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Clasificación de señales de
Electroencefalografía (EEG) con redes
neuronales en FPGA
Victor Asanza
Clasificación de señales de Electroencefalografía
(EEG) con redes neuronales en FPGA
Agenda
• Introducción
• Clustering of...
Introducción
Astrand, E., Wardak, C., & Ben Hamed, S. (2014). Selective
visual attention to drive cognitive brain–machine ...
Introducción
Equipos Comerciales de adquisición de señales EEG:
https://www.emotiv.com/ https://openbci.com/
Prototipos:
E...
Introducción
Introducción
Biosignals Computer
Interface
User Interface
Human-Machine
Interaction
Emotional Communications
(Social Disab...
Clustering of EEG Occipital Signals using K-means
Asanza, V., Ochoa, K., Sacarelo, C., Salazar, C., Loayza, F., Vaca, C., ...
Clustering of EEG Occipital Signals using K-means
Emotiv EEG electrode locations EEG Signal Acquisition
Emotiv
Frequency g...
Clustering of EEG Occipital Signals using K-means
SUBJECTS
Number of healthy volunteers: 5
Repeat an experiment : 10 times...
Clustering of EEG Occipital Signals using K-means
FEATURES
Variance (Time and Frequency): Var(t,f)
Covariance (Time and Fr...
Clustering of EEG Occipital Signals using K-means
EEG data
acquisition
Signals
Preprocessing
Features
Extraction
Features
...
EEG Signal Clustering for Motor and Imaginary Motor Tasks on
Hands and Feet
Asanza, V., Pelaez, E., & Loayza, F. (2017, Oc...
Data Set
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
• 25 Healthy subjects using a BCI-200...
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
64 surface EEG Electrodes International System...
Methodology and Results
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
EEG Signals
Data Set
S...
Methodology and Results
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
EEG Signals
Data Set
S...
Methodology and Results
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
EEG Signals
Data Set
S...
Electrodes
S
A
M
P
L
E
S
1 …. 64
0x01 0x32
.
.
.
.
0x25 0x21
656 samples (4,1s / Fs 160Hz)
x 64 surface EEG Electrodes
64
...
Methodology and Results
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
EEG Signals
Data Set
S...
Analysis of Results
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Percent success of all clu...
Field Programmable Gate Arrays (FPGAs)
Field Programmable Gate Arrays (FPGAs)
Arreglos de puertas lógicas programable
Field Programmable Gate Arrays (FPGAs)
DE10NANO - TerasicArquitectura H/S Processor - Cyclone V
NIOS II
processor
Implementation of a Classification System of EEG Signals Based on
FPGA
Asanza, V., Constantine, A., Valarezo, S., & Peláez...
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
64 surface EEG Electrodes International
System...
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
EEG Signals
Data Set
Signals
Preprocessing
7-3...
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
656 samples (4,1s / Fs 160Hz)
x 64 surface EEG...
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Block diagram of data processing in FPGA
NIOS ...
EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
Confusion Matrix of the classification
of all ...
Otros proyectos con FPGA
Otros proyectos con FPGA
Equipos Comerciales de adquisición de
señales de Electromiografía (EMG):
Asanza, V., Peláez, E., ...
Otros proyectos con FPGA
C. Cedeño Z., J. Cordova-Garcia, V. Asanza A., R. Ponguillo and L. Muñoz M., "k-NN-Based EMG Reco...
Otros proyectos con FPGA
Estudiantes:
• Galo Sánchez
• Juan Solano
Otros proyectos con FPGA
¡Gracias !
¿Preguntas?
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⭐⭐⭐⭐⭐ 2020 TELTEC WEBINAR: Clasificación de señales de Electroencefalografía (#EEG) con redes neuronales en #FPGA

Herunterladen, um offline zu lesen

IV Jornada de Telecomunicaciones - #TELTEC_2020

Agenda:
✅ Introducción
✅ Clustering of #EEG Occipital Signals using #K_means
⇨ 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.

⇨ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet
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.

✅ Field Programmable Gate Arrays (#FPGAs)

✅ Implementation of a Classification System of EEG Signals Based on FPGA

✅ Otros proyectos con FPGA
⇨ 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.
⇨ 2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA)

✅ Preguntas

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⭐⭐⭐⭐⭐ 2020 TELTEC WEBINAR: Clasificación de señales de Electroencefalografía (#EEG) con redes neuronales en #FPGA

  1. 1. Clasificación de señales de Electroencefalografía (EEG) con redes neuronales en FPGA Victor Asanza
  2. 2. Clasificación de señales de Electroencefalografía (EEG) con redes neuronales en FPGA Agenda • Introducción • Clustering of EEG Occipital Signals using K-means • EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet • Field Programmable Gate Arrays (FPGAs) • Implementation of a Classification System of EEG Signals Based on FPGA • Otros proyectos con FPGA • Preguntas
  3. 3. Introducción Astrand, E., Wardak, C., & Ben Hamed, S. (2014). Selective visual attention to drive cognitive brain–machine interfaces: from concepts to neurofeedback and rehabilitation applications. Frontiers in systems neuroscience, 8, 144. Different recording methods used to control BMIs 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.
  4. 4. Introducción Equipos Comerciales de adquisición de señales EEG: https://www.emotiv.com/ https://openbci.com/ Prototipos: Estudiantes: • Abel Silva • Jesús Miranda
  5. 5. Introducción
  6. 6. Introducción Biosignals Computer Interface User Interface Human-Machine Interaction Emotional Communications (Social Disability) Collective Humans Interactions Assistive Devices (Physical Disability) Robotics prosthetics Applications
  7. 7. Clustering of EEG Occipital Signals using K-means 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. Kadoya et Al.
  8. 8. Clustering of EEG Occipital Signals using K-means Emotiv EEG electrode locations EEG Signal Acquisition Emotiv Frequency generator F1: 5-9Hz F2: 24-29Hz EEG data acquisition Signals Preprocessing Features Extraction Features Selection Classification
  9. 9. Clustering of EEG Occipital Signals using K-means SUBJECTS Number of healthy volunteers: 5 Repeat an experiment : 10 times EMOTIV EPOC Sampling Rate: 128 samples por second Channels: 14 Resolution: 14 bits VISUAL STIMULATION Frequency: Cluster 1: 5, 6, 7, 8, 9 Cluster 2: 24, 26, 27, 28, 29 Hz Duration Time: 19,5 seconds Distribution of the 2 occipital electrodes Emotiv equipment. EEG data acquisition Signals Preprocessing Features Extraction Features Selection Classification Visual stimuli generated by a display with LEDs used to acquire the occipital EEG signals.
  10. 10. Clustering of EEG Occipital Signals using K-means FEATURES Variance (Time and Frequency): Var(t,f) Covariance (Time and Frequency): Cov(t,f) Correlation (Time and Frequency): Corr(t,f) Index Maximun Frequency: WhichMax(f) Minimum, Maximum, Median: Time and Frequency DC artifacts present in the occipital EEG signals 5Hz visual stimulus. EEG signal whithout DC artifacts in the 2 electrodes of the occipital area. 14,5 seg. EEG data acquisition Signals Preprocessing Features Extraction Features Selection Classification
  11. 11. Clustering of EEG Occipital Signals using K-means EEG data acquisition Signals Preprocessing Features Extraction Features Selection Classification Analysis of Results
  12. 12. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet 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.
  13. 13. Data Set EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet • 25 Healthy subjects using a BCI-2000 system • Available on the Physio Net website • Https://www.physionet.org/ • European Data Format (EDF) files. • Sampling frequency of 160Hz • Motor activity: open and close both hands or both feet • ⁓7 Both hands (T3) • ⁓7 Both feet (T4) • Imaginary motor activity: opening and closing both hands or both feet • ⁓9 Both hands (T1) • ⁓9 Both feet (T2).
  14. 14. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet 64 surface EEG Electrodes International System 10-10 DC artifact present on the 64 electrodes of the.
  15. 15. Methodology and Results EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet EEG Signals Data Set Signals Preprocessing Features Extraction Features Selection Classification Frequency analysis with the FFT of the original EEG signals Bandpass filter Buttherworth-IIR, 7-30 Hz (Mu 9-11Hz; Beta 12-30Hz) Frequency analysis with the FFT of the filtered EEG signals
  16. 16. Methodology and Results EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet EEG Signals Data Set Signals Preprocessing Features Extraction Features Selection Classification • Power Spectral Density (PSD) features • Maximum PSD value • Frequency • Arithmetic mean • Variance • 64 electrodes x 4 features
  17. 17. Methodology and Results EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet EEG Signals Data Set Signals Preprocessing Features Extraction Features Selection Classification Maximum PSD value and frequency occur in the 21 electrodes located in the motor cortex Maximum PSD value and frequency using unfiltered EEG signals
  18. 18. Electrodes S A M P L E S 1 …. 64 0x01 0x32 . . . . 0x25 0x21 656 samples (4,1s / Fs 160Hz) x 64 surface EEG Electrodes 64 656 Features 64 x 4 E X A M P L E EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet 25 Healthy subjects ⁓14 motor and ⁓18 imaginary motor activity 800 256
  19. 19. Methodology and Results EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet EEG Signals Data Set Signals Preprocessing Features Extraction Features Selection Classification K-means algorithm, with nine centroids K-medoids algorithm, with nine centroids Spectral Clustering results Results of Hierarchical Clustering
  20. 20. Analysis of Results EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet Percent success of all clustering algorithms Clusters: 1. T1 Imaginary motor activity/tasks of both hands 2. T2 Imaginary motor activity/tasks of both feet 3. T3 Motor activity/tasks of both hands 4. T4 Motor activity/tasks of both feet
  21. 21. Field Programmable Gate Arrays (FPGAs)
  22. 22. Field Programmable Gate Arrays (FPGAs) Arreglos de puertas lógicas programable
  23. 23. Field Programmable Gate Arrays (FPGAs) DE10NANO - TerasicArquitectura H/S Processor - Cyclone V NIOS II processor
  24. 24. Implementation of a Classification System of EEG Signals Based on FPGA Asanza, V., Constantine, A., Valarezo, S., & Peláez, E. (2020, April). Implementation of a Classification System of EEG Signals Based on FPGA. In 2020 Seventh International Conference on eDemocracy & eGovernment (ICEDEG) (Buenos Aires, Argentina). Kadoya et Al.
  25. 25. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet 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 (Mu 9-11Hz; Beta 12-30Hz)
  26. 26. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet EEG Signals Data Set Signals Preprocessing 7-30 Hz Features Extraction Classification 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)
  27. 27. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet 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
  28. 28. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet 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
  29. 29. EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet 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]
  30. 30. Otros proyectos con FPGA
  31. 31. Otros proyectos con FPGA Equipos Comerciales de adquisición de señales de Electromiografía (EMG): 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/
  32. 32. Otros proyectos con FPGA 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. • Carlos Cedeño • Miguel Daquilema Estudiantes:
  33. 33. Otros proyectos con FPGA Estudiantes: • Galo Sánchez • Juan Solano
  34. 34. Otros proyectos con FPGA
  35. 35. ¡Gracias ! ¿Preguntas?
  • ssuser1085bc

    Apr. 29, 2021

IV Jornada de Telecomunicaciones - #TELTEC_2020 Agenda: ✅ Introducción ✅ Clustering of #EEG Occipital Signals using #K_means ⇨ 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. ⇨ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and Feet 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. ✅ Field Programmable Gate Arrays (#FPGAs) ✅ Implementation of a Classification System of EEG Signals Based on FPGA ✅ Otros proyectos con FPGA ⇨ 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. ⇨ 2019: Artificial Neural Network based EMG recognition for gesture communication (InnovateFPGA) ✅ Preguntas

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