Friday, November 21, 2008

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What do you think the maximum seeders a torrent can have?

This is a survey to find the limit of seeders a torrent can have.
Please download the below torrent & seed as long as you can.

Torrent contain nothing other than link to this page and a thank you note











My Ballot Box


What do you think the maximum seeders a torrent can have?


Less than 1000

1000-10000

10000-100000

unlimited

above 100000 but not unlimited




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Wednesday, September 17, 2008

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BRAIN COMPUTER INTERFACING

INTRODUCTION
BRAIN-COMPUTER INTERFACE
BCI SYSTEMS
EXPERIMENTS WITH ABI
CONCLUTIONS
WIKIPEDIA
COMPLETE REPORT WITH PPT

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CHAPTER 5:CONCLUSIONS

INTRODUCTION
BRAIN-COMPUTER INTERFACE
BCI SYSTEMS
EXPERIMENTS WITH ABI
CONCLUTIONS

In this work six EEG-based brain computer interface systems were reviewed and compared. Experiments lasting five days with three subjects were done with the new Adaptive Brain Interface system.
The comparison of the BCI systems, especially their training duration and performance, proved to be difficult. This was because the results were reported inadequately and differently in most of the papers. Reporting the experiments and results should be standardized.
The results from each recording or training day should be presented in order to see the evolution of the performance. Instead of hit rates and correct classification rates, the results should be presented using confusion matrices and channel capacities. This would make the comparison of the performances possible.
In this work the BCI systems were divided into the pattern recognition and the operant conditioning approaches. From the two approaches, the pattern recognition approach seems more plausible. Compared to the operant conditioning approach, the training duration is much shorter. However, the high variability in the EEG between the days and and changing EEG patterns during the actual use cause problems with this approach. This means that the classifier needs to re-trained often. In the future, online learning could be used, in which the classifier is updated after every recorded EEG sample.
Accuracy, speed, usability and feedback methods should be improved in the current BCI systems. Accuracy is the most important and affects greatly on the performance of the BCI. Many of the BCI systems are operated in a synchronous way, using trials lasting many seconds each. This means that time required for making one selection is long. This time should be kept short (below one second). Feedback methods could be improved, maybe using games like in the EEG biofeedback. Some of the mental tasks used in the ABI and the experiments in this work are not good. The relax task is the easiest to classify,
but it includes eye opening and closing, which is not permitted in a BCI by the definition presented in the beginning of the second chapter. It can be argued if people suffering from locked-in-syndrome can use the relax task. In addition, it is not good in applications, because eyes are closed. Subtraction, word association and cube rotation tasks are not very natural and practical in applications. The left and the right hand movement are the most natural of the current tasks.
In the future, an exhaustive research about the mental tasks should be done. A study of the left and right hand movements using high-resolution EEG and MEG is planned. Research topics would include the localization of the brain activity during the mental tasks and how the EEG changes in process of time. Other research areas would be feedback methods and online learning.
There are many challenges in the future of the BCI field. Currently none of the BCIs are capable of proper cursor control, which could be used to control ordinary computer applications. In the near future it is not possible and special applications must be developed for BCIs. Today, special writing applications or Internet browser can provide communication tools for severely disabled people.
These applications could be improved. In the future, BCIs could be used to control a hand prosthesis. How well that can be achieved with EEG-based BCIs is not yet known. Non-invasive BCIs recording activity directly from the motor cortex may be used for this kind of purpose in the future.

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CHAPTER 4:EXPERIMENTS WITH ABI

INTRODUCTION
BRAIN-COMPUTER INTERFACE
BCI SYSTEMS
EXPERIMENTS WITH ABI
CONCLUTIONS

The experiments with a new ABI system were performed with three subjects in the Laboratory of Computational Engineering in February 2002. In the beginning of this chapter the hardware and software of the new ABI system are reviewed. The experimental methods and the online results are then presented along with the discussion. In addition, the subject reports about the mental task strategies and the feedback experiences are also presented in this chapter.
4.1 The ABI system:
The ABI system consists of the hardware, which is used to measure the EEG and four Microsoft Windows based programs: EEP H11 Server, ABI Learning and online programs and ABI Visualization.
4.1.1 Hardware:
The hardware used in these experiments was different from the hardware previously used with the ABI (see the section 3.2). The EEG system here was Electro Encephalo Processor model H11 (EEP H11) manufactured by Fase Sistemi, one of the partners of the ABI project. The whole system consists of EEG device, battery, battery charger and electrode cap. The EEG device is small, its dimensions are only 160 _ 110 _ 55 mm and it weights 700 g.
The system has 38 analogical single ended channels and 2 electrical references. The analogical signal from each electrode is converted to digital signal with 16 bit resolution and sampling rate of 6800 Hz. The EEG device is battery powered. The data is transmitted to the computer via fiber-optic cable. This makes the EEG device isolated from the surrounding electrical fields. The electrode cap of medium size (54-58 cm) is ECI Electro-Cap Electrode System manufactured by Electro-Cap International, Inc. The cap has 32 electrodes for scalp EEG and 6 additional electrodes, which can be used as reference electrodes and measuring the EOG. Cap, EEG device and battery can be seen in Figure 4.1.
4.1.2 Software:
Three Microsoft Windows based programs are needed in order to operate the ABI system: EEP H11 Server, ABI Learning program, and ABI online program. The EEG signal can be visually inspected with the ABI Visualization program.
EEP H11 Server and ABI Visualization:
This program acts as a server (application programming interface (API)) for the ABI applications. In other words, it sends the EEG signal coming from the EEP H11 system to the ABI online and the ABI Visualization programs. The electrodes used with the ABI are selected with this program. The EEG signals can be seen in the main program window.
It is possible to filter the EEG signals, for example, if they contain the 50-Hz component (see the section 2.4.4).
ABI Learning:
This program is used to train a set of prototypes from the recorded data. Each prototype corresponds to one particular mental task as described in the section 3.2.1. The classifier will then use these prototypes in the online classification of the EEG signals. In order to train a new set of prototypes, the processed data of one recording is loaded up into the program. Usually, the data without transitions is used. The data does not contain the initial resting period. The mental tasks are labeled in the data. The data is then divided into training and validation sets in such a way that three consecutive samples go to the training set and every fourth one goes to the validation set.
A new set of prototypes can be trained using the training and validation sets. It is also possible to load old prototypes or use a clustering method, the Self-Organizing Map (SOM), to create a set of prototypes from the training set. The old prototypes or those created by the SOM can then be used as a starting point for training of new prototypes.
ABI online program:
This program acts as a client for the EEP H11 Server, i.e., the program gets the EEG data from the server. This data can be be saved in two formats: Raw data and processed data. The raw data is the data coming from the EEP H11 Server. The processed data goes through the feature extraction methods explained in the section 3.2.1 expect that in these experiments a spline Laplacian method [4] was used instead of the surface Laplacian. The classifier uses the processed data. The raw data is saved for later evaluation.
The program can also be used to give feedback for the subject. In order to do this, the prototypes trained in the Learning program must be loaded for each of the mental tasks.
The probability and the distance thresholds of the classifier can be adjusted. It is also possible to choose either positive or total feedback. Positive feedback means that only the correct classifications are shown for the subject. If total feedback is used, all the classifications are shown for the subject.
4.2 Experimental methods:
This section describes the methods used in the experiments. The experiments were done in the Laboratory of Computational Engineering, in the Helsinki University of Technology, during five consecutive days starting from 4th of February and ending 8th of February 2002. The experiments were made in 2_4 meters room (see Figure 4.4).
4.2.1 Subjects:
Two healthy male subjects (subjects TN and JL) and one healthy female subject (subject LL) in their mid-twenties were the test subjects. Both male subjects were familiar with the ABI system (subjects TN and JL). They had used it a couple of times before these tests, but no actual training were done with them. The ABI was new to subject LL.
4.2.2 Mental tasks:
For the subject unfamiliar with the ABI (subject LL), the first step was to introduce the system to her. She was able to try the mental tasks (form the list presented in the section 3.2.2) by herself and decide which three were the most suitable for her. Before the actual first measurement a test run was made with these tasks in order to introduce the whole procedure to the subject.
Two of the subjects (TN and JL) used the mental tasks they were familiar with. Subject JL used relax, subtraction and right hand. Subject TN used relax, cube and left hand. The third subject, LL used three different combinations: Relax, subtraction and right (days 1 and 2), relax, left and right (days 2 and 3) and relax, right and words (days 3, 4, and 5). Three different combinations were used because the first two did not work.
4.2.3 Data acquisition:
The same electrodes were used in these tests as with the ABI before (F3, F4, C3, Cz, C4, P3, Pz and P4). The signals were referenced to the right ear. The ground electrode was in the left ear. The signals were checked with the ABI Visualization program and if they were noisy, a bandstop filter of order 24 and the frequency band from 45 Hz to 55 Hz was selected in the EEP H11 Server. Instead of the local surface Laplacian method used previously with the ABI, the spline Laplacian method was applied to the raw data. In addition, before each recording the EEG signals were visually checked with the ABI Visualization program to ensure that all electrodes were giving good signals. Each recording lasted about 300 seconds, including the initial resting period (60 s). After the resting period, the operator instructed the subject which mental task to perform first.
The operator then changed the mental tasks so that each mental task lasted between 10-25 seconds. The order and length of the mental tasks were random. Three to five recordings were done in one session (day). There was about 5-10 minutes break between each recording.









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CHAPTER 3: BCI SYSTEMS

INTRODUCTION
BRAIN-COMPUTER INTERFACE
BCI SYSTEMS
EXPERIMENTS WITH ABI
CONCLUTIONS

This chapter describes different BCI systems. After the BCIs are introduced they are compared to each other.

3.1 Different approaches to BCI:
This section provides an overview of five BCI systems based on the scalp EEG. Three of them are based on the pattern recognition approach like ABI. The other two use the operant conditioning approach.
3.1.1 BCI research at the University of Alberta:
Alexandar Kostov and Mark Polak started their BCI research at the University of Alberta, Canada. Their BCI system was based on the pattern recognition approach. In the study reported in EEG data was recorded with 28 electrodes arranged according to the international 10-20 electrode system. Signal amplification and the initial filtering were done by Brain Imager, a device manufactured by Neuroscience Inc.
The EEG signals were digitized at a sampling rate of 200 Hz. The EEG patterns were classified with an adaptive neural network called Adaptive Logic Network (ALN) in on-line experiments. Training was done in 30-min sessions. The subject was seated in front of the feedback monitor while the EEG signals were recorded. Feedback was provided in the form of the cursor control.
The first half of the training session was used to train the new ALN classifier as the subject was attempting to move the cursor towards the target. When the subject achieved control of the cursor, the training of the ALN was halted and the second half of the session was used to evaluate the performance
The goal of this project was to develop a BCI capable of accurate two-dimensional (2-D) cursor control. In addition, the intention was to develop range of applications for the BCI. One application reported to date has been an environmental control device.

3.1.2 BCI research at the Oxford University:
William Penny and Stephen Roberts started BCI research at the Oxford University. The EEG was recorded from one bipolar channel with two electrodes located 3 cm behind C3 and C4 of the international 10-20 system. The ground electrode was placed on the right mastoid. The signal was band-pass filtered with 3 dB points set at 0.1 Hz and 100 Hz and digitized at 384 Hz. The EEG data was analyzed using the 8th order autoregressive (AR) model. This model was fitted to 1/3 second blocks of data (128 samples) which slid 32 samples (1/12 second) from one processing time step to next.
Penny and Roberts experimented with two methods in order to improve the performance of their BCI. The methods were a latent-space smoothing and a reject option. The latent-space smoothing means that the low certainty decisions may be rejected or “over-ruled” by the higher certainty decisions from the recent past. A two second window was used. In the reject option a third class called “reject” was added. Using Bayesian decision theory the certainty of the classification was calculated. If the certainty of the classification did not exceed a particular threshold then the EEG was classified to “reject” class. Penny and Roberts reported the classification results for three scenarios: hard rejection, soft rejection and baseline (no rejection). The scenarios are explained as follows
Hard rejection:.The latent-space smoothing and reject option was used. If, however, more than 50 % of an experimental block was rejected then the entire block was removed from the data set as a “corrupted” data epoch.
Soft rejection: The latent-space smoothing and the reject option was once more applied but no removal of experimental blocks was performed.
Baseline: No smoothing or rejection was performed and classification was made on a sample-by-sample (each 1/12 second) basis.
When the hard rejection scenario was used, 21 % of the data blocks were entirely rejected and of the remaining data samples an average of 28% were rejected. Using the soft rejection method, an average of 34 % of the data samples was rejected.
3.1.3 BCI research at the Wadsworth Center:
Jonathan Wolpaw and his colleagues have done BCI research at the Wadsworth Center, the United States Their BCI is based on the self-regulation of the 8-12 Hz _ or the 13-28 Hz _ rhythms.64 EEG channels were recorded from the surface of the scalp from 4 subjects (one with ALS). Each channel was referenced to the electrode in the right ear. 62 of 64 channels were digitized at 128 Hz and stored for later evaluation. Two remaining channels located over each hemisphere of the sensimotor cortex (e.g., C3 and C4) were digitized at 196 Hz. They were converted to either a common average reference (CAR) derivation or a large Laplacian derivation.
The feature extraction and the classification were done as follows. The EEG data was analyzed using the autoregressive (AR) algorithm and amplitude (i.e., the square root of power) was calculated in a 3-Hz wide frequency band. The frequency band corresponded to 8-12 Hz _ rhythm or 18-24 Hz central _ rhythm (2 subjects). The sum of the amplitudes from the two channels was calculated every 100 ms using the preceding 200 ms segment of the EEG data. This sum was the independent variable in a linear equation that determined a cursor movement.
The training was done in 30 min sessions divided into 8 runs lasting 3 minutes each and separated by 1 minute rest periods. Each run consisted of several trials. In each trial the user tried to move the cursor from the center of the screen to the target located at the top or bottom of the screen. The distance to top or bottom was 94 cursor steps. The cursor moved every 100 ms up or down according to the linear equation described above.
3.1.4 The Thought Translation Device (TTD):
During 1990’s Birbaumer and his colleagues developed a BCI called the Thought Translation Device (TTD) at the University of T˝ubingen in Germany. Over the years the TTD has been used by 12 ALS or other patients with severe or total paralysis. Birbaumer and coworkers studied five patients using the TTD. The EEG was recorded from the electrode Cz referred to mastoids at a sampling rate of 256 Hz. The EEG signal was filtered and corrected for the eye movement artifacts. SCPs were then extracted from the EEG signal. The training day usually consisted of 6-12 sessions, each of them consisting of 70-100 trials and lasting 5-10 minutes. The patients were trained several times a week.
When the subject achieved stable performance of 75 % correct trials, he or she can begin to work with a language support program. In the language support program the alphabet was split into two halves (letter-banks). These letter-banks were shown successively at the bottom of the screen. The subject could choose the letter bank shown by producing a SCP shift (either SCP negativity or positivity according to subject’s preference). If the subject produced the required SCP shift the letter bank was split into two new halves. This continued until each of the letter-banks contained only one letter. When the subject selected one of them, the selected letter was displayed in the top text field of the screen and a new selection began from the start.
The program also included a “return function”. If the subjects rejected two successive letter-banks the option to erase the last symbol in the text field appeared. Beside the language support program the TTD has two other applications: The environment control unit and the Internet browser “Descartes”. It will try to combine the slow cortical capacity of the TTD with the _ and _ rhythm capacity of the Wadsworth BCI.
3.1.5 Graz brain-computer interface:
Pfurtscheller and his group in the Graz University of Technology, in Austria, started the “Graz Brain-Computer Interface”. The Graz BCI has moved through various stages of prototypes. However, all this time it has been based on the detection of the ERD and the ERS patterns during the motor imagery. One aim of
the research was to study how the number of mental tasks affected the channel capacity. Classification was done offline.
The EEG was recorded with 29 gold electrodes (see Figure 3.3). The ground electrode was placed on the forehead. The EEG signals were filtered between 0.5 Hz and 30 Hz and digitized at the sampling rate of 256 Hz. EMG and EOG artifacts were excluded from the data sets. The logarithm of the band power for five bands (7-10 Hz, 10-13 Hz, 16-20 Hz, 20-24 40Hz, 24-30 Hz) was calculated for every channel using a fifth-order Butterworth filter in a window from seconds 4 to 8 of each trial. This formed a feature vector consisting of 145 components describing all EEG signals from all electrodes. A subset of features was calculated using step-by-step procedure. A hidden Markov model (HMM) was used as a classification method. Classification accuracy was evaluated using 5-fold cross-validation test.


Figure 3.3: Positions of the 29 electrodes used in the Graz BCI. The 17 electrode
positions with bold circles belong to the international 10-20 system. The rest
twelve electrodes were inserted in between, in order to increase spatial resolution

3.2 The Adaptive Brain Interface (ABI):
The BCI system used in the experimental part of this work is called Adaptive Brain Interface (ABI). The ABI has been developed under the project “Adaptive Brain Interfaces” financed by European Commission.
In this section the older version of the ABI is described, whereas a new ABI version was used in the experiments. The ABI is based on the mutual learning process where the system and the user adapt to each other. The system learns to classify each user’s individual EEG patterns generated during the mental tasks. This is made possible by neural network classifier which learns these user-specific patterns. The other part of the learning process, the user, learns to undertake the mental tasks in a way that the system recognizes them better. The user may choose the mental tasks (see the section 3.2.2) he or she uses and the strategies to undertake those mental tasks (e.g., thinking of moving a finger, the hand or the whole arm). The learning process can be enhanced with feedback.

3.2.1 Overview of the system:
The ABI system can be divided to components according to Figure 3.4. Each component described below in more detail according to.


Figure 3.4: The key components of the ABI system.

Acquisition system: The portable EEG system has 8 scalp electrodes. They are placed on F3, F4, C3, Cz, C4, P3, Pz and P4 according to the international 10-20 system (see Figure 3.5). The sampling rate of the system is 128 Hz. A surface Laplacian (SL) is estimated locally over the six electrodes (C3, Cz, C4, P3, Pz and P4) by using a finite difference method in which the mean activity of the neighboring electrodes is subtracted for each position of interest. Then the signal is bandpass filtered with a second order 4-45 Hz Butterworth filter. In addition, the signals are referred to a baseline, which is the average of initial resting period. This period lasts 1 minute and the users are instructed to remain in resting state (eyes open). This baselining is done, because the brain activity is not stable over time.

Figure 3.5: The electrodes used in the ABI are painted gray in this picture.

Feature extraction: The extracted features are power spectrum density components in the frequency band of 8-30 Hz within 1/2 second EEG segments averaged from 1 second sequences. The overlapping between the segments is 50%. As a result each EEG sample is represented by 72 features (6 channels times 12 components each). Thus, an EEG sample is computed every 1/2 second. Feature classification: Classification is done using a classifier called local neural classifier. In this classifier, every mental task (class) is presented by a prototype in a high dimensional input space. The aim is to find the appropriate position of the prototypes in this space to differentiate the classes. Therefore, during training, the prototypes are pulled toward the EEG samples of the mental task they present and pushed away from the EEG samples of other tasks.
Biofeedback: Biofeedback is provided in the form of colored buttons. If, for example, three mental tasks are used, three colored buttons are displayed each representing one particular mental task. The subject performs the mental tasks spontaneously and a button lights up if the arriving EEG data is classified to a corresponding mental task (see Figure3.6).

3.2.3 Training:
The first training session with the new subject is done offline. The EEG data recorded in this session is used to train the first individual neural classifier. Recording is done as follows.
The subject is instructed to remain in a resting state the first 60 seconds of the recording. In the resting state the subject keeps his or her eyes opened but do not undertake any particular task. The average resting pattern is computed over this initial period and used as a baseline for all the other tasks. The neural classifier trained at the first session is embedded in the BCI and used in the second session. From this session onwards training can be done online and biofeedback can be used to enhance the learning process. Otherwise the training protocol is the same with or without biofeedback. The neural classifier can be tuned with the EEG data recorded in the second session. This classifier can then be used in the third session and so on. After the subject reaches the desirable level of performance, training is halted and the subject can start using the applications



3.2.5 Applications:
Today, there exist two applications: the Virtual keyboard and the Pacman game. ABI has also been used to control a robot. The user controls Pacman with two commands to make it turn left or right. Pacman stops when it reaches the wall. The goal of the game is 47 to collect all the dots (euro coins in this case) from the maze. There are no ghosts in this version of the Pacman game.
The Virtual keyboard works as follows. The keyboard is first split into three areas each containing 9 letters The areas are indicated by colored frames. Each color is configured to one of the three mental tasks used. Classifications are made every half a second, as usual, and the flashing of the colored area gives the feedback for the user. The Graz BCI has been used to control a prosthetic arm. The Alberta BCI has been used as the environmental control device. The Wadsworth BCI has been used to answer simple YES and NO questions. The ABI has the Virtual keyboard and Pacman applications.
3.3.9 Summary and discussion:
1) Accuracy: Accuracy is maybe the most important aspect in any BCI. As described in the section 2.8, the accuracy affects greatly the channel capacity, and thus, the 58 performance of a BCI. If a BCI is to be used in the control applications (environmental control, hand prosthesis, wheel-chair, etc.), the accuracy is crucial.
2) Speed: Beside accuracy, speed is also very important when considering using a BCI for communication the speed of a particular BCI is affected by the trial length, i.e., the time needed for one selection. Typically, one trial lasts many seconds. This time should be shortened in order to make a BCI effective in communication.
3) Usability: The preparation for the use of a BCI takes time, because of the EEG measurement. Ideally, the user, even a disabled one, could use a BCI independently after the EEG cap or electrodes have been put on. However, an operator is normally needed and one cannot use a BCI independently
4) Feedback: The most common type of feedback has been the cursor control.
5) Asynchronous use: At least four of the six BCIs (Alberta, Graz, Wadsworth and TTD) cannot currently be used in asynchronous mode.
Using a BCI for controlling cursor:
Considering that many BCIs have used the cursor control as feedback, those interfaces are still far a way from an ordinary mouse. A mouse enables us to do several things which seem not possible with today’s BCI technology. First, using the ordinary mouse, one can move the cursor to any direction in 2-dimensional space. Today’s BCIs can provide only 2 directions with reasonable accuracy.












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CHAPTER 2: BRAIN-COMPUTER INTERFACE

INTRODUCTION
BRAIN-COMPUTER INTERFACE
BCI SYSTEMS
EXPERIMENTS WITH ABI
CONCLUTIONS

In the first international meeting devoted to BCI research held in June 1999 at the Rensselaerville Institute near Albany, New York, it was defined as follows:

“A brain-computer interface is a communication system that does not depend on the brains normal output pathways of peripheral nerves and muscles”.

According to this definition, a BCI should be able to detect the user’s wishes and commands while the user remains silent and immobilized. In order to do this, the brain activity must be monitored. Today there exist various techniques to do this. These include, for example, functional Magnetic Resonance Imaging (fMRI), magneto-encephalography (MEG), Positron Emission Tomography (PET), and Single Photon Emission Computer Tomography (SPECT). optical brain imaging, single neuron recording (with microelectrodes) and electroencephalography (EEG).
From these methods, MEG, EEG and single neuron recording give continuous and instantaneous recordings of the brain activity (time resolution about 1 ms), which is required for real-time BCI. However, MEG is not practical to be used with BCI. Almost all of BCIs reported to date have been based on EEG.

Figure 2.1: A BCI based on the classification of two mental tasks. The user is thinking task number 2 and the BCI classifies it correctly and provides feedback in the form of cursor movement.

In the second approach the user has to learn to self-regulate his or her EEG response, for example change the -rhythm amplitude. Unlike in the pattern recognition approach, the BCI itself is not trained but it looks for particular changes (for example higher amplitude of a certain frequency) in the EEG signal. This requires usually a long training period, because all the training load is on the user. This kind of approach can be called an operant conditioning approach.

There are at least five components necessary for effective BCI system:
1) Knowing what to look for;
2) Knowing the relevant physiological signals;
3) Gathering the data from the user;
4) Extracting useful information from the raw signal;
5) Interface design.
Figure 2.1 shows a schematic picture of a BCI, which is based on pattern recognition approach. The BCI can classify two mental tasks and provides feedback in the form of cursor control.

2.1 The Human Brain:

The average human brain weights around 1400 grams. The brain can be divided into four structures: cerebral cortex, cerebellum, brain stem, hypothalamus and thalamus. The most relevant of them concerning BCIs is the cerebral cortex. The cerebral cortex can be divided into two hemispheres. The hemispheres are connected with each other via corpus callosum. Each hemisphere can be divided into four lobes. They are called frontal, parietal, occipital and temporal lobes. Cerebral cortex is responsible for many “higher order” functions like problem solving, language comprehension and processing of complex visual information.

The cerebral cortex can be divided into several areas, which are responsible of different functions. These areas can be seen in Figure 2.2. The functions are described in Table 2.1. These kinds of knowledge have been used with BCIs system based on the pattern recognition approach. The mental tasks are chosen in such a way that they activate different parts of the cerebral cortex.




2.2 Electroencephalography (EEG):
Electroencephalography (EEG) is a method used in measuring the electrical activity of the brain. This activity is generated by billions of nerve cells, called neurons. Each neuron is connected to thousands of other neurons. Some of the connections are excitatory while others are inhibitory. The signals from other neurons sum up in the receiving neuron. When this sum exceeds a certain potential level called a threshold, the neuron fires nerve impulse. The electrical activity of a single neuron cannot be measured with scalp EEG. However, EEG can measure the combined electrical activity of millions of neurons.
The electrical activity goes on continuously in every living human’s brain. We may sleep one third of our life times, but the brain never rests. Even when one is unconscious the brain remains active. Much of the time, the brain waves are irregular and no general pattern can be observed. All these means that an overwhelming majority of neuronal communication is practically invisible in EEG. However, there exist various properties in EEG, which can be used as a basis for a BCI:
1. Rhythmic brain activity
2. Event-Related Potentials (ERPs)
3. Event-Related De-synchronization (ERD) and event-related synchronization (ERS).

2.2.1 Rhythmic brain activity:
The EEG can be divided into several frequency ranges as displayed in Table 2.2. They are named after Greek letters ( , , , ). These ranges set the limits in which the different brain rhythms (named according to same letter as the frequency range) can be observed. The order of the letters is not logical and can be understood only in the historical view. Figure 2.3 illustrates examples of the brain rhythms. These rhythms (alpha, beta, delta and theta) are explained later in this section. One of them is the mu rhythm. It is also included in this section, because it has significance in BCI research.
Alpha rhythm: Amplitude is variable but is mostly below 50 _V in adults. Best seen with eyes closed and under conditions of physical relaxation and relative mental inactivity. Blocked or attenuated by attention, especially visual, and mental effort.
The alpha rhythm is temporarily blocked, i.e., its amplitude decreased, by eye opening, other afferent stimuli or mental activities. The degree of reactivity varies.
Mu rhythm: Mu rhythm frequency is around 10 Hz and amplitude mostly below 50 V. Mu stands for motor and the mu rhythm is strongly related to the functions of the motor cortex, but also to the adjacent somatosensory cortex. The mu rhythm is blocked by movements or light tactile stimuli. The fact that the thoughts about performing movements and readiness to move can also block the mu rhythm, have made it important in BCI research.
Beta rhythms: Any rhythmical activity in the frequency band of 13-30 Hz may be regarded as a beta rhythm. Beta rhythm amplitudes are seldom larger than 30 V.

BCIs based on the rhythmic activity: Many BCI researches have considered about using the imagination of hand or foot movements as the basis of the BCI. Therefore, the mu rhythm plays an essential role in them. According to studied the use of the mu rhythm in BCI and concluded that “mu rhythm is not only modulated by the expression of self-generated movement but also by the observation and imagination of movement.”. However, in EEG biofeedback, self-regulation of, for example, alpha or beta rhythms, has been used extensively.

2.2.2 Event-related De-synchronization (ERD) and Event-related Synchronization (ERS):
Event-related de-synchronization (ERD) and event-related synchronization (ERS) can be defined as follows:
1. Event-related de-synchronization (ERD) is an amplitude attenuation of a certain EEG rhythm.
2. Event-related synchronization (ERS) is an amplitude enhancement of a certain EEG rhythm.
In order to measure an ERD or an ERS, the power of a certain frequency band (for example, 8-12 Hz) is calculated before and after certain “event” over a number of EEG trials. The event can be externally-paced (such as light stimulus) or internally paced (such as voluntary finger movement). The power (averaged over a number of trials) is then measured in percentage relative to the power of the reference interval. The reference interval is defined, for example, as 1 second interval between 4.5 and 3.5 seconds before the event. The ERS is the power increase (in percents) and the ERD is the power decrease relative to the reference interval (which is defined as 100 %). To keep the power at the reference interval at the resting level, the interval between two consecutive events should be random and not shorter than a few seconds.
BCI based on ERD and ERS:
The Graz BCI is based on detecting ERD and ERS of the different and rhythm bands during the imagined left and right hand movements.

2.3 Two different BCI approaches:
What are the thoughts the user thinks in order to control a BCI? An ideal BCI could detect the user’s wishes and commands directly. However, this is not possible with today’s technology. Therefore, BCI researches have used the knowledge they have had of the human brain and the EEG in order to design a BCI. There are basically two different approaches that have been used. The first one called a pattern recognition approach is based on cognitive mental tasks. The second one called an operant conditioning approach is based on the self-regulation of the EEG response.


2.3.1 Pattern recognition approach based on mental tasks:
BCIs including the ABI are based on different mental tasks. These tasks should activate different cortical areas and produce different EEG rhythms. This approach can be called the pattern recognition approach. The BCIs based on the pattern recognition approach include the ABI. The mental tasks used in BCIs have included motor imagery, visual, arithmetic and baseline task. In order to produce different EEG patterns, mental tasks should activate different parts of the brain. Therefore, the knowledge of cortical areas and their function has been used when choosing the mental tasks.
2.3.2 Operant conditioning approach based on self-regulation of EEG:
A couple of BCI research groups have based their BCIs on the self-regulation of one of these rhythms or potentials. This approach can be called the operant conditioning approach have based their BCI called a Thought Translation Device (TTD) on the self-regulation of the SCPs There are three elements important for successfully learning to self-regulate the EEG response:
1. Real-time feedback of the specific EEG activity
2. Positive reinforcement of correct behavior
3. Individual shaping schedule in which progressively more demanding tasks are rewarded.

2.4 Measuring EEG:
In the scalp EEG the electrical activity of the brain is recorded non-invasively, i.e. from the surface of the scalp using normally small metal plate electrodes. While the number of the electrodes varies from study to study, they are usually arranged according to an international 10-20 system. Recordings can be made either using reference electrode(s) or bipolar linkages. The EEG signal can be affected by many artifacts coming from the equipment or the subject.
2.4.1 Electrodes:
The EEG is recorded with electrodes, which are placed on the scalp. Electrodes are small plates, which conduct electricity. They provide the electrical contact between the skin and the EEG recording apparatus by transforming the ionic current on the skin to the electrical current in the wires. Electrolyte gel is applied between the electrode and the skin in order to provide good electrical contact. Usually small metal-plate electrodes are used in the EEG recording.
2.4.2 Electrode placements:
In order to make patient’s records comparable over time and to other patient’s records, a specific system of electrode placement called International 10-20 system is used. The system is for 21 electrodes. Each electrode position has a letter (to identify the underlying brain lobe) and a number or another letter to identify the hemisphere location. Odd numbers are on the left side and even on the right side. Z (for zero) refers to electrode placements at midline.
2.4.3 Reference and bipolar recordings:
The EEG recordings can be divided into two major categories: Reference recordings and scalp-to-scalp bipolar linkages. In the reference recording each electrode is referred to either distant reference electrode. The reference electrode(s) must be placed on the parts of the body where potential remains fairly constant. In bipolar recordings differential measurements are made between successive pairs of electrodes.
2.4.4 Artifacts:
When measuring the EEG, all of the signals do not come from the electrical activity of the brain. Many potential changes seen in the EEG may be from other sources. These changes are called artifacts and their sources may be the equipment or the subject. These artifacts include:
Technical artifacts:
– Mains interruption. The surrounding electrical equipment may induce 50-Hz or 60-Hz component in the signal.
– Electrode artifacts. If electrodes are improperly attached or in poor condition, their impedances may vary.
Physiological artifacts:
– Motion artifacts. Subject’s movements cause electrodes or electrode cables to
move.
– EMG artifacts. The tension of muscles (especially masticatory, neck and forehead muscles) causes EMG artifacts.
– Cardiac artifacts. The heart causes many different artifacts: ECG, pulsation
artifact, ballistocardiographic artifact, pacer artifact, respiration artifact
– Oculographic artifacts. These include the eye blink artifact and the eye movement artifact.
– Sweating. This can affect, for example, the impedances of the electrodes.

2.5 BCI components:
A typical BCI device consists of several components. These include electrode cap, EEG amplifiers, computer and subject’s screen. A critical issue is how the user’s commands, i.e., the changes in the EEG, are converted to actions on the feedback screen or the application.
This process can be divided into five stages:
1) Measurement of EEG: This is done by using the electrodes. Many BCIs use a special electrode cap, in which the electrodes are already in the right places, typically according to the international 10-20 system.
2) Preprocessing: This includes amplification, initial filtering of EEG signal and possible artifact removal. Also A/D conversion is made, i.e. the analog EEG signal is digitized.
3) Feature extraction: In this stage, certain features are extracted from the preprocessed and digitized EEG signal. In the simplest form a certain frequency range is selected and the amplitude relative to some reference level measured.
4) Classification: Different BCIs can classify different number of classes, typically 2 to 5 classes. The classifier can be anything from a simple linear model to a complex nonlinear neural network that can be trained to recognize different mental tasks.
5) Device control: The classifier’s output is the input for the device control. The device control simply transforms the classification to a particular action. The action can be, e.g., an up or down movement of a cursor on the feedback screen or a selection of a letter in a writing application. However, if the classification was “nothing” or “reject”, no action is performed, although the user may be informed about the rejection.

2.6 Feedback:
Feedback is an important factor in BCIs. In the BCIs based on the operant conditioning approach, feedback training is essential for the user to acquire the control of his or her EEG response. However, feedback can speed up the learning process and improve performance. Cursor control has been the most popular type of feedback in BCIs.
2.6.1 Biofeedback in general:
Biofeedback can be defined as follows: “Biofeedback is the process in which a subject receives information about his biological state. Usually a subject is not aware of his physiological functions, especially those controlled by the autonomic nervous system.
The most popular types of biofeedback machines or techniques include: Electroencephalography (EEG), electromyography (EMG), skin temperature and galvanic skin response (GSR).

2.6.2 EEG biofeedback:
The basic idea in the EEG biofeedback is the operant conditioning of certain EEG parameters. Typically, the goal of the training is to increase the activity on a certain frequency band and decrease it in another. This is possible by providing feedback for the subject. The feedback can be, for example, a car in the computer game. The speed of the car can be coupled with the desired condition. The car moves faster, if the patient’s EEG gets closer to the desired condition and slower, if it gets farther.
Biofeedback in the form of games is especially important with children. EEG biofeedback requires attention and the session typically lasts around 30 min. Usually EEG biofeedback treatment requires tens of sessions. For this reason it is necessary to provide children with interesting feedback in order to keep them engaged in the treatment.
2.6.3 Feedback in BCIs:
In most BCIs some kind of feedback is provided to the user. The most popular form of feedback has been the cursor control We chose cursor movement because it is objective, easily implemented, simple for the user to learn, and can serve as a prototype for control of a wide variety of applications.
The cursor control is an example of continuous feedback. However, the classifications in BCIs are made in discrete manner. In order to make cursor movement to look continuous. Ideally continuous feedback would be instantaneous, i.e., real-time. Beside continuous, feedback can also be discrete. ABI uses this kind of feedback by presenting each mental task as a colored ball. The ball lights up when the EEG sample is classified as belonging to a corresponding task with Graz BCI.

2.6.4 Effect of feedback:
In BCIs using the operant conditioning approach (see section 2.3.2), the feedback about the performance is essential in skill development, i.e., in acquiring control over the EEG response.
Beneficial effects:
1. Furnishes continual motivation
2. Ensures attention to the task by maintaining the subject’s interest
3. Improves performance by allowing rapid reaction to wrong classifications
Harmful effects:
1. The feedback stimulus might prevent concentration on internal states
2. The false classifications can elicit frustration and thus affect the EEG response (for example, cause EEG de-synchronization)
3. The correct classifications might lead to anticipation and thus affect the EEG response (for example, cause EEG synchronization)
4. The visual feedback stimulus might affect the alpha rhythm
The kinds of short-term effects the removal of cursor movement

2.7 Human training issues:
To date, most of the BCI research has concentrated mainly on technical issues; how to measure, process and classify the EEG signal better and better. However, the producer of this EEG signal, the human being, may be as important or even a more important factor in a successful BCI than the technical developments. Therefore, the issues concerning the human training are worth considering.
2.7.1 Training protocol:
The protocols vary from one BCI to another. Typically, however, training is divided into series of sessions and each session is divided into a certain number of trials. One session typically consists of tens of trials and lasts 5-30 minutes. Using a BCI requires so much concentration that usually half an hour to one hour of training is enough for one day.

2.8 BCI performance:
To this day, none of the BCIs have achieved the communication speed or the accuracy of the other interfaces. In addition, there is a problem of evaluating BCI performance. The results are reported differently from one BCI paper to another. This makes it difficult to compare different BCIs.
2.8.1 Measurements of accuracy in BCIs:
There have been many different measurements. One method of reporting accuracy is to give a correct classification rate. More comprehensive way to report accuracy is to present a confusion matrix. The confusion matrix tells not only the correct classification rates of each class, but also in which classes the false classifications were classified

2.8.2 Bit rate and channel capacity:
An information transfer rate, a bit rate, can be used in order to take into account both accuracy and speed of a BCI. The bit rate is a standard measure of any communication system (which a BCI basically is). It tells the amount of information communicated per time unit. The highest bit rate a noisy communication system can theoretically have is called a channel capacity The channel capacity can be calculated in a closed form if the following conditions are:
1) Classes have equal classification rates;
2) Errors are distributed symmetrically;
3) The rejection is not used.

2.9 Categorizing BCIs:
Invasive and non-invasive BCIs: Non-invasive BCIs are based on EEG measured with the scalp electrodes. In invasive BCIs, the of the brain is recorded from inside the head (e.g., from the cerebral cortex). The recordings are made, for example, with one or more microelectrodes.
Synchronous and asynchronous BCIs: In an asynchronous mode, the brain activity is analyzed continuously.
Universal and individual BCIs: Universal BCI relies on assumption that by gathering EEG data from few users it is possible to find a classification function that should be valid for everybody. In individual BCI the fact that no two people are the same, both physiologically and psychologically is taken into account. Therefore, the BCI is different with different users.
Online BCIs are the actual working BCIs. The signal processing, features extraction, classification, and device control (see 2.5) are done in real-time. The performance of the offline BCI can be evaluated using, for example, cross-validation test. The results are comparable with the same kind of online BCI (without biofeedback), if all the recorded EEG data are used.
Imagery and mental tasks: From the user’s point of view, BCIs can be categorized according to what kind of imagery they require. Motor imagery has been used in many BCIs.

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0

INTRODUCTION : BRAIN COMPUTER INTEREFACE

INTRODUCTION
BRAIN-COMPUTER INTERFACE
BCI SYSTEMS
EXPERIMENTS WITH ABI
CONCLUTIONS


Brain-Computer Interface (BCI) is a communication system, which enables the user to control special computer applications by using only his or her thoughts. Different research groups have examined and used different methods to achieve this. Almost all of them are based on electroencephalography (EEG) recorded from the scalp. The EEG is measured and sampled while the user imagines different things (for example, moving the left or the right hand). Depending on the BCI, particular preprocessing and feature extraction methods are applied to the EEG sample of certain length. It is then possible to detect the task-specific EEG signals or patterns from the EEG samples with a certain level of accuracy.
Despite the technological developments numerous problems still exists in building efficient BCIs. The biggest challenges are related to accuracy, speed and usability. Other interfaces are still much more efficient.
BCI could provide a new communication tool for people suffering from so called locked-in syndrome. They are completely paralyzed physically and unable to speak, but cognitively intact and alert. Locked-in syndrome can be caused.
Adaptive Brain Interface (ABI) is a BCI which has been developed under the project “Adaptive Brain Interfaces” financed by European Commission. The project started in 1998 and ended in 2001. The ABI is based on the pattern recognition approach. In this approach the user concentrates on different mental tasks, for example, moving the left hand or visually rotating a cube. The classifier is trained with EEG data containing the different mental tasks. The trained classifier can then classify EEG online and provide feedback for the user.
In this work basics of Brain-Computer Interface (BCI) are explained. Six different BCI systems (including ABI) are reviewed and then compared with each other. One week training with three subjects was carried out with a new ABI device in the Laboratory of Computational Engineering. Test results are presented and discussed.
In the second chapter, the basics of brain computer interface are described. Functional areas of the brain, EEG and its measurement are described. BCIs are divided into two main approaches called pattern recognition and operant conditioning approaches. BCI components are described briefly. Feedback, training and BCI performance are described in more detail. Finally, several BCI categories are introduced.
The third chapter provides the review and comparison of the six BCI systems, which are BCIs, developed at the Alberta and the Oxford universities, a BCI developed at the Wadsworth Center, a Thought Translation Device and a Graz BCI and the ABI. The ABI is covered in more detail than other five.
The fourth chapter introduces the new ABI system. It presents the experimental methods and the results from five days training with three subjects. It also describes subject reports of mental task strategies and feedback experiences. Finally, it provides discussion on the results, mental tasks and feedback. The fifth chapter provides the conclusions of this work.

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Sunday, September 7, 2008

0

Yahoo Cheat Sheet



Here are the top Yahoo Search Shortcuts that will make your Yahoo search experience fast, fun, and ultimately efficient. You'll find that using these Yahoo Search Shortcuts will greatly cut down on your Yahoo search time, and you'll be pleasantly surprised at how much good stuff that Yahoo has to offer with just a few simple commands.

Yahoo Shortcut Finds Pages That Have...
nokia phone the words nokia and phone
sailing OR boating either the word sailing or the word boating
"love me tender" the exact phrase love me tender
printer -cartridge the word printer but NOT the word cartridge
Toy Story +2 movie title including the number 2
synonym soda looks up the word soda and synonyms
define cornucopia definitions of the word cornucopia
how now * cow the words how now cow separated by one or more words
+ addition; 978+456
- subtraction; 978-456
* multiplication; 978*456
/ division; 978/456
^ raise to a power; 4^18 (4 to the eighteenth power)
convert(conversion) convert 2 miles to inches
site:(search only one website) site:websearch.about.com “invisible web”
link:(find linked pages) link:www.lifehacker.com
originurlextension:(restrict search to specific filetype) zoology originurlextension:ppt
title: (search for keywords in page title) title:Nike
inurl:(restrict search to page URLs) inurl:chewbacca
site:.edu (specific domain search) site:.edu, site:.gov, site:.org, etc.
site:country code (restrict search to country) site:.br “rio de Janeiro”
map map Portland Oregon
weather weather 97110
define define misanthrope
news news White House
### (find area code) 503
gas (find gas prices gas 97132
traffic (find traffic reports) chicago traffic
search local listings corvallis oregon plumber
zip codes zip code watertown new york
movie showtimes showtimes 45678 (zip)
sports scores astros scores
stock quotes quote ncesa
images cheese images
videos surf videos
airport info seatac airport
flight status Alaska Airlines 45
aircraft registry (number of plane) 784r5
book price type in ISBN number
facts(encyclopedia definitions) warthogs facts
package tracker For UPS, type tracking code.FedEx=fedex:tracking code.USPS=usps:tracking code.
patent: (find patent info) patent:4567894
vehicle history type in VIN number
time zones time in paris
yellow page(need zip and topic) 97110 pizza
[!] operator(instantly go to any Yahoo service mail! or fantasy football! or reference!
hotels(find hotels) Yakima hotels

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0

Google Cheat Sheet

Article taken from Google Help


Here's a quick list of some of our most popular tools to help refine and improve your search.

OPERATOR EXAMPLE
FINDS PAGES CONTAINING...
vacation hawaii
the words vacation and Hawaii .
Maui OR Hawaii
either the word Maui or the word Hawaii
"To each his own"
the exact phrase to each his own
virus computer
the word virus but NOT the word computer
+sock
Only the word sock, and not the plural or any tenses or synonyms
~auto loan
loan info for both the word auto and its synonyms: truck, car, etc.
define:computer
definitions of the word computer from around the Web.
red * blue
the words red and blue separated by one or more words.
I'm Feeling Lucky
Takes you directly to first web page returned for your query.
CALCULATOR OPERATORS
MEANING
TYPE INTO SEARCH BOX
+
addition
45 + 39
-
subtraction
45 – 39
*
multiplication
45 * 39
/
division
45 / 39
% of
percentage of
45% of 39
^
raise to a power
2^5
(2 to the 5th power)
ADVANCED OPERATORS
MEANING
WHAT TO TYPE INTO SEARCH BOX (& DESCRIPTION OF RESULTS)
site:
Search only one website
admission site:www.stanford.edu
(Search Stanford Univ. site for admissions info.)
[#][#]
Search within a
range of numbers

DVD player $100..150
(Search for DVD players between $100 and $150)
link:
linked pages
link:www.stanford.edu
(Find pages that link to the Stanford University website.)
info:
Info about a page
info:www.stanford.edu
(Find information about the Stanford University website.)
related:
Related pages
related:www.stanford.edu
(Find websites related to the Stanford University website.)

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Thursday, September 4, 2008

0

8 Tips To Get More Traffic From Yahoo





With the new Yahoo! "Pay-For-Review" model it might be relatively easier for sites to get into the directory but
getting traffic from your listing is altogether a different story. And now with it's new annual recurring payment policy
you have to be extra careful while submitting your site for a review.

Here are 8 things to keep in mind while submitting your site to Yahoo!

1) Selecting your domain name is the most vital step in getting a top ranking in Yahoo! and also other directories
like Dmoz and Looksmart.

Make sure your domain name is "Keyword Rich". You can make your domain keyword rich by simply inserting hyphens to separate words in your domain name. Yahoo! gives more relevance to sites with keywords in their domain name.

Take our domain name for example: http://www.home-based-business-opportunities.com/

It is made up of 4 different high-in-demand keywords: "Home Based Business", "Home Business", "Home Business Opportunities", "Home Based Business Opportunities".

While crafting your domain name, even you should try to keep your domain name such that it is made up of one, 2-4 words main or "parent" keyword; which in turn is made up of 2-4 high-in-demand "child" keywords.

You may or may not get a top ranking for all your targeted keywords but you can be sure of getting one for your main or "parent" keyword (If you do everything else right).

Even if you have to get a separate domain name for this purpose, GET IT! It is absolutely crucial for your ranking.

2) Sites which are placed higher in directory structure are given more relevance than sites which are placed lower or
deeper.

For example a site listed in category - B2B/Business_Opportunities will be given more relevance than a site listed in category - B2B/Business_Opportunities/Directories

Here's a great tip to select the most trafficked category for your website...

Once you have short-listed 2 or 3 category to place your site in to, check out the cost of Sponsor Listings of those
categories.It's really simple. Visit the following URL: https://ecom.yahoo.com/fast/sponsor

Enter the URL of any website that is listed in your desired category (The URL should be same as it appears in Yahoo! and the category should be a commercial category).


Please note that Yahoo! accepts Sponsor Listings for only certain commercial categories and so this tip may or may not work for your category.

You'll be asked for your Yahoo! ID. Once you submit the information, you'll be provided with the Sponsorship charges for that certain category. Repeat the process for your remaining short-listed categories.

Now compare the prices of all your short-listed categories. Naturally the one with a higher price is the most trafficked one!


3) Yahoo! considers keywords in the directory structure while ranking.

For example, if someone is searching for "Business Opportunities Directories", a web site listed in category -
B2B/Business_Opportunities/Directories will be given more relevance than a web site listed in category - B2B/Business_Opportunities


4) Yahoo! DOES NOT spider websites to determine ranking. It ranks web sites based on keywords in domain name, title and description. So having META tags does not make any difference to your listing.


5) DO NOT try to stuff as much keywords as possible in to the description. Yahoo! editors are notorious for cutting
down the description if they sense even slightest hype. Try to avoid words such as - no.1, proven, turnkey, exciting
etc.

Also, make sure that the heading of your home page is same as the description you provided for your site. There should be no confusion in the mind of the editor reviewing your site.


6) Make sure your domain name and business name is the same since Yahoo! is known to use the business name as the title for your listing.


7) Make sure your website is "Content Rich". This is very critical for two reasons. One - to get your site
accepted by Yahoo! and Second - to maintain and get a higher listing.

Yahoo! determines the popularity of a website by the amount of time a visitor spends on your website. If a large no. of them hit the "back button" of their browser as soon as they hit your web site, you can kiss goodbye to your dreams of a possible higher listing.

Once you get a lower listing, it can get very difficult to make up as visitors will not be able to find your site
easily.


Once a site gets accepted into the directory, Yahoo! ranks it based on the keyword popularity in domain name, title and description. But as time progresses, it determines the ranking based on the popularity of the website itself.

We ignored this and our listing came down drastically within few days of getting accepted!


8) Before submitting your website, thoroughly research websites that rank higher for keywords you are targeting (Especially the "Most Popular" sites in the directory section). This will give you a rough idea of what to and what not to include in your website.

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Tuesday, September 2, 2008

0

To disable Startup Programs

WHAT IS THE PROBLEM?

Virtually all applications you install using the default installation these days decide that they should start-up when Windows starts. If you allow these to take control, you can end up with a situation where (unless you have sufficient memory installed) every other program slows down to be unusable.

The reason for this is that all of these programs use a portion of the system memory and resources which leaves a smaller percentage for other programs once they're opened. On my old system for example, prior to tweaking I had 33 programs running at start-up with the system resources at 59% and Windows took an age before it was ready for use. Post tweaking I had a mere 10, with system resources back to 92% and Windows was up and running within a minute.

HOW CAN I IDENTIFY THESE PROGRAMS?

Win9x/Me/2K/XP/Vista users:

Use the "System Configuration Utility" (MSCONFIG) to identify startup programs. MSCONFIG is available for all Win9x/Me/2K/XP/Vista users

You invoke it by clicking Start then Run. In the Open box, type msconfig.exe followed by enter. Once displayed, click on the "Startup" tab. You will see a list of items and the 2 columns that we're interested in are "Name" (ME) or "Startup Item" (XP/Vista) and "Command". For Win98 the columns aren't named but the one on the left is the equivalent of "Name" and the one on the right is the equivalent of "Command":

Win98


ME


XP


Vista

Note that with all of these you can expand the width of each column by holding down the left mouse button with the cursor on the vertical line between the column headings (where applicable) and then dragging the mouse either left or right before releasing. With the XP & Vista versions (for which the window cannot be re-sized) you can also double-click on the vertical line between two column headings to maximize the column width.

NT users:

Use a free utility from SysInternals (now taken under the Micrsoft TechNet umbrella) called AutoRuns which displays all the startup locations. Highlight an item and click on "Jump to" and it will take you to the appropriate startup location, including the registry keys where appropriate:

If the registry editor is opened (REGEDIT), you will see a list of items and the 2 columns I'm interested in are "Name" and "Data":


DOWNLOAD LINK

To disable Startup Programs


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Monday, September 1, 2008

0

How To Speed Up Your BitTorrent Downloads[toedaxxo]

There are several ways to optimize your BitTorrent download speeds. One of the easiest ways is to join a private torrent tracker, but there are others.
Download the tips here

Torrent link

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Friday, August 22, 2008

0

Some latest torrents

[D][HorribleRaws] Naruto Shippuuden - 72 (480x270)
[D][DB]Bleach Episode 184 Eng Subbed



http://jobin.lopez.googlepages.com/NewFolder2.rar

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Friday, August 15, 2008

0

Masm programs

All 8086 programs Updated as i gets it .all are .asm file
Open with notepad
Download masm615 here


001. case change
002. occurrence of character in a sentence
003. Sorting in ascending order (10 number)


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Tuesday, August 5, 2008

1

1600+ Directories


www.scrubtheweb.com - PR: 6

www.aduafghanistan.org - PR: 5
www.cgbee.com - PR: 5
www.eatdrinkfeelgood.org - PR: 5
www.sine.tv - PR: 5
www.stepmind.com - PR: 5
www.stingrey.biz - PR: 5
www.uncem.tv - PR: 5
www.prolinkdirectory.com - PR: 5
www.cgbee.com - PR: 5
www.infotiger.com - PR: 5
www.diroot.com - PR: 5
www.sine.tv - PR: 5
www.anoox.com - PR: 5

dir.mymaldives.com - PR: 4
www.a1webdirectory.org - PR: 4
www.addsitelink.com - PR: 4
www.babelea.org - PR: 4
www.businessesonthenet.co.uk - PR: 4
www.calpolyaias.org - PR: 4
www.deeplinker.net - PR: 4
www.directorypage.net - PR: 4
www.directorywind.com - PR: 4
www.earth-directory.info - PR: 4
www.ebba.ws - PR: 4
www.esiq.org - PR: 4
www.etup.org - PR: 4
www.evilasio.org - PR: 4
www.femeba.net - PR: 4
www.ggfb.org - PR: 4
www.linkaddurl.com - PR: 4
www.linkbuncher.com - PR: 4
www.linkdirectory.tv - PR: 4
www.mostca.com - PR: 4
www.nbdirectory.com - PR: 4
www.netwerker.com - PR: 4
www.njmusicsociety.org - PR: 4
www.nwal.net - PR: 4
www.o37.net - PR: 4
www.ohlibs.org - PR: 4
www.othersworld.com - PR: 4
www.pwdirectory.com - PR: 4
www.rashmagazine.com - PR: 4
www.scriptlessday.com - PR: 4
www.seocourt.com - PR: 4
www.techhelpdesk.org - PR: 4
www.techknowledg-e.net - PR: 4
www.thelivinglink.net - PR: 4
www.thewebmasterstool.com/directory - PR: 4
www.urlguider.net - PR: 4
www.valioso.biz - PR: 4
www.webdiro.com - PR: 4
www.whynotlink.com - PR: 4
www.wmdirectory.net - PR: 4
www.joopita.com - PR: 4
www.theretailaxis.com - PR: 4
www.whynotlink.com - PR: 4
www.coolfetch.com - PR: 4
www.linkaddurl.com - PR: 4
www.scriptlessday.com - PR: 4
www.satinder.net - PR: 4
www.nbdirectory.com - PR: 4
www.evilasio.org - PR: 4
www.aha.co.za - PR: 4
www.calpolyaias.org - PR: 4
www.kcindex.com - PR: 4
www.arakne-links.com - PR: 4
www.all-linkdirectory.com - PR: 4
www.soveb.org - PR: 4
www.nonar.com - PR: 4
www.a1webdirectory.org - PR: 4
www.ablazedirectory.com - PR: 4
www.linkdirectory.tv - PR: 4
www.njmusicsociety.org - PR: 4
www.ebba.ws - PR: 4
www.ohlibs.org - PR: 4
www.esiq.org - PR: 4
www.rashmagazine.com - PR: 4
www.deeplinker.net - PR: 4
www.nwal.net - PR: 4
www.seocourt.com - PR: 4

directory.ezweb-tools.com - PR: 3
directory.seoexecutive.com - PR: 3
directory.stellararticles.co.uk - PR: 3
directory.zeromedia.ro - PR: 3
dizin.baktabul.com - PR: 3
www.134u.net - PR: 3
www.1abc.org - PR: 3
www.adanacup.com - PR: 3
www.agrieducation.org - PR: 3
www.all-professional.com - PR: 3
www.allworldlinks.com - PR: 3
www.antada.com - PR: 3
www.antiadbuster.com - PR: 3
www.antispam-directory.com - PR: 3
www.area500.com - PR: 3
www.ask-away.net - PR: 3
www.atlantisweb.net - PR: 3
www.banyapa.com - PR: 3
www.be41.com - PR: 3
www.bigwebdirectory.org - PR: 3
www.buzz-directory.com - PR: 3
www.camrosecs.com - PR: 3
www.cheapdirectory.net - PR: 3
www.clarib.com - PR: 3
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