⭐⭐⭐⭐⭐ 2020 #IEEE #IES #UPS #Cuenca: Clasificación de señales de Electroencefalografía (#EEG) con Redes Neuronales #NN en #FPGA
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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 #IEEE #IES #UPS #Cuenca: Clasificación de señales de Electroencefalografía (#EEG) con Redes Neuronales #NN en #FPGA
- 1. Clasificación de señales de Electroencefalografía (EEG) con redes neuronales en FPGA Víctor Asanza
- 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. 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. Introducción Equipos Comerciales de adquisición de señales EEG: https://www.emotiv.com/ https://openbci.com/ Prototipos: Estudiantes: • Abel Silva • Jesús Miranda
- 5. Introducción
- 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. 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. 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. 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. 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. Clustering of EEG Occipital Signals using K-means EEG data acquisition Signals Preprocessing Features Extraction Features Selection Classification Analysis of Results
- 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. 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. 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. 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. 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. 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. 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. 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. 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. Field Programmable Gate Arrays (FPGAs)
- 22. Field Programmable Gate Arrays (FPGAs) Arreglos de puertas lógicas programable
- 23. Field Programmable Gate Arrays (FPGAs) DE10NANO - TerasicArquitectura H/S Processor - Cyclone V NIOS II processor
- 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. 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. 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. 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. 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. 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. Otros proyectos con FPGA
- 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. 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. Otros proyectos con FPGA Estudiantes: • Galo Sánchez • Juan Solano
- 34. Otros proyectos con FPGA