P300 Wave Detection Based on Rough Sets

2006 ◽  
pp. 207-223 ◽  
Author(s):  
Sheela Ramanna ◽  
Reza Fazel-Rezai
Keyword(s):  
Rough Sets ◽  
P300 Wave ◽  
Wave Detection

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.

BUSINESS FAILURE PREDICTION USING ROUGH SETS: A COMPARISON WITH MULTIVARIATE ANALYSIS TECHNIQUES

1999 ◽  
Vol 04 (01) ◽  
Author(s):  
C. Zopounidis ◽  
M. Doumpos ◽  
R. Slowinski ◽  
R. Susmaga ◽  
A. I. Dimitras
Keyword(s):  
Multivariate Analysis ◽  
Rough Sets ◽  
Failure Prediction ◽  
Business Failure ◽  
Analysis Techniques

Ultrasonic Wave Detection in Atmospheric Pressure Plasma Using Fraunhofer Diffraction Effect

PIERS Online ◽  
2010 ◽  
Vol 6 (7) ◽  
pp. 636-639
Author(s):  
Toshiyuki Nakamiya ◽  
Fumiaki Mitsugi ◽  
Shota Suyama ◽  
Tomoaki Ikegami ◽  
Yoshito Sonoda ◽  
...  
Keyword(s):  
Ultrasonic Wave ◽  
Atmospheric Pressure ◽  
Atmospheric Pressure Plasma ◽  
Diffraction Effect ◽  
Fraunhofer Diffraction ◽  
Wave Detection ◽  
Pressure Plasma

Uncooled bolometer for millimeter-wave detection using manganese-cobalt-nickel-oxide thin film

2020 ◽  
Vol 13 (12) ◽  
pp. 124002
Author(s):  
Tao Hu ◽  
Wanli Ma ◽  
Jing Wu ◽  
Zhibo Zhang ◽  
Wei Zhou ◽  
...  
Keyword(s):  
Thin Film ◽  
Nickel Oxide ◽  
Millimeter Wave ◽  
Oxide Thin Film ◽  
Wave Detection ◽  
Cobalt Nickel ◽  
Cobalt Nickel Oxide ◽  
Nickel Oxide Thin Film

Approximation Sets of Rough Sets

2012 ◽  
Vol 23 (7) ◽  
pp. 1745-1759 ◽  
Author(s):  
Qing-Hua ZHANG ◽  
Guo-Yin WANG ◽  
Yu XIAO
Keyword(s):  
Rough Sets

Analysing diabetes using rough sets

2020 ◽  
pp. 533-538
Author(s):  
S. Arjun Raj ◽  
M. Vigneshwaran
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
International Diabetes Federation ◽  
Published Data ◽  
Lower And Upper Approximations

In this article we use the rough set theory to generate the set of decision concepts in order to solve a medical problem.Based on officially published data by International Diabetes Federation (IDF), rough sets have been used to diagnose Diabetes.The lower and upper approximations of decision concepts and their boundary regions have been formulated here.

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