EEG SIGNALS Data Set Methodology and Results Analysis of Results Trabajos en cursor Resultados Obtenidos ⭐ For more information visit our blog: https://vasanza.blogspot.com/

1. Clasificación de señales de
Electroencefalografía (EEG) con redes
neuronales en FPGA
Víctor Asanza

2. FPGA
(Field-Programmable Gate Array)
Arreglos de Puertas Programables por Campos
Aplicaciones con FPGAs

3. FPGA
DE10-STANDARD
System on Programmable Chip
(SoPC)

4. EEG SIGNALS
64 surface EEG Electrodes
International System 10-20
DC artifact present on the 64 electrodes of
the EEG signal

5. 0.5–4 Hz
• Delta waves
• Sleep REM
4 –8 Hz
• Theta
waves
• Meditation
8 –15 Hz
• Alpha waves
• Relax
• μ waves (8-12Hz)
• Imaginary Motor
16-31 Hz
• Beta waves
• Alert
32 –110 Hz
• Gamma
waves
EEG SIGNALS

6. Data Set
• Emotiv Epoc
• 25 Healthy subjects
• Sampling frequency of 160Hz
• Task (open and close both hands or both feet)
• Motor activity/tasks of both hands (E1)
• Motor activity/tasks of both feet (E2)

7. Methodology and Results
EEG Signals
Signals
Preprocessing
Features
Extraction
Features
Selection
Classification
Frequency analysis with the FFT of
the original EEG signals
Bandpass filter
Buttherworth-IIR, 7-30 Hz
Frequency analysis with the
FFT of the filtered EEG signals

8. Methodology and Results
EEG Signals
Signals
Preprocessing
Features
Extraction
Features
Selection
Classification
• A periodogram (Welch PSD)
• Power Spectral Density (PSD) features
• Maximum PSD value
• Frequency
• Arithmetic mean
• Variance
• 64 electrodes x 4 features

9. Methodology and Results
EEG Signals
Signals
Preprocessing
Features
Extraction
Features
Selection
Classification
21 x 4 features in the imaginary motor task
both hands
Maximum PSD value and frequency occur in
the 21 electrodes located in the motor cortex

10. Analysis of Results
Clusters:
1. T3 Motor activity/tasks of both hands
2. T4 Motor activity/tasks of both feet
Time to open 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]

11. Trabajos en curso
Control de brazo robótico usando redes neuronales en señales EEG

12. Resultados Obtenidos
Paper aceptado en conferencia internacional
“k-NN-based EMG recognition for gestures communication with limited hardware resources”
http://www.smart-world.org/2019/uic/

13. Resultados Obtenidos
Tercer Lugar en concurso internacional
“Artificial Neural Network based EMG recognition for gesture communication”
http://www.innovatefpga.com/cgi-bin/innovate/teams.pl?Id=AS027

14. Resultados Obtenidos
http://www.innovatefpga.com/cgi-bin/innovate/teams.pl?Id=AS027

15. 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
For more information

16. Thank you!