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6,059 results for “Journale”
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 2. Timing of one trial of the experiment with continuous feedback (Guger et al, 2001)
<p>At the beginning of each trial (t = 0 s), a fixation cross appeared on the black screen. After two seconds a warning stimulus was given in the form of a beep. From 3 to 4.25s, an arrow (cue stimulus), pointing to the left or right, was shown on the screen. The subject was instructed to imagine a left or right hand movement until the end of the trial, depending on the direction of the arrow. The EEG was sampled and classified on line throughout the session. Between 4.25 and 8s, the classification result was used to give a continuously updated feedback stimulus in the form of a horizontal bar that appeared in the center of the screen. The paradigm is illustrated in fig (2).</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 1. Being Brain and Chess Game Strategy - similarities
<p>Finally, the following similar reactions between a chess player and a human being must be mentioned and considered. The power of reason for every being, human brain, or chess game player lies in similarities and has three main directions (see Figure 1)</p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 4. Scheme for the polynomial with [16, 14, 13, 11] tap sequence
<p>A simulation program for the functioning on LFSR of the 16th degree for the Galois implementation was developed. In the following example an analysis for the 14 selected primitive polynomials will be presented. A list with the positions which will influence the future state is called tap sequence.</p> <p>This sequence can be represented by a polynomial mod 2, only with coefficients 1 and 0, called Feedback Polynomial or Characteristic Polynomial. For the above scheme this polynomial is: P(X)= X^16+X^14+X^13+X^11+1</p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 3. Galois implementation
<p>In Galois implementation there is a Shift Register, whose content is modified each step at a binary value sent to the output. In Galois configuration the single shifted out bit is XOR ed with several bits in the shift register and in conventional configuration each new bit input to the shift register is the XOR of several bits in the register.</p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 2. Fibonacci implementation
<p>A LFSR can be represented as a polynomial of variable x referred to as the generator polynomial or the characteristic polynomial. The input bit is given from a linear function of the initial status for a special shift register called Linear Feedback Shift Register (LFSR). The initial value of the register is called seed and the produced sequence is completely determined by the initial status. Because the register has a finite number of possible statuses, after a period the sequence will be repeated. If the feedback function is very well chosen, the produced sequence will be random and the cycle will be very long, called by Golomb (1967) maximum lengths shift register sequences. Goresky and Klapper (2004) show two possibilities to implement a LFSR: • Fibonacci Form • Galois. </p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 1. Basis scheme for a Feedback Shift Register
<p>Every LFSR works by taking the XOR of the selected bits in its internal state and any LFSR containing all zero bits will never move to any other state, so one possible state must be excluded from any cycle. A LFSR is composed of memory cells connected together as a shift register with linear feedback. In digital circuits a shift register is formed by flip-flops and EXOR gates chained together with a synchronous clock. Shift registers are a form of sequential logic like counters. Always the shift registers produce a discrete delay of a digital signal or waveform. Considering that a shift register has n stages, the waveform is delayed by n discrete clock times. Usually the naming of the shift register follows a type of convention shown normally in digital logic, with the least significant bit on the left. According to the communication protocol, the signals will be addressed, not the registers. There are n+1 signals for each n-bit register. Always the next state of an LFSR is uniquely determined from the previous one by the feedback network. Any LFSR will generate a sequence of different states starting with the initial one, called seed. A feedback shift register is composed of: - a shift register - a feedback function. </p>
BRAIN Journal-A Repeated Signal Difference for Recognising Patterns-Figure 3. Binary input stimulus dataset
<p>If the input value is 1, then the neuron is likely to be part of the cohesive set and would update the weight and also the global and local counts each event. If the input value is 0, then the neuron is not likely to be part of the cohesive set. For this case, it would not update the weight value as it is 0. It does update the global count as that uses unit increments, but only updates the local count when an earlier trigger switch tells it to. Each update event is also counted, so that averaged totals can be produced. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 2. Result of the implementation of EVM method.
<p>In our research, we focus on the parameter values in the second case. Then we convert the obtained video to the images sequence. The number of images is decreased after the implementation of the EVM method. The reason of this problem is the integration of multiple pixels in the images. The resulting output is a generic extract of all motions created in the face.</p> <p>In this study, two experiments are performed as follows: Phase1: investigating the emotional/unemotional detection on face in micro expressions. Phase2: investigating facial micro expressions recognition </p>
BRAIN Journal-A Repeated Signal Difference for Recognising Patterns-Figure 2. Schematic of the same neuron N1, shared between 2 different patterns P1 and P2.
<p>It can be assumed that only one pattern fires during each time unit, where Rule 1 of the Introduction states that if a neuron fires a stronger signal for a pattern, it sends feedback to ask it to fire again, the next time unit. If it fires a weaker signal, the request is not to fire next time. Stronger and weaker relates to a more variable type of on or off. As the mechanism is not final yet, there could be some flexibility with it. Rule 1 also states that if the neuron does not fire for the current pattern, it receives the switch command from the last pattern it did fire for. Note that the actual firing event can produce either zero or some input and so an earlier event can also force a weak feedback signal. Rule 2 states that this particular feedback is only the on/off switch command and does not affect the size of the output value. A schematic of the idea is illustrated in Figure 2, which relates to the same neuron shared between 2 pattern events, at times t1 and t2. The schematic is described further in section 0. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 5.The chart of run time in emotional/unemotional detection in two databases.
<p>The processing time of our proposed method is of 0.420468 fps and in regular data is of 5.27 fps. As the results show, the EVM method has good effect in processing. Of course, the creation time of the magnification image sequence was not considered in the result. In other words, the processing time on a magnified images sequence is less than the non-magnification state. Figure 5 shows the running time in emotional/unemotional detection. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure7. Model 3D of women body.
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 7 .</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 3. (3.a) – The flowchart of Graph cuts method; (3.b)- the result of Graph cuts image segmentation.
<p>Figure 3 describes the steps implemented Graph cuts algorithm for the segmentation of human body parts. The results obtained are 5 main sections that include the hands, the legs, the center of the body (chest, waist, hips), and the head. The result of the display image is taken from the human image database, which was collected by us (Нгуен, 2016). </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 4. The chart of average percent of emotional/unemotional detection in SMIC
<p>In Figure 4 the blue color curve represents the average percent of emotional/unemotional detection using the EVM method. The red color curve also represents the average percent of emotional/unemotional detection without this method. Table 1 shows the running time and speed in this experiment. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 2. Human body sizes for men/women.
<p>We propose an efficient, simple and robust human body feature extraction based on the front and side images of a human body. Description of anthropometric data - men/women: Dataset based on an experiment is used to test the system data describing the anthropometric features of men, includes 12 sizes of the human body, which are presented in figure 2. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 1. Overall structure of micro expression recognition
<p>In this study we investigate the emotional/unemotional detection on the face and micro expression recognition. As expressed in Murthy & Jadon (2007) and Frank & Nöth (2003) the eigenface method is not able to perform micro expression recognition. So, we use the EVM method for micro expression recognition based on the eigenface method. Using this magnification method, the subtle motions are retrieved from the face during micro expression and those are not visible. In order to do this work, we need a dataset including the spontaneous subtle facial motions. Therefore, in this study we use two databases, SMIC and CASME. According to the obvious features in the EVM method for retrieving and displaying subtle motions, first we test these databases using this method. Then we evaluate the rate of emotional/unemotional detection in the face and micro expression recognition using the eigenface method. An overall structure of our research is shown in Figure 1. </p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 3.The chart of emotional/unemotional detection on the face in negative, positive and surprise states (Regular and magnified data)
<p>To evaluate the emotional/unemotional detection on the face, 328 tests were performed: 164 tests on the magnified data and 164 tests on the regular data. For this purpose, the train set includes the neutral state and only one of the emotional states (negativism, positivism and surprise) according to the test set. So that the train set includes regular data in 328 experiments. The experimental results are shown in Figure 3. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 1. Flowchart of anthropometric system
<p> Our purpose is to develop an automatic measurement and modeling system based on 2D images (front and side images). This system used to image processing methods and machine learning algorithms. Our system has 3 main parts; there are human body feature extraction, training and testing processes, and the classification for new data. The novelty of our approach: - Classification of anthropometric features based on machine learning algorithms. - Development a non-contact anthropometric program for the smartphones on operation system Android. - Construction of a 3D-model of the human body based on the results of anthropometric features extraction. Our system can also be used to integrate to different environments, such as online shopping websites to support users fitting their clothes sizes and medical applications. The flowchart of our anthropometric system is described in figure 1. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 5. Flowchart of data classification
<p>The Random Forest is a powerful classification method because of the following. First, errors are minimized as a result of a random forest, synthesizing through training (learner). The second, random choice at every stage in the Random Forest will reduce the correlation between the learners in the synthesis of the results. In addition, we also found that the total error of layered forest trees depends on their individual errors in forest trees, as well as the correlation between the trees. The article uses the wrapper model (Christopher Tong, 2000) with the objective function for the evaluation, Random Forest algorithm is shown in figure 5. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 8. Model 3D of man body
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 8 .</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.