scholarly journals Parallel Computing Sparse Wavelet Feature Extraction for P300 Speller BCI

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Zhihua Huang ◽  
Minghong Li ◽  
Yuanye Ma
Keyword(s):  
Feature Extraction ◽  
Parallel Computing ◽  
Information Transfer ◽  
Small Scale ◽  
Online Experiments ◽  
P300 Speller ◽  
Information Transfer Rate ◽  
Desktop Computers ◽  
The Difference ◽  
Computing Scheme

This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet. In this space, Fisher Criterion is used to measure the difference between target and nontarget ones. Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors. To ensure the online experiments, the computation tasks are distributed to several computers that are managed and integrated by Storm so that they could be parallelly carried out. The proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters. Our method achieved higher accuracies of classification and detection. The ITRs also reflected the superiority of our method. The parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers. The average feedback time for one round of EEG epochs was 1.57 ms. The proposed method can improve the performance of P300 Speller BCI. Its parallel computing scheme is able to support fast feedback required by online experiments. The number of repeated stimuli can be significantly reduced by our method. The parallel computing scheme not only supports our wavelet feature extraction but also provides a framework for other algorithms developed for P300 Speller.

scholarly journals Creation of a Hardware-Software Dynamic Signal Simulator of the Upgraded Inter-Satellite Radio Link of the GLONASS System

2021 ◽  
Vol 8 (3) ◽  
pp. 63-71
Author(s):  
A. A. Cherkasova ◽  
◽  
R. F. Salakhov ◽  
D. A. Astaсhov ◽  
◽  
...  
Keyword(s):  
Information Transfer ◽  
Doppler Frequency ◽  
Radio Link ◽  
Satellite Measurements ◽  
Process Measurement ◽  
Signal Simulator ◽  
Information Transfer Rate ◽  
The Difference ◽  
Measuring Signal ◽  
Orthogonal Phase

This work is aimed at creating a hardware-software signal simulator of the upgraded inter-satellite radio link (ISRL) of the GLONASS system. The simulator shapes ISRL signals with dynamically changing parameters of the Doppler frequency shift and delay, which correspond to the mutual dynamics of spacecraft (SC) motion of the GLONASS system. The upgraded inter-satellite radio link will provide (as compared to the current ISRL) an increase in the information transfer rate of up to four times, as well as boost the accuracy of measuring the distance between satellites by two times. Modernization consists in complementing the radio signal of the second orthogonal (phase-shifted carrier frequency by 90 degrees relative to the existing one) component. To modernize the ISRL, it is necessary to create and verify new equipment for receiving and transmitting signals of the upgraded ISRL of the GLONASS system. The simulator is designed to process measurement algorithms embedded in the on-board equipment for inter-satellite measurements and assess their consistency. Consistency evaluation consists in measuring and analyzing the difference between the Doppler parameters and delay introduced into the signal and the estimation of these parameters in the receiving equipment of the ISRL. This difference will be the measurement error. Dynamic simulation is performed for 24 system points, corresponding to GLONASS satellites, on the half-period of satellite revolution (20 280 seconds). The signal is generated at the input of the antenna-feeder device of one of the satellites in accordance with the information for generating the measuring signal, parameters of the transmitters of the signals of the upgraded ISRL and the almanac of the satellite constellation (because the signal at the input of the antenna-feeder device of the navigation receiver incomes from several SC) specified by the user.

BRAIN SIGNATURES PERSPECTIVE FOR HIGH-SECURITY AUTHENTICATION

2020 ◽  
Vol 32 (04) ◽  
pp. 2050025
Author(s):  
Nikhil Rathi ◽  
Rajesh Singla ◽  
Sheela Tiwari
Keyword(s):  
Classification Accuracy ◽  
Information Transfer ◽  
Rejection Rate ◽  
Internet Banking ◽  
P300 Speller ◽  
Security Authentication ◽  
Information Transfer Rate ◽  
Shoulder Surfing ◽  
Authentication System ◽  
High Level

In the recent past, the web (internet) has emerged as the most interactive authentication system for all of us (i.e. Internet banking passwords, system or building access, and e-payment platforms, etc.) and as a result, traditional authentication systems (like passwords or token-based) are never again more secure i.e. they are vulnerable to attacks. As a result, the security of individual information and safe access to a system winds up prime necessities. Therefore, the EEG-based authentication system has recently become a reasonable key for high-level security. This study centers upon P300 evoked potential-based authentication system designing. In this paper, a new visual stimulus paradigm (i.e. [Formula: see text] P300 speller) using pictures of different objects as stimuli for a person authentication system is designed instead of the conventional character-based paradigm (i.e. [Formula: see text] speller) for increasing the classification accuracy and Information Transfer Rate (ITR). The trial begins by exhibiting a collection of pictures of various objects on four corners of the PC screen comprising of random object pictures (non-target) alongside password pictures (target) that trigger P300 reactions. The P300 reaction’s rightness then checks the identity of the subject concerning the focused pictures (Target). The proposed investigation model achieves higher classification accuracy of 96.78%, along with 0.03075 False Rejection Rate (FRR), 0.03297 False Acceptation Rate (FAR), and ITR of [Formula: see text]. This study has shown that P300-based authentication system has an advantage over conventional methods (Password, Token, etc.) as EEG-based systems cannot be mimicked or forged (like Shoulder surfing in case of password) and can still be used for disabled users with a brain in good running condition. The classification results revealed that the performance of the QDA classifier outperformed other classifiers based on accuracy and ITR.

scholarly journals A Spelling Paradigm With an Added Red Dot Improved the P300 Speller System Performance

2020 ◽  
Vol 14 ◽  
Author(s):  
Yan Wu ◽  
Weiwei Zhou ◽  
Zhaohua Lu ◽  
Qi Li
Keyword(s):  
Classification Accuracy ◽  
Information Transfer ◽  
Event Related Potential ◽  
Virtual Character ◽  
P300 Speller ◽  
Information Transfer Rate ◽  
Control Paradigm ◽  
Character Matrix ◽  
Green Circle ◽  
Visuospatial Information

The traditional P300 speller system uses the flashing row or column spelling paradigm. However, the classification accuracy and information transfer rate of the P300 speller are not adequate for real-world application. To improve the performance of the P300 speller, we devised a new spelling paradigm in which the flashing row or column of a virtual character matrix is covered by a translucent green circle with a red dot in either the upper or lower half (GC-RD spelling paradigm). We compared the event-related potential (ERP) waveforms with a control paradigm (GC spelling paradigm), in which the flashing row or column of a virtual character matrix was covered by a translucent green circle only. Our experimental results showed that the amplitude of P3a at the parietal area and P3b at the frontal–central–parietal areas evoked by the GC-RD paradigm were significantly greater than those induced by the GC paradigm. Higher classification accuracy and information transmission rates were also obtained in the GC-RD system. Our results indicated that the added red dots increased attention and visuospatial information, resulting in an amplitude increase in both P3a and P3b, thereby improving the performance of the P300 speller system.

A Research Combining Spatial Filter with Autoregressive Model for EEG Feature Extraction

2010 ◽  
Vol 20-23 ◽  
pp. 605-611 ◽  
Author(s):  
Lin Liu ◽  
Qing Guo Wei
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Autoregressive Model ◽  
Information Transfer ◽  
Decisive Role ◽  
Spatial Filter ◽  
Fisher Discriminant Analysis ◽  
Left Hand ◽  
Fisher Discriminant ◽  
Information Transfer Rate

In a noninvasive brain-computer interface (BCI), EEG feature extraction is a key part for improving classification accuracy and resulting information transfer rate, and it has a crucial and decisive role. In this paper, three different methods were proposed that combine spatial filtering with autoregressive model for EEG feature extraction. Six subjects participated in the BCI experiment during which they were asked to imagine movements of left hand and right hand. Each subject carried out four sessions and each session contained 120 trials. EEG data recordings were used for off-line analysis and the 10 leads around C3 and C4 were chosen for feature extraction. Autoregressive model coefficients and the parameters derived from other three methods were proposed as classification features. Fisher discriminant analysis (FDA) was used as linear classifier. The results show that classification accuracy rates obtained from the three proposed methods are far higher than those acquired from autoregressive model coefficients. At the same time the classification results of each subject are very stable, proving the effectiveness of these novel feature methods.

scholarly journals Visual Simulation of Detailed Turbulent Water by Preserving the Thin Sheets of Fluid

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 502 ◽  
Author(s):  
Jong-Hyun Kim ◽  
Wook Kim ◽  
Young Kim ◽  
Jung Lee
Keyword(s):  
Turbulent Flows ◽  
Visual Simulation ◽  
Numerical Dissipation ◽  
Small Scale ◽  
Thin Sheets ◽  
Global Flow ◽  
Low Mass ◽  
The Difference ◽  
Water Simulation ◽  
Turbulent Water

When we perform particle-based water simulation, water particles are often increased dramatically because of particle splitting around breaking holes to maintain the thin fluid sheets. Because most of the existing approaches do not consider the volume of the water particles, the water particles must have a very low mass to satisfy the law of the conservation of mass. This phenomenon smears the motion of the water, which would otherwise result in splashing, thereby resulting in artifacts such as numerical dissipation. Thus, we propose a new fluid-implicit, particle-based framework for maintaining and representing the thin sheets and turbulent flows of water. After splitting the water particles, the proposed method uses the ghost density and ghost mass to redistribute the difference in mass based on the volume of the water particles. Next, small-scale turbulent flows are formed in local regions and transferred in a smooth manner to the global flow field. Our results show us the turbulence details as well as the thin sheets of water, thereby obtaining an aesthetically pleasing improvement compared with existing methods.

A Dual Stimuli Approach Combined with Convolutional Neural Network to Improve Information Transfer Rate of Event-Related Potential-Based Brain-Computer Interface

2018 ◽  
Vol 28 (10) ◽  
pp. 1850034 ◽  
Author(s):  
Wei Li ◽  
Mengfan Li ◽  
Huihui Zhou ◽  
Genshe Chen ◽  
Jing Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Rate ◽  
Information Transfer ◽  
Brain Computer Interface ◽  
Event Related Potential ◽  
Computer Interface ◽  
Generation Rate ◽  
Information Transfer Rate ◽  
Command Generation

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

scholarly journals Analysis of livestock breeders’ perceptions and their adaptation measures to climate change in Morocco‘s arid rangelands

2020 ◽  
Vol 6 (1) ◽  
pp. 1-25
Author(s):  
Wadii Snaibi
Keyword(s):  
Climate Change ◽  
Local Level ◽  
Small Scale ◽  
Climate Data ◽  
Chi Square ◽  
Adaptation Measures ◽  
Significant Difference ◽  
The Difference ◽  
Eastern Morocco ◽  
Climate Analysis

AbstractThe high plateaus of eastern Morocco are already suffering from the adverse impacts of climate change (CC), as the local populations’ livelihoods depend mainly on extensive sheep farming and therefore on natural resources. This research identifies breeders’ perceptions about CC, examines whether they correspond to the recorded climate data and analyses endogenous adaptation practices taking into account the agroecological characteristics of the studied sites and the difference between breeders’ categories based on the size of owned sheep herd. Data on perceptions and adaptation were analyzed using the Chi-square independence and Kruskal-Wallis tests. Climate data were investigated through Mann-Kendall, Pettitt and Buishand tests.Herders’ perceptions are in line with the climate analysis in term of nature and direction of observed climate variations (downward trend in rainfall and upward in temperature). In addition, there is a significant difference in the adoption frequency of adaptive strategies between the studied agroecological sub-zones (χ2 = 14.525, p <.05) due to their contrasting biophysical and socioeconomic conditions, as well as among breeders’ categories (χ2 = 10.568, p < .05) which attributed mainly to the size of sheep flock. Policy options aimed to enhance local-level adaptation should formulate site-specific adaptation programs and prioritise the small-scale herders.

Fluid Flow and Heat Transfer of Liquid Sodium in Small-Scale Heat Sinks With Different Geometries

2021 ◽  
Author(s):  
Mahyar Pourghasemi ◽  
Nima Fathi
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Cross Sections ◽  
Heat Sink ◽  
Volume Ratio ◽  
Heat Sinks ◽  
Liquid Sodium ◽  
Small Scale ◽  
Nusselt Numbers ◽  
The Difference

Abstract 3-D numerical simulations are performed to investigate liquid sodium (Na) flow and the heat transfer within miniature heat sinks with different geometries and hydraulic diameters of less than 5 mm. Two different straight small-scale heat sinks with rectangular and triangular cross-sections are studied in the laminar flow with the Reynolds number up to 1900. The local and average Nusselt numbers are obtained and compared against eachother. At the same surface area to volume ratio, rectangular minichannel heat sink leads to almost 280% higher convective heat transfer rate in comparison with triangular heat sink. It is observed that the difference between thermal efficiencies of rectangular and triangular minichannel heat sinks was independent of flow Reynolds number.

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