Tools i used in my experiment :
1- Mac MINI: Version 10.13.6 Processor 2.3GHz Intel Core i5Memory 8GB 1333 MHz DDR3 Graphics intel HD 3000, 512 Mb Serial Number C07GW9HFDJD0 The Mac mini is the device used in programming, it has Java installed on it and it used to collect brain wave data , data analyzing and data merging . 2- Nintendo switch:for this experiment the Nintendo switch is needed rise the concentration level , in order to make high reading on brainwave . Playing games with Nintendo switch helps the brain wave on making a certain reading, this reading for the most intense moment in the experiment. 3- Book ;The book is needed on concentration process as well but unlike the Nintendo switch, reading book will make the brain waves work on different level, it won’t be intense but it won’t be relaxing too. 4- Bose headphone;This device is used for music, listening to certain type of music helps the mind waves entering the relaxing mode . In this mode we need to record brain wave on relaxing mode to compare it with attention or concentration mode. 5- I Pad;The iPad is used for watching movie clips , the difference between watching movies and listen to music that when listening to music we only use our ear to hear and that gives us a reading but when watching a movie clip we use both eye and ear to see and hear and that gives us another reading . 6- Mind wave sensor;We use mind wave sensor to record mind waves to mini mac, for there cording of brain waves electrodes to pick up the electrical activity and their attached with the EEG machine.
For data analysis i used java command line to run libsvm (support vector machine ) SVM is a pattern recognition technique based on super vised learning. It has heightened generalization capability, and a “kernel trick” allows it to applying cases where liner classification is impossible. After collecting all the data,we need its time for using the SVM to train the data, merging the sets and check the accuracy. We have two type of kernel the 0 (linear) and 1 (polynomial) and the deference between them that linear require less time in training meanwhile the polynomial kernel looks not only at the given features of input samples to determine their similarity, but also combinations of these. Sometimes they get the same accuracy depends on the data sets.
I used Weka as well for various options on data analysis , weka includes tools for data preparation, classification, regression, classification, association rule mining, and visualization.
