⭐⭐⭐⭐⭐ IX Jornadas Académicas y I Congreso Científico de Ciencias e Ingeniería: Clasificación de señales de Electroencefalografía #EEG con Redes Neuronales #NN en #FPGA

•

0 likes•8,213 views

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/

Education

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

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

FPGA
DE10-STANDARD
System on Programmable Chip
(SoPC)

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

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

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)

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

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

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

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]

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

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/

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

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

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

Este trabajo presenta el diseño experimental para el registro de señales de electroencefalografía (EEG) en 20 sujetos sometidos a potenciales evocados visualmente en estado estable (SSVEP). Además, la implementación de un sistema de clasificación basado en las señales SSVEP-EEG de la región occipital del cerebro obtenidas con el dispositivo Emotiv EPOC. ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ 2020 #IEEE #IES #UPS #Cuenca: Clasificación de señales de Electroencefa...

Victor Asanza

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

⭐⭐⭐⭐⭐ SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi

Victor Asanza

This work presents the experimental design for recording Electroencephalography (EEG) signals in 20 test subjects submitted to Steady-state visually evoked potential (SSVEP). The stimuli were performed with frequencies of 7, 9, 11 and 13 Hz. Furthermore, the implementation of a classification system based on SSVEP-EEG signals from the occipital region of the brain obtained with the Emotiv EPOC device is presented. These data were used to train algorithms based on artificial intelligence in a Raspberry Pi 4 Model B. Finally, this work demonstrates the possibility of classifying with times of up to 1.8 ms in embedded systems with low computational capacity. ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ #BCI System using a Novel Processing Technique Based on Electrodes Sele...

Victor Asanza

This work proposes an end-to-end model architecture, from feature extraction to classification using an Artificial Neural Network. The feature extraction process starts from an initial set of signals acquired by electrodes of a Brain-Computer Interface (BCI). The proposed architecture includes the design and implementation of a functional six Degree-of-Freedom (DOF) prosthetic hand. A Field Programmable Gate Array (FPGA) translates electroencephalography (EEG) signals into movements in the prosthesis. We also propose a new technique for selecting and grouping electrodes, which is related to the motor intentions of the subject. We analyzed and predicted two imaginary motor-intention tasks: opening and closing both fists and flexing and extending both feet. The model implemented with the proposed architecture showed an accuracy of 93.7% and a classification time of 8.8y«s for the FPGA. These results present the feasibility to carry out BCI using machine learning techniques implemented in a FPGA card. ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ Implementation of a Classification System of #EEG Signals Based on #FPGA

Victor Asanza

In the field of prosthetics, different technologies have been incorporated in recent years to improve their development and control, likewise the application of Field-Programmable Gate Arrays (FPGA) related to the Biomedicine field has increased due to its flexibility to perform multiple instructions in a reduced amount of time. This paper presents the implementation of a classification system based on FPGA capable of classifying characterized data, representing an imaginary motor task and a motor task in lower extremities. A three-layer feed-forward neural network was designed in Matlab, testing different architectures to assess the performance of the classifier, using methods such as the confusion matrix and the ROC curve.

⭐⭐⭐⭐⭐ LECCIÓN SISTEMAS EMBEBIDOS, 2do Parcial (2020 PAO 1) C6

Victor Asanza

⭐⭐⭐⭐⭐ #FPGA Based Meteorological Monitoring Station

Victor Asanza

In this paper, we propose to implement a meteorological monitoring station using embedded systems. This model is possible thanks to different sensors that enable us to measure several environmental parameters, such as i) relative humidity, ii) average ambient temperature, iii) soil humidity, iv) rain occurrence, and v) light intensity. The proposed system is based on a field-programmable gate array device (FPGA). The proposed design aims at ensuring highresolution data acquisition and at predicting samples with precision and accuracy in real-time. To present the collected data, we develop also a web application with a simple and friendly user interface. ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ EMG Signal Processing with Clustering Algorithms for Motor Gesture Tasks

Victor Asanza

✅ Recent research shows the possibility of using electromyography #EMG electrical signals to control devices or prosthesis. ✅ The EMG signals are measured in muscles, such as the forearm. These signals can lead to determine the intentionality of the patient when performing some motor tasks, however the signals are susceptible to noise due to the voltage levels in microvolts that are acquired. ✅ In this work, the preprocessing of the EMG signals includes the design and test of a filter. ✅ Our designed filter allows eliminating any signal components from the electrical network or any other sources that are not EMG signals. Then, analyzing the frequency components and the distribution of the filtered EMG signals validated the preprocessing efficiency. ✅ Later, the filtered data was processed with K-means, DBSCAN and Hierarchical Clustering algorithms. ✅ The results show that the K-means clustering algorithm was able to group the nine gestures made by the subjects, while the DBSCAN and Hierarchical algorithms were not able to perform the nine clusters, otherwise they match the performance of clustering two groups combining gestures.

This research presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e. 0.5 - 30 Hz) into account to separate artifacts from seizures. It requires a reference seizure epoch of N-sec which can either be generated from a patient-specific seizure database (if available) or can be simulated by a simple mathematical model of seizure. The purpose of the algorithm is to reduce as much artifacts as possible without distorting the desired seizure events to be detected/diagnosed. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data: fully simulated, semi-simulated and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for detection of seizures from non-seizure epochs have been found to be easily distinguishable after artifacts are removed and consequently reduces the false alarms in seizure detection. Results from an extensive experiment with these datasets prove the efficacy of the proposed algorithm and hence this algorithm (with some modifications) is expected to be a future candidate for artifact removal not only in epilepsy diagnosis applications but also in other applications (e.g. BCI or other neuroscience studies).

EEG based Motor Imagery Classification using SVM and MLP

Dr. Rajdeep Chatterjee

Variants of Support Vector Machines (SVM) were employed for classification and also compared the results with Multi-layered Perceptron (MLP). Empirical results show that both SVM and MLP were suitable for such motor imagery classifications with the accuracies 85% and 85.71% respectively. Among all employed feature extraction techniques wavelet-based methods specifically the energy- entropy feature set gave promising results for both the classifiers.

Design and Implementation Data Receiver Application of Digital Electrocardiog...

Riko Saputra

Feature Extraction Techniques and Classification Algorithms for EEG Signals t...

Editor IJCATR

EEG (Electroencephalogram) signal is a neuro signal which is generated due the different electrical activities in the brain. Different types of electrical activities correspond to different states of the brain. Every physical activity of a person is due to some activity in the brain which in turn generates an electrical signal. These signals can be captured and processed to get the useful information that can be used in early detection of some mental diseases. This paper focus on the usefulness of EGG signal in detecting the human stress levels. It also includes the comparison of various preprocessing algorithms ( DCT and DWT.) and various classification algorithms (LDA, Naive Bayes and ANN.). The paper proposes a system which will process the EEG signal and by applying the combination of classifiers, will detect the human stress levels.

IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...

IRJET Journal

Electroencephalogram

classgoeducation

BCI FOR PARALYSES PATIENT CONVERTING AUDIO TO VIDEO

Harathi Devi Nalla

A machine learning algorithm for classification of mental tasks.pdf

PravinKshirsagar11

In this article, a contemporary tack of mental tasks on cognitive parts of humans is appraised using two different approaches such as wavelet transforms at a discrete time (DWT) and support vector machine (SVM). The put forth tack is instilled with the electroencephalogram (EEG) database acquired in real-time from CARE Hospital, Nagpur. Additional data is also acquired from a brain-computer interface (BCI). In the working model, signals from the database are wed out into different frequency sub-bands using DWT. Initially, updated statistical features are obtained from different frequency sub-bands. This type of representation defines the wavelet co-efficient which is introduced for reducing the measurement of data. Then, the projected method is realized using SVM for segregating both port and veracious hand movement. After segregation of EEG signals, results are achieved with an accuracy of 92% for BCI competition paradigm III and 97.89% for B-alert machine.

Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...

Md Kafiul Islam

BRAIN WAVE TECHNOLOGY - controlling electronic devices using brainwaves

LAKSHMI TEJA SAYABARAPU

With the growing dependence of mankind on the use of different electronic devices in day to day life, we want to have easy handling to these electronic devices and easiest one is the possible brain wave command to electronic devices without even to use of hands. Great advances have been achieved towards brain-computer interface (BCI) or controlling electronic devices with brain waves. Forget about keyboard, mouse, touch screens or even voice recognition: the real dream is thinking about what we want our electronic gadget to do. Imagine a future where we can move anything with just our mind. The idea of interfacing minds with machines has long captured the human imagination. Brainwaves are tiny electrical impulses released when a neuron fires in the brain. Brain-computer interface (BCI) technology works by monitoring these electrical impulses with a forehead sensor. The neural signals are input into our Think Gear chip, and interpreted with our patented Attention and Meditation algorithms. The measured electrical signals and calculated interpretations are then output as digital messages to the computer, toy, or mobile device, allowing you to see your brainwaves on the screen, or use your brainwaves to affect the device’s behavior. The four main types of brainwave patterns are delta, theta, alpha, and beta, and these can be detected and interpreted and signals sent wirelessly to devices to control them. The interface enables a direct communications pathway between the brain and the object to be controlled. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. BCI (brain–computer interface) has long been a favorite of sci-fi movies. A few paralyzed patients could soon be using a wireless brain-computer interface able to stream their thought commands as quickly as a home Internet connection

Improved feature exctraction process to detect seizure using CHBMIT-dataset ...

IJECEIAES

One of the most dangerous neurological disease, which is occupying worldwide, is epilepsy. Fraction of second nerves in the brain starts impulsion i.e. electrical discharge, which is higher than the normal pulsing. So many researches have done the investigation and proposed the numerous methodology. However, our methodology will give effective result in feature extraction. Moreover, we used numerous number of statistical moments features. Existing approaches are implemented on few statistical moments with respect to time and frequency. Our proposed system will give the way to find out the seizure-effected part of the brain very easily using TDS, FDS, Correlation and Graph presentation. The resultant value will give the huge difference between normal and seizure effected brain. It also explore the hidden features of the brain.

Artificial neural networks

Tharushi Ruwandika

Telemetry 101: Exploring the New ADInstruments’ Small Animal Telemetry Systems

InsideScientific

Telemetry holds an important place in many in vivo physiology and neuroscience studies. Join Phil Griffiths, PhD for a technology overview of these unique small animal telemetry systems. In recent years significant technological advances have been made to improve the quality and fidelity of data collected via telemetry. This webinar will explore the benefits of using telemetry and discuss the specific advantages of ADInstruments’ new small animal telemetry systems, including wireless power technology for continuous, long-term recording and the integration of Millar Mikro-Tip® pressure sensors for increased accuracy and fidelity. Finally, Phil will highlight a number of common applications of the telemetry systems and showcase some exciting publications from existing users. These applications range from cardiovascular physiology and intracranial pressure measurements to epilepsy and stroke models. Key Topics Include: - What are the benefits of telemetry over other techniques for recording physiological parameters in vivo? - What are the advantages of wireless power technology in telemetry studies? - How does incorporating Millar Mikro-Tip® pressure sensors enhance telemetry studies? - How could ADInstruments telemetry fit into my physiology or neuroscience experiments?

Artificial neural network for misuse detection

Sajan Sahu

A new eliminating EOG artifacts technique using combined decomposition method...

TELKOMNIKA JOURNAL

Normally, the collected EEG signals from the human scalp cortex by using the non-invasive EEG collection methods were contaminated with artifacts, like an eye electrical activity, leading to increases in the challenges in analyzing the electroencephalogram for obtaining useful clinical information. In this paper, we do a comparison of using two decomposing methods (DWT and EMD) with CCA technique or High Pass Filter, for the elimination of eye artifacts from EEG. The eye artifacts (EOG) signals were extracted from the un-cleaned or raw EEG signals by DWT and EMD with CCA approach or H.P.F. The root means square error ratio of the uncontaminated EEG signal to the contaminated EEG signal with eye artifacts were the performance indicators for both elimination methods, which indicate that the combined CCA method outperforms the combined H.P.F method in the elimination of eye blinking contamination artifact from the EEG signal.

PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...

Md Kafiul Islam

9099163.pdf

PravinKshirsagar11

This article focuses on providing solutions for one important application termed as agriculture. In India, one major occupation for people living in urban and rural areas is agriculture where an economic rate depends only on the crops they yield. In such cases, if an intelligent monitoring device is not integrated then it becomes difficult for the farmers to grow their crops and to accomplish marginal income from what they have invested. Also existing methods have been analyzed in the same field where some devices have been installed and checked for increasing the productivity of horticulture crops. But existing methods fail to install an intelligent monitoring device that can provide periodic results within short span of time. Therefore, a sensor based technology with Internet of Things (IoT) has been implemented in the projected work for monitoring major parameters that support the growth and income of farmers. Also, an optimization algorithm for identifying the loss in different crops has been incorporated for maximizing the system boundary and to transmit data to farmers located in different areas. To prove the cogency of proposed method some existing methods have been compared and the results prove the projected technique produces improved results for about 58%.

Noise analysis & qrs detection in ecg signalsHarshal Ladhe

⭐⭐⭐⭐⭐ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and ...

Victor Asanza

✅ Modern technologies uses Brain Computer Interfaces (BCI) to control devices or prosthesis for people with physical impairments. In some cases, EEG data are used to determine the intentionality of the subject when performing motor and imaginary motor tasks. ✅ However, EEG signals are very susceptible to noise due to the lower voltage levels that are acquired. We used a data set of 64 EEG recordings of 25 subjects while they were doing motor and imaginary motor movements of hands and feets. Data were preprocessing, including the design of a filter for noise reduction outside the expected frequency spectral that operate the EEG signals. ✅ Then, we used features extraction based on spectral density. Finally, the application of five clustering algorithms to detect motor and imaginary motor tasks. ✅ Results showed that the k-means, k-medoids and hierarchical clustering algorithms were better in detecting motor activity, and hierarchical clustering for imaginary tasks of hands. ✅ Finally, the results show that k-means, k-medoids and Hierarchical clustering algorithms have a better performance detecting motor activity of both hands, but the spectral clustering algorithm has a better performance in the detection of motor tasks of both feet.

⭐⭐⭐⭐⭐ 2020 TELTEC WEBINAR: Clasificación de señales de Electroencefalografía ...

Victor Asanza

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

What's hot

IRJET- Analysis of Electroencephalogram (EEG) Signals

IRJET Journal

Reni_and_Sam_Poster_Final_449Reni Magbagbeola

Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...

Md Kafiul Islam

EEG based Motor Imagery Classification using SVM and MLP

Dr. Rajdeep Chatterjee

Design and Implementation Data Receiver Application of Digital Electrocardiog...

Riko Saputra

Feature Extraction Techniques and Classification Algorithms for EEG Signals t...

Editor IJCATR

IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...

IRJET Journal

Electroencephalogram

classgoeducation

BCI FOR PARALYSES PATIENT CONVERTING AUDIO TO VIDEO

Harathi Devi Nalla

A machine learning algorithm for classification of mental tasks.pdf

PravinKshirsagar11

Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...

Md Kafiul Islam

BRAIN WAVE TECHNOLOGY - controlling electronic devices using brainwaves

LAKSHMI TEJA SAYABARAPU

Improved feature exctraction process to detect seizure using CHBMIT-dataset ...

IJECEIAES

Artificial neural networks

Tharushi Ruwandika

Telemetry 101: Exploring the New ADInstruments’ Small Animal Telemetry Systems

InsideScientific

Artificial neural network for misuse detection

Sajan Sahu

A new eliminating EOG artifacts technique using combined decomposition method...

TELKOMNIKA JOURNAL

PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...

Md Kafiul Islam

9099163.pdf

PravinKshirsagar11

Noise analysis & qrs detection in ecg signalsHarshal Ladhe

What's hot (20)

IRJET- Analysis of Electroencephalogram (EEG) Signals

Reni_and_Sam_Poster_Final_449

Poster Presentation on "Artifact Reduction from Scalp EEG for Epilepsy Seizur...

EEG based Motor Imagery Classification using SVM and MLP

Design and Implementation Data Receiver Application of Digital Electrocardiog...

Feature Extraction Techniques and Classification Algorithms for EEG Signals t...

IRJET - FPGA based Electrocardiogram (ECG) Signal Analysis using Linear Phase...

Electroencephalogram

BCI FOR PARALYSES PATIENT CONVERTING AUDIO TO VIDEO

A machine learning algorithm for classification of mental tasks.pdf

Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...

BRAIN WAVE TECHNOLOGY - controlling electronic devices using brainwaves

Improved feature exctraction process to detect seizure using CHBMIT-dataset ...

Artificial neural networks

Telemetry 101: Exploring the New ADInstruments’ Small Animal Telemetry Systems

Artificial neural network for misuse detection

A new eliminating EOG artifacts technique using combined decomposition method...

PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...

9099163.pdf

Noise analysis & qrs detection in ecg signals

Similar to ⭐⭐⭐⭐⭐ IX Jornadas Académicas y I Congreso Científico de Ciencias e Ingeniería: Clasificación de señales de Electroencefalografía #EEG con Redes Neuronales #NN en #FPGA

⭐⭐⭐⭐⭐ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and ...

Victor Asanza

⭐⭐⭐⭐⭐ 2020 TELTEC WEBINAR: Clasificación de señales de Electroencefalografía ...

Victor Asanza

⭐⭐⭐⭐⭐ 2020 WEBINAR: TECNOLOGÍA Y SALUD (Abril 2020)

Victor Asanza

Procesamiento de Señales Biomédicas de Electroencefalografía y Electromiografía Agenda: ✅ Introducción ⇨ 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. ⇨ 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. ✅ Resultados Obtenidos ⇨ 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

Scaling Down Instrumentation Deploying Analog Mixed Signal Technology

Shivaprasad Tilekar

A Brain Computer Interface Speller for Smart Devices

Mahmoud Helal

ThesisPresentation_UpdAshraf ElSharif

Emotiv epoc final

vipin sharma

Senior Project Student's Presentation on Design of EMG Signal Recording System

Md Kafiul Islam

Disha Mehrotra_Resume

Disha Mehrotra

Matthew Gray Summer 2015 PresentationMatthew Gray

Nikita Resumenikitasanjose

Eced

Mohanaprakash Karthikeyan

Eced ece department

Kesava Shiva

EEG signal background and real-time processing

Robert Oostenveld

Understanding large scale neural recordings ground truth data sets and the t-...

George Dimitriadis

Differential data processing for energy efficiency of wireless

Daniel Lim

Wireless sensor networks consist of many types of wireless sensors. Sensor nodes are used for event detection, event ID location detection, and motion control. Sensor nodes are also used to collect data and act as a gateway or router. However, the location in which the sensor node is used may be an environment where charging or maintaining power is difficult. Given that the lifetime of the sensor nodes has a great effect on the service life of the wireless sensor network, research has been conducted to maximize the senor nodes lifetime by efficiently using their energy. The differential data processing for the energy efficiency of the wireless sensor network proposed in this paper provides energy efficient communication by reducing the data size. The reduction is achieved by transmitting the difference values between the previously collected data and the currently collected data.

Bci

Mohamed Magdy

Brain wave controlled robot

Rahul Wagh

Bci

Mohamed Magdy

Inroduction to BCI

Mohamed Abdelaal

Similar to ⭐⭐⭐⭐⭐ IX Jornadas Académicas y I Congreso Científico de Ciencias e Ingeniería: Clasificación de señales de Electroencefalografía #EEG con Redes Neuronales #NN en #FPGA (20)

⭐⭐⭐⭐⭐ EEG Signal Clustering for Motor and Imaginary Motor Tasks on Hands and ...

⭐⭐⭐⭐⭐ 2020 TELTEC WEBINAR: Clasificación de señales de Electroencefalografía ...

⭐⭐⭐⭐⭐ 2020 WEBINAR: TECNOLOGÍA Y SALUD (Abril 2020)

Scaling Down Instrumentation Deploying Analog Mixed Signal Technology

A Brain Computer Interface Speller for Smart Devices

ThesisPresentation_Upd

Emotiv epoc final

Senior Project Student's Presentation on Design of EMG Signal Recording System

Disha Mehrotra_Resume

Matthew Gray Summer 2015 Presentation

Nikita Resume

Eced

Eced ece department

EEG signal background and real-time processing

Understanding large scale neural recordings ground truth data sets and the t-...

Differential data processing for energy efficiency of wireless

Bci

Brain wave controlled robot

Bci

Inroduction to BCI

More from Victor Asanza

⭐⭐⭐⭐⭐ Device Free Indoor Localization in the 28 GHz band based on machine lea...

Victor Asanza

By exploiting the received power change in a communication link produced by the presence of a human body in an otherwise empty room, this work evaluates indoor free device localization methods in the 28 GHz band using machine learning techniques. For this objective, a database is built using results from ray tracing simulations of a system comprised of 4 receivers and up to 2 transmitters, while a person is standing within the room. Transmitters are equipped with uniform linear arrays that switch their main beams sequentially at 21 angles, whereas the receivers operate with omnidirectional antennas. Statistical localization error reduction of at least 16% over a global-based classification technique can be obtained through the combination of two independent classifiers using one transmitter and a reduction of at least 19% for 2 transmitters. An additional improvement is achieved by combining each independent classifier with a regression algorithm. Results also suggest that the number of examples per class and size of the blocks (strips) in which the study area is partitioned play a role in the localization error.

⭐⭐⭐⭐⭐ SOLUCIÓN EXAMEN SISTEMAS DIGITALES 2, 1er Parcial (2022PAO2)

Victor Asanza

La siguiente partición funcional que incluye una Maquina Secuencial Sincrónica (MSS) y tres registros de sostenimiento, debe realizar el ingreso de datos a cada uno de los registros y luego permitirá encontrar el valor máximo y mínimo ingresado. Además, cada uno de los registros indicados es de 8 bits para mostrar los valores encontrados de máximo (Qmax) y mínimo (Qmin) serán de 8 bits cada uno. El sistema digital funciona con una MSS modelo Moore de la siguiente forma: 1. La MSS luego de ser reiniciado empieza en el estado inicial. 2. El Sistema Digital en el estado inicial, esperará que el usuario presione y suelte la tecla Start dos veces, luego de lo cual esperará el ingreso de datos. 3. El ingreso de datos se lo hará presentando un byte en la entrada Datos, presionando y soltando la tecla Load (el usuario deberá realizar este paso tres veces, uno por cada registro). 4. Luego de ingresar los 3 datos, el usuario deberá presionar y soltar la tecla Find. Esta señal es la que le indica a la MSS del Sistema Digital, que es momento de realizar la búsqueda del valor máximo y mínimo. 5. Una vez finalizado el proceso de búsqueda de los valores máximo y mínimo, se activará la salida Done. El valor máximo se guardará en el RegistroMax y se presentará en su salida Qmax, por otro lado, el valor mínimo se guardará en el RegistroMin y se presentará en su salida Qmin. 6. La señal Done, las salidas Qmax y Qmin se presentarán hasta que el usuario presione y suelte la tecla Start una vez, luego de lo cual la MSS regresará al estado inicial.

⭐⭐⭐⭐⭐ CV Victor Asanza

Victor Asanza

⭐⭐⭐⭐⭐ Trilateration-based Indoor Location using Supervised Learning Algorithms

Victor Asanza

The indoor positioning system (IPS) has a wide range of applications, due to the advantages it has over Global Positioning Systems (GPS) in indoor environments. Due to the biosecurity measures established by the World Health Organization (WHO), where the social distancing is provided, being stricter in indoor environments. This work proposes the design of a positioning system based on trilateration. The main objective is to predict the positioning in both the ‘x’ and ‘y’ axis in an area of 8 square meters. For this purpose, 3 Access Points (AP) and a Mobile Device (DM), which works as a raster, have been used. The Received Signal Strength Indication (RSSI) values measured at each AP are the variables used in regression algorithms that predict the x and y position. In this work, 24 regression algorithms have been evaluated, of which the lowest errors obtained are 70.322 [cm] and 30.1508 [cm], for the x and y axes, respectively. Published in: 2022 International Conference on Applied Electronics (AE) ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ Learning-based Energy Consumption Prediction

Victor Asanza

✅ Published in: https://doi.org/10.1016/j.procs.2022.07.035 As more people send information to the cloud-fog infrastructure, this brings many problems to the management of computer energy consumption. Therefore, energy consumption management of servers, fog devices and cloud computing platform should be investigated to comply with the Green IT requirement. In this paper, we propose an energy consumption prediction model consisting of several components such as hardware design, data pre-processing, characteristics extraction and selection. Our main goal is to develop a non-invasive meter based on a network of sensors that includes a microcontroller, the MQTT communication protocol and the energy measurement module. This meter measures voltage, current, power, frequency, energy and power factor while a dashboard is used to present the energy measurements in real-time. In particular, we perform measurements using a workstation that has similar characteristics to the servers of a Datacenter locate at the Information Technology Center in ESPOL, which currently provide this type of services in Ecuador. For convenience, we evaluated different linear regression models to select the best one and to predict future energy consumption based on the several measurements from the workstation during several hours which enables the consumer to optimize and to reduce the maintenance costs of the IT equipment. The supervised machine learning algorithms presented in this work allow us to predict the energy consumption by hours and by days. ⭐ The matlab code used for data processing are available in: https://github.com/vasanza/Matlab_Code/tree/EnergyConsumptionPredictionDatacenter ⭐ The dataset used for data processing are available in:https://ieee-dataport.org/open-access/data-server-energy-consumption-dataset ✅ Read more related topics: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ Raspberry Pi-based IoT for Shrimp Farms Real-time Remote Monitoring wit...

Victor Asanza

This project analyses the optimal parameters for the shrimp farming, trying to help the aquaculture of Ecuador, using a cyberphysical system, which includes temperature, salinity, dissolved oxygen, and pH sensors to monitor the water conditions and an embedded system to control it using an XBee andATMega328p microcontrollers to remotely activate and deactivate aerators to maintain the quality of each pool in neat conditions.

⭐⭐⭐⭐⭐Classification of Subjects with Parkinson's Disease using Finger Tapping...

Victor Asanza

La enfermedad de Parkinson es el segundo trastorno neurodegenerativo más común y afecta a más de 7 millones de personas en todo el mundo. En este trabajo, clasificamos a los sujetos con la enfermedad de Parkinson utilizando datos de la pulsación de los dedos en un teclado. Utilizamos una base de datos gratuita de Physionet con más de 9 millones de registros, preprocesada para eliminar los datos atípicos. En la etapa de extracción de características, obtuvimos 48 características. Utilizamos Google Colaboratory para entrenar, validar y probar nueve algoritmos de aprendizaje supervisado que detectan la enfermedad. Como resultado, conseguimos un grado de precisión superior al 98 %.

⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS EMBEBIDOS, 1er Parcial (2022 PAO1)

Victor Asanza

Examen 1er parcial que incluye temas de los capítulos: Capítulo 1, historia de los sistemas IoT y sistemas ciberfísicos. Capítulo 2, tipos de arquitecturas incluyendo las multiprocessor y multicore. Capítulo 3, donde se estudia las memorias FLASH, RAM, EEPROM. Capítulo 4, registros de configuraciones del ADC, PWM, comunicacion serial, I2C y SPI. ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ CHARLA #PUCESE Arduino Week: Hardware de Código Abierto TSC-LAB

Victor Asanza

✅ #PUCESE, organizó el webinar: "ARDUINO WEEK 2022 PUCESE" ✅ Arduino Week PUCE Esmeraldas- Charla con Expertos ➡️ This is an initiative developed by FIEC-ESPOL professors. Temperature and Speed Control Lab (TSC-LAB) is an open-source hardware development. ➡️ Topics 1- Introducción 2- Hardware de Código Abierto 3- Temperature and Speed Control Lab (TSC-LAB) 4- Códigos de ejemplo 5- Datasets 6- Publicaciones científicas 7- Proyectos 8- Cursos ⭐ Para más contenido visita nuestro blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN FUNDAMENTOS DE ELECTRICIDAD Y SISTEMAS DIGITALES, 2...

Victor Asanza

Problema 1A: (10%) Dado la siguiente expresión booleana que define el comportamiento de la señal de salida F sin minimizar, reducir dicha expresión usando mapas de Karnaugh (A, B, C, D) agrupando unos. Luego, seleccionar cuál de las siguientes opciones es la correcta. Problema 2: (10%) Dado la siguiente expresión booleana que define el comportamiento de la señal de salida F sin minimizar, reducir dicha expresión usando mapas de Karnaugh (A, B, C, D) agrupando unos. Luego, seleccionar cuál de las siguientes opciones es la correcta. Problema 3: (25%) Se desea diseñar un Sistemas Digital que capaz de controlar dos actuadores tipo bomba (A y B) en función del nivel de agua presente en un tanque. Este nivel de agua se monitorea con dos sensores (S0 y S1). El Sistemas Digital se muestra en la siguiente gráfica. Problema 5: (15%): Dado el siguiente circuito digital, primero obtener la expresión resultante y luego seleccionar el mapa que corresponde al funcionamiento de dicha expresión. Problema 6: (15%): Dado el siguiente circuito, encontrar la expresión booleana que define el comportamiento de la señal de salida F sin minimizar, luego reducir la expresión booleana usando mapas de Karnaugh (A, B, C, D) agrupando unos. Problema 7: (20%). En la siguiente gráfica se puede observar el registro de un electrodo de Electromiografía (EMG) durante la ejecución de una tarea motora en extremidad superior. La señal EMG tiene una amplitud en el orden de los microvoltio - milivoltios y es susceptible a ruido debido a la adherencia del electrodo utilizado, frecuencia cardiaca, red eléctrica, tejido adiposo, etc. Como se muestra en la Fig. 1 el análisis post adquisición en el dominio de la frecuencia de la señal EMG indica que existe ruido de baja frecuencia menores a 5Hz debido a ruidos relacionados a movimientos relativos y en 50 Hz debido a la red eléctrica. Las señales EMG tienen información en el rango de 7 a 20Hz, por lo cual se sugiere diseñar un filtro RC paso banda que permita eliminar el ruido de la señal EMG. ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ SOLUCIÓN LECCIÓN SISTEMAS DIGITALES 2, 2do Parcial (2021PAO2) C6

Victor Asanza

Problema #1 (50%) Dado el siguiente diagrama de un microprocesador genérico de 32 bits por instrucción de hasta 1023 instrucciones visto completamente en clase, que utiliza datos almacenados en memoria RAM (Register Files), como se muestra a continuación. Problema #2: (10%) ¿Cuáles de las siguientes afirmaciones referentes a las memorias de Instrucciones de un microprocesador son ciertas? Problema #3: (10%) ¿Cuáles de las siguientes afirmaciones referentes a las memorias EEPROM son ciertas? Problema #4: (10%) ¿Cuáles de las siguientes afirmaciones referentes a las memorias de datos (Register File) son ciertas? Problema #5: (20%) Shen et Al., escribió el paper titulado “An FPGA-based Distributed Computing System with Power and Thermal Management Capabilities” en donde desarrolla una plataforma computacional distribuida compuesta de múltiples FPGAs conectadas via Ethernet y cada FPGA está configurada como un sistema multi-core. Los núcleos en el mismo FPGA se comunican a través de la memoria compartida, mientras que diferentes FPGA se comunican a través de enlaces Ethernet, como se muestra en la siguiente gráfica. ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ Performance Comparison of Database Server based on #SoC #FPGA and #ARM ...

Victor Asanza

New emerging storage technologies have a great application for IoT systems. Running database servers on development boards, such as Raspberry or FPGA, has a great impact on effective performance when using large amounts of data while serving requests from many clients at the same time. In this paper, we designed and implemented an embedded system to monitor the access of a database using MySql database server installed on Linux in a standard FPGA DE10 with HPS resources. The database is designed to keep the information of an IoT system in charge of monitoring and controlling the temperature inside greenhouses. For comparison purposes, we carried out a performance analysis of the database service running on the FPGA and in a Raspberry Pi 4 B to determine the efficiency of the database server in both development cards. The performance metrics analyzed were response time, memory and CPU usage taking into account scenarios with one or more requests from clients simultaneously. ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ SOLUCIÓN EXAMEN SISTEMAS DIGITALES 2, 1er Parcial (2021PAO2)

Victor Asanza

⭐⭐⭐⭐⭐ SOLUCIÓN LECCIÓN FUNDAMENTOS DE ELECTRICIDAD Y SISTEMAS DIGITALES, 2do ...

Victor Asanza

Problema #1,2,3: (10%) El siguiente circuito es de un filtro paso banda. Los datos del circuito son los siguientes, R1 = 1K[Ω] y R2 = 1K[Ω]. ¿cuáles de las siguientes afirmaciones son correctas? Problema #4,5,6: (10%) El siguiente bloque convertidor analógico digital (ADC) de 8 bits de resolución, se tiene un voltaje de referencia de 5Vcc. ¿cuáles de las siguientes afirmaciones son correctas? ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS DIGITALES 1, 1er Parcial (2021 PAO1)

Victor Asanza

Problema #1 (x%). El siguiente es un Sistema Digital que tiene las señales ‘A’,’ B’, ‘C’ y ‘D’ como entradas de un bit; por otro lado, la señal ‘Y’ es una salida de un bit tal como se muestra en la siguiente imagen: El comportamiento de la señal de salida ‘Y’ en función de las señales de entrada, es descrito con el siguiente código VHDL: Código GitHub: https://github.com/vasanza/MSI-VHDL/blob/2021PAO1/ExamenParcial/ExamSD1_1.vhd Realizar los siguientes desarrollos: a) Usando mapas de karnaught y agrupamiento de minterms (SOP), simplificar la expresión booleana hasta obtener su minima expresión (x/2 %). b) Utilizando puertas lógicas, graficar el circuito que represente a la ecuación simplificada en el literal anterior (x/2 %). Problema #2 (x%). El siguiente es un Sistema Digital que tiene las señales ‘A’ y ‘B’ como entradas de dos bits; por otro lado, la señal ‘Y’ es una salida de dos bits tal como se muestra en la siguiente imagen: El comportamiento de la señal de salida ‘Y’ en función de las señales de entrada, es descrito con el siguiente código VHDL: Código GitHub: https://github.com/vasanza/MSI-VHDL/blob/2021PAO1/ExamenParcial/ExamSD1_2.vhd Realizar los siguientes desarrollos: a) Usando mapas de karnaught y agrupamiento de minterms (SOP), simplificar la expresión booleana hasta obtener su minima expresión de Y(1) = f(A(1),A(0),B(1),B(0)) y Y(0) = f(A(1),A(0),B(1),B(0)) (x/2 %). b) Indicar con sus propias palabras el funcioamiento que realiza el sistemas digital propuesto (x/2 %). ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ SISTEMAS DIGITALES 2, PROYECTOS PROPUESTOS (2021 PAO1)

Victor Asanza

⭐⭐⭐⭐⭐ SISTEMAS DIGITALES 1, PROYECTOS PROPUESTOS (2021 PAE)

Victor Asanza

Proyectos propuestos basados en MSS: VALOR MÍNIMO DE 3 NÚMEROS VALOR MÁXIMO DE 3 NÚMEROS VALOR PROMEDIO DE 4 NÚMEROS CONTADOR UP EN GRAY CONTADOR DOWN EN BCD VALIDADOR DE CLAVE DE 3 DIGITOS SUMADOR DE 3 NUMEROS BCD VALIDADOR DE 3 NÚMEROS ASCENDENTES VALIDADOR DE 3 NÚMEROS DESCENDENTE VALIDADOR DE 3 NÚMEROS MULTIPLOS DE BASE 2 ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ Localización en ambiente de interiores basado en Machine Learning con r...

Victor Asanza

⭐⭐⭐⭐⭐ SOLUCIÓN EVALUACIÓN SISTEMAS DIGITALES 1, 2do Parcial (2021 PAE)

Victor Asanza

Problema #1 (10%). El siguiente Sistema Digital permite resolver Mapas de Karnaugh de dos variables. Este sistema Digital tiene como entrada los cuatro valores presentes dentro del mapa y como salida tres Displays de 7 segmentos cátodo común que indiquen la resolución del mapa con la cantidad de grupos de uno, de dos y de cuatro. Este circuito siempre determina la resolución más eficiente, bajo la condición de que únicamente se tendrán valores de Ceros y Unos dentro del mapa; Código VHDL de la pregunta: https://github.com/vasanza/MSI-VHDL/tree/2021PAE/ExamenFinal Problema #2 (20%). El siguiente Sistema Digital funciona como una maquina secuencial modelo moore. Este sistema Digital tiene como entrada las señales: X0, X1, X2 y X3; y como salidas las señales: Q0 y Q1; tal como se presenta a continuación: Hay que recordar que las maquinas secuenciales sincrónicas están conformadas por tres bloques principales: Decodificador de estados siguientes, memoria de estados y decodificador de salidas. El decodificador de estados siguientes se representa con los siguientes multiplexores: Problema #3 (40%). El siguiente Sistema Digital funciona como una maquina secuencial modelo mealy. Este sistema Digital tiene como entrada la señal: A; y como salidas las señales: Q y P; tal como se presenta a continuación: Hay que recordar que las maquinas secuenciales sincrónicas están conformadas por tres bloques principales: Decodificador de estados siguientes, memoria de estados y decodificador de salidas. La memoria de estados implementado con Flip-Flops tipo D, el decodificador de estados siguientes y de salidas implementado con multiplexores se representa a continuación: La asignación de códigos de estado que deberá emplear es el siguiente: Se le pide: a) Completar los siguientes mapas de Karnaugh que describen el comportamiento de los decodificadores de salidas y de estados siguientes (20p). b) Realizar el diagrama de estados completo que describe el funcionamiento de la maquina secuencial sincrónica, utilizando el siguiente formato: A / Q, P. (20p). Problema #4 (30%). Utilizando el el registro universal 74194 en modo carga paralelo (S1=1 y S0=1), realizar el circuito que permita generar la siguiente secuencia: Se le pide: a) Completar la siguiente tabla de estados presentes y siguiente del registro universal 74194 (10P). b) Determinar la expresión booleana de las entradas en paralelo A, B, C y D (10P). c) Dibujar el circuito resultante utilizando puertas lógicas (no usar multiplexores) (10P). ⭐ For more information visit our blog: https://vasanza.blogspot.com/

⭐⭐⭐⭐⭐ CHARLA #PUCESE: Industrial Automation and Internet of Things Based on O...

Victor Asanza

✅ Contenido: Introduction AVR Architecture ➡️ Acquisition ➡️ Identification ➡️ Control Design ARM Architecture ➡️ GPIO Control Automation Solutions ➡️ Industrial Shields FPGA Architecture vs Hardware Design ➡️ Behavioral Signal Processing with Machine Learning Based on FPGA ➡️ More FPGA projects ➡️ On going jobs Future Work ⭐ For more information visit our blog: https://vasanza.blogspot.com/

More from Victor Asanza (20)