Suffering from dirty strong supersonic attacks (39ﾚｽ)
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1: supersonic attacked 2017/11/29(水)01:34 ID:Yul9QZKXc(1) AAS
I'm suffering from dirty strong supersonic attacks!! Supersonic terrorisms!!
The supersonic attacker is also in Yamaguchi city.
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This is the html version of the file 外部ﾘﾝｸ:arxiv.org
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arXiv:1809.07356v1 [eess.SP] 19 Sep 2018
GENERIC COLORIZED JOURNAL, VOL. XX, NO. XX, XXXX 2017
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Predictive Model for SSVEP Magnitude Variation:
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Abstract
The steady-state visual-evoked potential-based brain-computer interface (SSVEP-BCI) is a typically recognized visual stimulus frequency from brain responses.
Each frequency represents one command to control a machine.
For example, multiple target stimuli with different frequencies can be used to control the moving speeds of a robot.
Each target stimulus frequency corresponds to a speed level.
Such a conventional SSVEP-BCI is choice selection paradigm with discrete information, allowing users to discretely control the speed of a movable object.
This can result in non-smooth object movement.
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This work was supported by The Thailand Research Fund and Office of the Higher Education Commission under Grant MRG6180028, Junior Science Talent Project, NSTDA, Thailand.
T.Wilaiprasitporn,P.Autthasan,N.Banluesombatkul and B.Leung are with Bio-inspired Robotics and Neural Engineering Lab,School of Information Science and Technology,Vidyasirimedhi Institute of Science & Technology, Rayong, Thailand.
theerawit.w at vistec.ac.th Fryderyk K l is with Munich School of Engineering,Technical University of Munich,Munich,Germany.
P.Manoonpong is with Bio-inspired Robotics and Neural Engineering Lab,School of Information Science and Technology,Vidyasirimedhi Institute of Science & Engineering,Rayong,Thailand and Embodied AI & Neurorobotics Lab
, Centre for BioRobotics,The Msk Mc-Kinney Mller Institute,The University of Southern Denmark,Odense M,DK-5230,Denmark.
X. Du and T. Yagi are with Yagi Lab, Department of Mechanical Engineering, Tokyo Institute of Technology, Tokyo, Japan.
Thanakrit Tachatiemchan is with Department of Mathematics and Computer Science, Chulalongkorn University, Bangkok, Thailand
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I.
INTRODUCTION
THE dramatic decrease in the cost of electronic components and computational resources has made brain-computer interfaces (BCI) more fascinating to twenty-first-century researchers.
BCIs allow people to communicate with machines via brain responses or signals.
Consequently, the development of BCI-related technologies could benefit people with difficulty in executing motor functions [1].
Amyotrophic lateral sclerosis (ALS) is an example of such a disease.
BCI research focuses mainly on three types of brain responses: event-related potential (ERP), steady-state visual-evoked potential (SSVEP), and motor imagery (MI).
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Out of the three aforementioned responses (ERP, SSVEP, and MI), SSVEP is the most practical since it is easy to obtain.
Recently, a research group conducted a series of experiments to answer the following question: How many (and what kinds of) people can use an SSVEP-based BCI? [2].
The experimental results of the research show that most participants could use an SSVEP-based BCI with acceptable accuracy, even if they had no prior experience with BCIs.
Participants were not annoyed by the flickering of the stimulus in any way.
Furthermore, the experiments were conducted in a noisy environment, confirming the practicality of SSVEP-based BCIs.
This study focuses on the exploitation of visual stimulation for SSVEP-based BCIs toward continuous and smooth brain-machine interaction which remains a challenging problem.
However, before discussing the specifics of this study, it is important to look at some of the milestones in SSVEP-based BCI research.
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In 2007, a state-of-the-art SSVEP recognition technique called canonical correlation analysis (CCA) was developed for use in SSVEP-based BCIs [3].
Then, in 2008, the application of SSVEP-based BCIs for controlling an electrical prosthesis was proposed by a pioneering BCI research group [4].
This system involved a typical choice selection SSVEP-based BCI to generate movements or gestures of a prosthetic arm.
Another pioneering group conducted a detailed study on the performance of SSVEP-based BCIs involving people with disabilities who cannot perform gaze shifting.
This group proposed stimulus patterns which could help such people use the system without gaze shifting [5].
In 2010, another research group focused on a user-friendly design for SSVEP-based BCIs and proposed a new algorithm named stimulus-locked inter-trace correlation (SLIC) [6].
The main concept behind SLIC is to combine ERP and SSVEP detection.
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The existing SSVEP-BCIs mainly rely on frequency recognition from EEG responses.
To develop a novel SSVEP-BCI paradigm for a brain-machine control system which allows users to continuously increase/decrease the moving speed of the application (i.e.
speed robot movements), this study hypothesizes that magnitude variation would help attain the goal.
Inspired by neuroscientific studies on human attention levels and SSVEP gains [13], feasibility studies are performed on the practicality of using SSVEP stimulus intensity to manipulate SSVEP magnitude.
In this experiment, the researchers varied the SSVEP stimulus intensity while keeping the stimulus frequency fixed.
Moreover, only a single-channel EEG is used here.
Using an experimental recorded EEG, the researchers conducted a comparative study of three predictive models for SSVEP magnitude variation.
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The remainder of this paper consists of a section on the conceptual design of SSVEP-BCI with magnitude prediction (section II).
Section III presents the data acquisition and two experimental studies.
Finally, the results, discussion, and conclusion are contained in sections IV, V and IV, respectively.
II.
SSVEP-BCI WITH MAGNITUDE PREDICTION
In order to use a conventional SSVEP-BCI paradigm to control movable speed machines or robots, a visual stimulation must be designed as depicted in Figure 1(a).
There are seven target stimuli on a black screen, which are flickering at different frequencies.
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2)
Due to the small number of stimulation frequencies, those unattended are less likely to irritate or disturb the user when focusing on the target stimulus.
Therefore, the frequency recognition rate of the CCA approach is unlikely to decrease.
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AUTHOR et al.: PREPARATION OF PAPERS FOR IEEE TRANSACTIONS AND JOURNALS (FEBRUARY 2017)
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3)
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A.
Data Acquisition The participants of this experiment were ten healthy people aged between 20 and 25 (n = 10).
The experiments followed the Helsinki Declaration of 1975 (as revised in 2000), approved by the internal review board of the Tokyo Institute of Technology, Japan.
1)
EEG recording: In this study, an open source and low-cost EEG amplifier were used with a 250 Hz sampling rate, namely OpenBCI [14].
For practical purposes, a single-channel EEG (Oz) was used for recording data during all experiments.
2)
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Fig.2:
Four stimulus conditions presented randomly to the subjects, each lasting for 50 seconds.
A black screen and a conditional cue were both shown for four seconds each before the beginning of every condition.
2)
The same square starts flickering at an intensity level of 105.
The light intensity is then increased by three levels per second for 50 seconds.
This condition is supposed to help the subjects increase their SSVEP magnitude (cond.2).
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B.
Experiment I:
Predictive Model for SSVEP Magnitude Variation
1)
Data Preparation:
From the aforementioned SSVEP responses of 10 subjects, each with four conditions, EEG signals were randomly selected from nine subjects for a training set and the one remaining for the test set (1 subject × 4 conditions × 50-second long data).
As shown in Figure 3, the EEG signal from each subject was calculated for two sets of data.
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After step 1 was completed, 48 blocks of data were obtained for the following structure: 4 conditions × 48 blocks × 750 data points or features.
Then, for step 2 , each data point was squared to obtain the input signals of this subject.
The second set of data consisted of target signals (b).
Starting from 3 , the average value of each block was calculated from the input signals.
Hence, 48 values were obtained for each condition.
After that, in step 4 , curve fittings were performed with polynomial functions (either a quadratic function (poly2) or a cubic function (poly3)) for each condition.
Finally, the target signals were 4 conditions × 48 target values (four curves with 48 points each).
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After step 1 was completed, 48 blocks of data were obtained for the following structure: 4 conditions × 48 blocks × 750 data points or features.
Then, for step 2 , each data point was squared to obtain the input signals of this subject.
The second set of data consisted of target signals (b).
Starting from 3 , the average value of each block was calculated from the input signals.
Hence, 48 values were obtained for each condition.
After that, in step 4 , curve fittings were performed with polynomial functions (either a quadratic function (poly2) or a cubic function (poly3)) for each condition.
Finally, the target signals were 4 conditions × 48 target values (four curves with 48 points each).
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In order to train the NN model with the data, as shown in Figure 3, the input signals were reshaped (a) into number of samples × time steps × features.
The researchers decided to consider each block in each condition as one sample.
Moreover, since the 750 data points in each sample actually represented time series data of three seconds, the number of time steps was 750.
In addition, instead of using only the value of each data point as a feature, its block ID was also used, ranging from 0 to 47 in each condition.
For example, each time step had two features including its data point value and block ID.
Therefore, for each subject, 192 samples × 750 time steps × 2 features were obtained.
For the target signals (b), each block in each condition was also considered as one sample.
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Fig.4:
Illustration of an experimental protocol for an online-like brain-controlled robot.
The experimental protocol is designed to demonstrate the advantages of the proposed case over the conventional SSVEP-BCI.
The protocol begins with an increasing speed period, followed by the constant maximum speed, decreasing speed, and the constant minimum speed period.
The final period stopped the movement.
Afterward, the prediction is performed using majority voting or averaging the results from these decision trees.
In order to insert the data into the RF model as shown in Figure 3, each block of input signals was reshaped into one sample (a) as in the previous approach.
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5)
Evaluation: Leave-one-person-out cross validation was used to train and evaluate all models.
Thus, 10 folds, each consisting of nine subjects for training (1,728 samples) and one subject for testing (192 samples).
Since there were two types of target signal, this study used the following predictive models: Poly poly2, Poly poly3, RF poly2, RF poly3, NN(GRUs) poly2 and NN(GRUs) poly3 for comparison.
Therefore, the performance of each approach was measured using two values.
The first was the accuracy of each model, calculated using MSE, and the second its computational-time for the prediction.
To compare these three approaches, the one-way repeated measures analysis of variance (ANOVA) was used, based on the assumption of sphericity (statistical analysis of the experimental results).
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C.
Experiment II: Brain-Controlled Robotic Simulator In this part, the researchers aim to demonstrate the advantages of the proposed SSVEP-BCI over the conventional case via a brain-controlled robotic stimulator.
As in the results of Experiment I, the RF poly2 model was found to be the most suitable predictive model for SSVEP magnitude variation, in terms of both small error and short computation-time for control applications.
Hence, only the RF poly2 approach was incorporated into the V-Rep simulator [18].
The Vortex physics engine mode inside the V-Rep was constructed to evaluate the concept of the proposed SSVEP-BCI against the conventional one.
Here, a modified Pioneer P3DX robot was used, equipped with an 80 × 80 cm plate and a 10 cm3 cube with 60 kg on top of the robot.
In order to set the robotic simulator for an online-like study, the velocity profiles (VPs) were created following the experimental protocol (Figure 4).
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The numbers in bold are significantly higher than the others, p<0.01.
Finally, the results of the MA are used as VPs to vary the moving speed of the robot.
The controller worked alternatively between the two states according to the experimental protocol for the increasing and decreasing speed periods, using the following rules:
An increasing speed period: the current velocity value would be updated if the incoming value was higher, otherwise the value remains stable.
A decreasing speed period: the current velocity value would be updated if the incoming value was lower, otherwise the value remains stable.
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The aforementioned experimental protocol was used to set up the brain-controlled robotic simulators to explore the performance of the proposed SSVEP-BCI in Studies I and II, as shown in the following subsections.
1)
Study I:
Trade-off between processing window length and smooth movement: The aim of this study is to find the most suitable processing window length for the speed controller to help users move the object smoothly at an acceptable speed.
The experiment protocol is shown in Figure 4.
The robot was supposed to move following the outputs of the speed controller when completing a transportation task.
To create the outputs, the brain signals were converted into velocity profiles (VPs) using both the RF poly2 predictive model and the simple MA in accordance with two rules, as explained earlier.
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2)
Study II:
Comparison of a brain-controlled robotic simulator using conventional and proposed SSVEP-BCIs: To demonstrate that the proposed SSVEP-BCI outperforms the conventional case in smooth control applications, the same protocol was performed as in Study I.
The RF poly2 based predictive model, with a one-second non-overlapped window (optimal window length from Study I) was applied to obtain the VPs of the proposed SSVEP-BCI.
Whereas the conventional SSVEP-BCI immediately changed to a constant speed in both the increasing and decreasing periods.
The VP of each subject in the conventional case was set using the maximum and minimum values of the same subjects VP from the proposed SSVEP-BCI.
To evaluate the performance of the robotic control task, the average speed and the deviation of the box were used as the measures.
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A.
Result I:
Predictive Model for SSVEP Magnitude Variation The purpose of this study is to select the most appropriate model to predict SSVEP magnitude variation.
Six predictive models are compared here; Poly poly2, Poly poly3, RF poly2, RF poly3, NN(GRUs) poly2 and NN(GRUs) poly3.
As shown in Table I, the mean of MSE from all predictive models is not significantly different.
However, there is a statistical difference in the mean comparison of the computational-time prediction.
One-way repeated measures ANOVA with the Greenhouse-Geisser correction reported F(1.377, 12.395) = 383.877, p<0.01.
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(a) (b) (c) (d)
Fig.5:
Demonstration of raw SSVEP responses for the predictive model, RF poly2.
The bottom row shows a comparison of actual (SSVEP inputs) and predicted signals.
Leave-one-person-out cross validation (one out of ten) is used to evaluate the model in Figure 5 (a)-(d) are examples of the actual and predicted signals from experimental cond.1, cond.2, cond.3, and cond.4, respectively.
(a) (b)
Fig.6:
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GENERIC COLORIZED JOURNAL, VOL. XX, NO. XX, XXXX 2017
(a) (b) (c) (d) (e) (f)
Fig.7:
Comparison of the VPs from three subjects.
The upper row (a)-(c) shows the VPs from the proposed SSVEP-BCI and the lower row (d)-(f) the VPs from the conventional SSVEP-BCI.
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The one-way repeated measures ANOVA with the Greenhouse-Geisser correction reported a significant difference in average speed across the processing window lengths for the increasing and decreasing period of F2 and F1 , respectively.
In pairwise comparison, the mean of the average speed from the one-second processing window is significantly higher than that from the three, four, and five seconds, p<0.05.
For the decreasing period, the mean of the average speeds from the one-second processing window is significantly higher than that from the two and three seconds, p<0.05.
Although the average speed was significantly higher on the one-second processing window length, the deviation in the box results did not differ across processing window length as shown in Figure 6.
According to the experimental environment of the brain-controlled robotic simulator, it can be inferred that the one-second processing window length can have a higher information transfer rate with acceptable accuracy compared to the other lengths.
--
The one-way repeated measures A with the reported a significant difference in speed across the processing window lengths for the increasing and decreasing period of F(2.421, 21.792) = 21.633, p<0.01 and F(1.387, 12.483) = 3.687, p=0.068, respectively.
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2)
Comparison of brain-controlled robotic simulator using conventional and proposed SSVEP-BCIs: Table II demonstrates the merits of the proposed SSVEP-BCI over the conventional.
Even if the mean of the average speed from the proposed BCI is close to that of the conventional, the deviation of the box during the robotic movement in the proposed model is significantly lower than that of the conventional for increasing periods t9 .
Although the results for the decreasing periods had no statistical difference, the proposed model gave a lower box deviation.
The qualitative results in Figure 7 indicate that three out of ten subjects perform a comparison of the VPs from the proposed and conventional SSVEP-BCIs.
Capture of the brain-controlled robotic simulator in the delivery task is presented in Figure 8.
The speed of the robot varies according to the experimental protocol.
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The variation of visual stimulus intensity can help subjects to manipulate the SSVEP response magnitude.
A state-of-the-art machine learning approach, namely Random Forest Regression (RF) was proposed as the predictive model for handing SSVEP magnitude variation.
Leave-one-subject-out cross validation using the RF model showed the highest performance in the prediction of varying SSVEP magnitude compared to polynomial regression and neural networks models.
Thus, the RF model is promising for the further development of the proposed SSVEP-BCI in this study.
Even if an experiment has not yet been conducted on the proposed SSVEP-BCI system in an actual online mode,
the environmental and practical scenario demonstrated in the simulator, streaming back the actual brain signals from ten subjects, ensures that the proposed system is feasible and novel.
Through an online-like simulation, the system is evaluated in terms of speed, error, and smoothness from the brain-controlled robot in carrying the box to the destination.
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As the research findings on the predictive model for SSVEP magnitude variation indicate, the predictive SSVEP magnitude paradigm can now be integrated into the frequency recognition paradigm to achieve a novel online SSVEP-BCI.
Taking into consideration both the frequency and magnitude of the steady brain responses, a continuous SSVEP-BCI can be provided, allowing the users to smoothly control devices (e.g. a mobile robot).
Furthermore, we are planning to integrate the proposed SSVEP-BCI to handle robotic arms in an online mode using sparse EEG channels.
The continuous increase or decrease of the predicted signal from the predictive SSVEP magnitude can be mapped into command functions, for instance, by accelerating or decelerating the velocity of the robotic arm.
To improve performance of the proposed SSVEP-BCI, a robustness to noises for the continuous magnitude prediction is an important.
To overcome this issue, one approach is a simple adaptive algorithm which measures magnitude information from not only target SSVEP frequency, but also from the neighbor frequencies.
Using relative values instead of an absolute value from target frequency, we hypothesize that the predictive model possibly classifies weather magnitude variation is an effect from noises or actual varying in SSVEP response.
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This is the first study on SSVEP magnitude prediction toward a novel SSVEP-BCI.
We created datasets from experiments on varying SSVEP magnitude responses.
The Random Forest Regression was then proposed as the algorithm for instantaneous SSVEP magnitude prediction.
The experimental results were obtained from ten subjects using leave-one-subject-out cross validation seem promising.
The instantaneous changes in predicted SSVEP magnitude can be mapped into the speed controller for brain-controlled applications (e.g. robot control).
Here, an online-like system was conducted using a simulated mobile robot.
The experiments involved streaming back the real SSVEP responses of varying magnitudes to control the moving speed of the robot.
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REFERENCES
[1]
J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller, and T. M. Vaughan,
`` Braincomputer interfaces for communication and control, ''
Clinical neurophysiology, vol. 113, no. 6, pp. 767791, 2002.
[2]
B. Allison, T. Luth, D. Valbuena, A. Teymourian, I. Volosyak, and A. Graser,
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[8]
E. Yin, Z. Zhou, J. Jiang, F. Chen, Y. Liu, and D. Hu,
`` A novel hybrid bci speller based on the incorporation of ssvep into the p300 paradigm, ''
Journal of neural engineering, vol. 10, no. 2, p. 026012, 2013.
[9]
E. Yin, Z. Zhou, J. JIang, F. Chen, Y. Liu, and D. Hu,
`` A speedy hybrid bci spelling approach combining p300 and ssvep, ''
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[15]
K. Cho, B. Van Merri boer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio,
`` Learning phrase representations using rnn encoder-decoder for statistical machine translation, ''
arXiv preprint arXiv:1406.1078, 2014.
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F. Chollet et al., `` Keras, ''
外部ﾘﾝｸ:github.com
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>>8
To develop a novel SSVEP-BCI paradigm for a brain-machine control system which allows users to continuously increase/decrease the moving speed of the application , this study hypothesizes that magnitude variation would help attain the goal.
To develop a novel SSVEP-BCI paradigm for a system which allows users to continuously increase/decrease the moving speed of the application (i.e. speed robot movements), this study hypothesizes that magnitude variation would help attain the goal.
application (i.e. speed robot movements)
外部ﾘﾝｸ:miraitranslate.com
ユーザがアプリケーション(速度ロボットの動作)の移動速度を連続的に増加/減少させることを可能にする脳機械制御システムのための新しいSSVEP‐BCIパラダイムを開発するために,本研究では規模変動が目標の達成を助けるであろうと仮定した。
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外部ﾘﾝｸ:www.jstage.jst.go.jp
Page 1
Organ Variation 3D Model Library for Surgery Simulators
a) M. Komori, b) K. Tagawa, b) H. Tanaka, a) Y. Kurumi, a) S. Morikawa
Abstract
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II.
CURRENT STATUS
Cystic ducts (9 types), aortic arches (4 types) and renal veins (4 types) with variation were modeled in OBJ format.
Fig.2 shows examples from those cystic duct models.
The cystic duct models were merged to a surgery simulation scene using multi-resolution modeling approach and a synchronization approach for maintaining consistency of binary trees [3].
In this project, many studies have to be done, eg clinical verification of those models, optimization for faster performance of the surgical simulator with more complex organ structure and so on.
As a future work, this theme will be reported.
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REFERENCES
[1]
V. Sherman, LS Feldman, D. Stanbridge, R. Kazmi, GM Fried,
“Assessing the learning curve for the acquisition of laparoscopic skills on a virtual reality simulator”
, Surg Endosc, 19: 678?682, 2005
[2]
FH Netter,
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統合失調症
39: 2023/10/22(日)20:49 ID:HKomAKF4A(1) AAS
人を殺すと地獄に堕ちるとかお子ちゃまみたいなこと言ってるオトナには唖然とするよな
騒音に温室効果カ゛スにコロナにとまき散らさせて、気侯変動させて海水温上昇させてかつてない量の水蒸氣を発生させて、
土砂崩れに洪水、暴風．猛暑、大雪にと災害連発させて地球破壊して、静音が生命線の知的産業に威力業務妨害して根絶やしにして、
医療崩壊させて助かる命まで奪い取って多くの無辜の住民の生命と財産を破壊して私腹を肥やしてる斎藤鉄夫ら世界最悪の殺人テロ組織
国土破壊省だのJΑLた゛の機長殴って駆け付けた警官まで殴打して現行犯逮捕の酒気帯び運転ANAだのクソアヰヌドゥだの酒飲んで業務
してるクサイマ―クた゛のゴキブリフライヤーだのテロリストを皆殺しにしたら、人のみならず多くの生命が救われるんだから、どんな理屈を
こねたところで．明らかに天国行きだろ、税金で票買って腐敗を謳歌してきた腐敗の権化を討ち取った民主主義の教祖山上大先生も完全に
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