scholarly journals EEG P300 wave detection using Emotiv EPOC+: Effects of matrix size, flash duration, and colors

2017 ◽  
Author(s):  
Saleh Alzahrani ◽  
Charles W Anderson
Keyword(s):  
P300 Amplitude ◽  
Eeg Signals ◽  
Flash Duration ◽  
Matrix Size ◽  
P300 Wave ◽  
P300 Speller ◽  
Wave Detection ◽  
The Matrix

Objective: The P300 signal is an electroencephalography (EEG) positive deflection observed 300 ms to 600 ms after an infrequent, but expected, stimulus is presented to a subject. The aim of this study was to investigate the capability of Emotiv EPOC+ headset to capture and record the P300 wave. Moreover, the effects of using different matrix sizes, flash duration, and colors were studied. Methods: Participants attended to one cell of either 6x6 or 3x3 matrix while the rows and columns flashed randomly at different duration (100 ms or 175 ms). The EEG signals were sent wirelessly to OpenViBE software, which is used to run the P300 speller. Results: The results provide evidence of capability of the Emotiv EPOC+ headset to detect the P300 signals from two channels, O1 and O2. In addition, when the matrix size increases, the P300 amplitude increases. The results also show that longer flash duration resulted in larger P300 amplitude. Also, the effect of using colored matrix was clear on the O2 channel. Furthermore, results show that participants reached accuracy above 70% after three to four training sessions. Conclusion: The results confirmed the capability of the Emotiv EPOC+ headset for detecting P300 signals. In addition, matrix size, flash duration, and colors can affect the P300 speller performance. Significance: Such an affordable and portable headset could be utilized to control P300-based BCI or other BCI systems especially for the out-of-the-lab applications.

scholarly journals EEG P300 wave detection using Emotiv EPOC+: Effects of matrix size, flash duration, and colors

2017 ◽  
Author(s):  
Saleh Alzahrani ◽  
Charles W Anderson
Keyword(s):  
P300 Amplitude ◽  
Eeg Signals ◽  
Flash Duration ◽  
Matrix Size ◽  
P300 Wave ◽  
P300 Speller ◽  
Wave Detection ◽  
The Matrix

Objective: The P300 signal is an electroencephalography (EEG) positive deflection observed 300 ms to 600 ms after an infrequent, but expected, stimulus is presented to a subject. The aim of this study was to investigate the capability of Emotiv EPOC+ headset to capture and record the P300 wave. Moreover, the effects of using different matrix sizes, flash duration, and colors were studied. Methods: Participants attended to one cell of either 6x6 or 3x3 matrix while the rows and columns flashed randomly at different duration (100 ms or 175 ms). The EEG signals were sent wirelessly to OpenViBE software, which is used to run the P300 speller. Results: The results provide evidence of capability of the Emotiv EPOC+ headset to detect the P300 signals from two channels, O1 and O2. In addition, when the matrix size increases, the P300 amplitude increases. The results also show that longer flash duration resulted in larger P300 amplitude. Also, the effect of using colored matrix was clear on the O2 channel. Furthermore, results show that participants reached accuracy above 70% after three to four training sessions. Conclusion: The results confirmed the capability of the Emotiv EPOC+ headset for detecting P300 signals. In addition, matrix size, flash duration, and colors can affect the P300 speller performance. Significance: Such an affordable and portable headset could be utilized to control P300-based BCI or other BCI systems especially for the out-of-the-lab applications.

scholarly journals A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3961
Author(s):  
Daniela De Venuto ◽  
Giovanni Mezzina
Keyword(s):  
Recognition Performance ◽  
Low Complexity ◽  
Single Trial ◽  
Eeg Signals ◽  
Speed Increase ◽  
P300 Speller ◽  
Embedded Platform ◽  
Temporal Features ◽  
High Recognition Accuracy ◽  
Car Driving

In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI), keeping high recognition accuracy performance. The architecture, designed to improve the portability of the algorithm, demonstrated full implementability on a dedicated embedded platform. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; thus, it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. The symbolized EEG signals are then sent to an autoencoder model to emphasize those temporal features that can be meaningful for the following CNN stage. This latter consists of a seven-layer CNN, including a 1D convolutional layer and three dense ones. Two datasets have been analyzed to assess the algorithm performance: one from a P300 speller application in BCI competition III data and one from self-collected data during a fluid prototype car driving experiment. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by +5.75 bits/min the state-of-the-art for this parameter. Jointly with the speed increase, the recognition performance returned disruptive results in terms of the harmonic mean of precision and recall (F1-Score), which achieve 51.78 ± 6.24%. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes. The realized network has been validated on an STM32L4 microcontroller target, for complexity and implementation assessment. The implementation showed an overall resource occupation of 5.57% of the total available ROM, ~3% of the available RAM, requiring less than 3.5 ms to provide the classification outcome.

scholarly journals P300 and decision-making in neuromarketing

2021 ◽  
Vol 4 (3) ◽  
pp. 21-26
Author(s):  
Aida Azlina Mansor ◽  
Salmi Mohd Isa ◽  
Syaharudin Shah Mohd Noor
Keyword(s):  
Decision Making ◽  
Test Methods ◽  
Event Related Potential ◽  
P300 Amplitude ◽  
P300 Latency ◽  
Decision Making Process ◽  
Related Research ◽  
P300 Wave ◽  
Human Event ◽  
Marketing Methods

Neuromarketing provides insights into consumers' decision-making that traditional marketing test methods cannot offer. The foundation in the process of decision-making is P300. Thus, the P300 wave is a potential Event-Related Component (ERP) used to measure consumers' decision-making process. The P300 wave represents a positive transition in human event-related potential. Therefore, the P300 is determined by measuring the amplitude and latency of the consumers. A higher P300 amplitude indicates greater confidence in the decision-making process, while a longer P300 latency indicates lower attentiveness. Thus, P300 in neuroscience, which investigates customers' responses in-depth, cannot be accomplished by typical marketing methods. For many years, P300 components such as attitudes, preferences, and information-based decision-making have been examined extensively in marketing-related research. However, a review of an ERP in neuromarketing method is fewer reported. This mini review describes some analysis on P300 and decision-making by several researchers.

scholarly journals Modelling and Simulation of Moored-Floating Structures Using the Tension-Element-Method

2018 ◽  
Author(s):  
Tobias Martin ◽  
Arun Kamath ◽  
Hans Bihs
Keyword(s):  
Fluid Structure Interaction ◽  
Steady State Solution ◽  
Immersed Boundary ◽  
Matrix Size ◽  
Fluid Structure ◽  
Floating Structures ◽  
Structure Interaction ◽  
Level Set Function ◽  
The Matrix ◽  
Set Function

The application of a discrete mooring model for floating structures is presented in this paper. The method predicts the steady-state solution for the shape of an elastic cable and the tension forces under consideration of static loads. It is based on a discretization of the cable in mass points connected with straight but elastic bars. The successive approximation is applied to the resulting system of equations which leads to a significant reduction of the matrix size in comparison to the matrix of a Newton-Raphson method. The mooring model is implemented in the open-source CFD model REEF3D. The solver has been used to study various problems in the field of wave hydrodynamics and fluid-structure interaction. It includes floating structures through a level set function and captures its motion using Newton and Euler equations in 6DOF. The fluid-structure interaction is solved explicitly using an immersed boundary method based on the ghost cell method. The applications show the accuracy of the solver and effects of mooring on the motion of floating structures.

Numerical Analysis for Thermal Wave NDT of Debonding Defect of Coating on the Matrix of the Composite Materials

2012 ◽  
Vol 174-177 ◽  
pp. 1357-1362
Author(s):  
Wei Zhang ◽  
Qi Ke Li ◽  
Zheng Wei Yang
Keyword(s):  
Heat Transfer ◽  
Composite Materials ◽  
Thermal Wave ◽  
Simulation Method ◽  
Heat Transfer Process ◽  
Defect Size ◽  
Wave Detection ◽  
Factors Influencing ◽  
The Matrix ◽  
Simulation Results

For the disadvantages of the traditional NDT methods for coating defects, Thermal Wave Nondestructive Testing (TWNDT) technology was used to detect coating debonding defect on composite Materials matrix. Numerical simulation method was used to simulate the heat transfer process of thermal wave in the coating material with defects. The factors influencing the testing sensitivity were in-depth studied, including the debonding defect size and coating thickness. Simulation results show that: TWNDT technology can quickly and efficiently identify the size of the coating debonding defects, while the thickness of the coating is an important factor influencing the inspection sensitivity for the thermal wave detection.

scholarly journals Effect of Matrix Size in Affecting Noise Reduction Level of Filtering

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Solly Aryza ◽  
Muhammad Dharma Tuah Putra Nasution ◽  
Darmawan Napitupulu ◽  
Darmawan Napitupulu ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Noise Reduction ◽  
Nonlinear Filtering ◽  
Median Filter ◽  
Median Filtering ◽  
Matrix Size ◽  
Matrix Block ◽  
The Matrix ◽  
Pixel Value

Improved image quality needs to be done to improve data processing on the image. This quality improvement can be made by doing masking technique. Median Filter is one technique to enhance the quality of an image in a particular space. Median filtering improves the image by specifying a specific pixel from its neighboring pixels. The median filtering calculation uses a matrix block with an odd number. Each matrix block will have a middle value after the pixel values have regularly been sorted. This method is included in the category of nonlinear filtering. With the median filtering, the output pixel value is determined by the median of the specified mask environment. Median Filter has different results when using different matrix sizes as well. The results of this process can determine how gentle the result of noise reduction. In general, the larger the size of the matrix, the higher the blurriness of an image.

Sign in / Sign up

Export Citation Format

Share Document