG Electrodes International
System (10/10)
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). Supervised
pattern recognition techniques for detecting motor intention of
lower limbs in subjects with cerebral palsy. In Ecuador Technical
Chapters Meeting (ETCM), 2017 IEEE (pp. 1-5). IEEE.
Introduction

4. Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., &
Peláez, E. (2016, October). Clustering of EEG occipital signals using k-
means. In Ecuador Technical Chapters Meeting (ETCM), IEEE (pp. 1-5).
IEEE.
Asanza, V., Pelaez, E., & Loayza, F. (2017, October). EEG signal
clustering for motor and imaginary motor tasks on hands and feet.
In Ecuador Technical Chapters Meeting (ETCM), 2017 IEEE (pp. 1-
5). IEEE.
Kadoya et Al.
Kadoya, K., Lu, P., Nguyen, K., Lee-Kubli, C., Kumamaru, H., Yao, L., ... & Takashima, Y. (2016). Spinal cord
reconstitution with homologous neural grafts enables robust corticospinal regeneration. Nature medicine.
Introduction

5. Commercial EEG signal acquisition equipment
https://www.emotiv.com/ https://openbci.com/ (10/20)
Introduction

6. Introduction
ELECTRONIC PROTOTYPES DESIGN
For more information about Hardware Design, check the link:
ELECTRONIC PROTOTYPES DESIGN
https://vasanza.blogspot.com/p/shared-material.html
Estudiantes: Abel Silva & Jesús Miranda

7. Introduction
For more information about Hardware Design, check the link:
ELECTRONIC PROTOTYPES DESIGN
https://vasanza.blogspot.com/p/shared-material.html

8. Field Programmable Gate Arrays (FPGAs)
For more information about FPGA, check the link:
DIGITAL SYSTEMS 1, DIGITAL SYSTEMS 2, DIGITAL SYSTEMS DESIGN and VHDL
https://vasanza.blogspot.com/p/shared-material.html

9. Field Programmable Gate Arrays (FPGAs)
Arreglos de puertas lógicas programable
For more information about FPGA, check the link:
DIGITAL SYSTEMS 1, DIGITAL SYSTEMS 2, DIGITAL SYSTEMS DESIGN and VHDL
https://vasanza.blogspot.com/p/shared-material.html

10. Field Programmable Gate Arrays (FPGAs)
DE10NANO - TerasicArquitectura H/S Processor - Cyclone V
NIOS II
processor
For more information about FPGA, check the link:
DIGITAL SYSTEMS 1, DIGITAL SYSTEMS 2, DIGITAL SYSTEMS DESIGN and VHDL
https://vasanza.blogspot.com/p/shared-material.html

11. Implementation of a Classification System of EEG Signals Based on
FPGA
V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG
Signals Based on FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment
(ICEDEG), Buenos Aires, Argentina, 2020, pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752.
EEG Signals
Data Set
Signals
Preprocessing
Features Extraction Classification

12. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands
64 surface EEG Electrodes International
System 10-10
DC artifact present on the 64 electrodes of the
EEG signal
EEG-BCI (0,1 - 100)Hz, (10uV - 10mV)
EEG Signals
Data Set
Signals
Preprocessing
7-30 Hz
Features Extraction Classification
V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG Signals Based on
FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina, 2020,
pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752.

13. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands
0.5 –4 Hz
• Delta
waves
• Sleep
REM
4 –7 Hz
• Theta
waves
• Meditation
8 –12 Hz
• Alpha waves
• Relax
• μ waves (7,5-
12,5Hz)
• Imaginary Motor
16 –31 Hz
• Beta waves
• Alert
• Concentration
32 –110 Hz
• Gamma
• High brain
activity
V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG Signals Based on
FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina, 2020,
pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752.

14. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands
RMS(PSD(1:64))
64 surface EEG Electrodes
Periodogram estimation of the Power
Spectral Density (PSD)
Time-domain representation of the EEG
signal from motor activity of both feet
(ME2)
EEG Signals
Data Set
Signals
Preprocessing
7-30 Hz
Features Extraction Classification
V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG Signals Based on
FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina, 2020,
pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752.

15. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands
656 samples (4,1s / Fs 160Hz)
x 64 surface EEG Electrodes
Electrodes
S
A
M
P
L
E
S
1 …. 64
0x01 0x32
.
.
.
.
0x25 0x21
64
656
• EEG Signals Data Set
• https://physionet.org/
• 8 healthy subjects
⁓7 motor activity of both feet (ME2)
⁓9 imaginary motor activity of both feet (IE2)
Features
64 x 1
Label
S
A
M
P
L
E
S
[1 0]
[1 0]
[1 0]
[1 0]
[1 0]
128
65
EEG Signals
Data Set
Signals
Preprocessing
7-30 Hz
Features Extraction Classification
V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG Signals Based on
FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina, 2020,
pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752.

16. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands
Block diagram of data processing in FPGA
NIOS II processor
Diagram of the pattern recognition function of neural
networks in Simulink Multi-Layer Perceptron (MLP)
EEG Signals
Data Set
Signals
Preprocessing
7-30 Hz
Features Extraction Classification
V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG Signals Based on
FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina, 2020,
pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752.

17. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands
Confusion Matrix of the classification
of all events
Logic utilization (Cyclone V)
1,303 / 41,910
(4%)
Total block memory bits
47,360 /5,662,720
(<1%)
Total pins
45/499
(9%)
Resources used by the fpga
Analysis of Results
crossval(trainedClassifier.Classification, 'KFold', 5);
• ME2 events with 92,1% accuracy
• IE2 events with 93,8% accuracy
Time to look for the file in the SD 21,26 [us]
Time to open the file in the SD 22,30 [us]
Processing time of the neural
network
27,36 [us]
V. Asanza, A. Constantine, S. Valarezo and E. Peláez, "Implementation of a Classification System of EEG Signals Based on
FPGA," 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG), Buenos Aires, Argentina, 2020,
pp. 87-92, doi: 10.1109/ICEDEG48599.2020.9096752.

18. More FPGA projects

19. More FPGA projects
Asanza V., Sanchez G., Cajo R., Peláez E. (2021) Behavioral Signal Processing with Machine Learning Based on FPGA. In:
Botto-Tobar M., Zamora W., Larrea Plúa J., Bazurto Roldan J., Santamaría Philco A. (eds) Systems and Information Sciences.
ICCIS 2020. Advances in Intelligent Systems and Computing, vol 1273. Springer, Cham. https://doi.org/10.1007/978-3-030-
59194-6_17
Behavioral Signal Processing with Machine Learning Based on FPGA
Overview of our proposed architecture
Results obtained while testing different ser of neurons in
Hidden Layer
Resources used by FPGA

20. More FPGA projects
V. A. Armijos, N. S. Chan, R. Saquicela and L. M. Lopez, "Monitoring of system memory usage embedded in FPGA," 2020
International Conference on Applied Electronics (AE), Pilsen, Czech Republic, 2020, pp. 1-4, doi:
10.23919/AE49394.2020.9232863.
Monitoring of system memory usage embedded in FPGA
Comparison in Usage of memory vs. Time Comparison of memory usage
Representation of communication between FPGA and HPS

21. More FPGA projects
C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Recognition for Gestures
Communication with Limited Hardware Resources," 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced &
Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City
Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Leicester, United Kingdom, 2019, pp. 812-817.
k-NN-Based EMG Recognition for Gestures Communication with Limited Hardware Resources

22. More FPGA projects
Innovate FPGA 2019: Artificial Intelligence at the Edge!
http://www.innovatefpga.com/cgi-bin/innovate/teams.pl?Id=AS027
Artificial Neural Network based EMG recognition for gesture communication

23. More FPGA projects
Innovate FPGA 2019: Artificial Intelligence at the Edge!
http://www.innovatefpga.com/cgi-bin/innovate/teams.pl?Id=AS027
Artificial Neural Network based EMG recognition for gesture communication

24. More FPGA projects
EMG Signal Processing with Clustering Algorithms for motor gesture Tasks
Asanza, V., Peláez, E., Loayza, F., Mesa, I., Díaz, J., & Valarezo, E. (2018, October). EMG Signal Processing with Clustering
Algorithms for motor gesture Tasks. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) (pp. 1-6). IEEE
https://www.myo.com/

25. On going jobs
Robotic arm control (Youbionic) using EMG/EEG signals

26. For more information
Víctor Asanza
Mail: vasanza@espol.edu.ec
Facultad de Ingeniería en Electricidad y Computación, FIEC
Escuela Superior Politécnica del Litoral, ESPOL
Campus Gustavo Galindo Km 30.5 Vía Perimetral, P.O. Box 09-01-5863
090150 Guayaquil, Ecuador
Personal Blog: Orcid: ResearchGate: Google Schoolar:

27. Thank you!