BRAINWAVE RECOGNITION

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1 College of Engineering, Design and Physical Sciences Electronic & Computer Engineering BEng/BSc Project Report BRAINWAVE RECOGNITION Page 1 of 59

2 Method EEG MEG PET FMRI Time resolution The spatial resolution 1 ms 1 ms 1 min 5 s 1cm 5 cm 5 mm 5 mm Limitations Only the cortex Low spatial Only blood Only blood of the brain resolution and flow, require flow and and difficult difficult short-life contain noise interpretation interpretation isotopes Benefits Inexpensive, user friendly Contain deeper structure Medical imaging and functional analysis Table (Brain image techniques comparison [3]) Medical imaging and functional analysis Electroencephalography (EEG) Electroencephalography (EEG) is a medical imaging technique that reads scalp electrical activity generated by brain structures [4]. This technique uses metal Page 12 of 59 electrodes and conductive media to detect the electrical activities in the brain. In 1875 English physician call Richard Caton has discovered the existence of electrical current in the brain. Furthermore, he used rabbits and monkeys for his experiments [4]. In 1924 German neurologist call Hans Berger measured the electrical activity on the human scalp using ordinary radio equipment [4]. During more than 100 years of the history, EEG technique has undergone massive progress. When compared to other brain imaging techniques, EEG recordings are acquired with relatively inexpensive and portable devices [2]. Moreover, EEG signal amplifiers with high noise rejection and high sensitivity are used to measure the voltage variations on the scalp area, resulting from the electric fields created by the firing of collections of pyramidal neurons of the cortex [2]. EEG is a realistic method compare to other brain monitoring techniques and the number of electrodes depends on the 4

3 2.4 - Brain Computer Interface (BCI) Brain Computer Interface is a technology which generates commands to control and manipulate equipment bedsides a human body by monitoring signal patterns and identifying brain thought and ideas through a computer, so as to realize one s desired actions or communication with outside environment [9]. Moreover, this technology sometimes called as direct neural interface or brain-machine interface [10]. In the 1970s university of California Los Angeles began to research about BCI technology. Furthermore, the idea behind this research was detect the intended action using brain and use extrapolated signals to move robots or prosthetic gadgets [10]. This technology has developed by researches over 4 decades. There are two methods available to extract brain signals, which are invasive BCIs and Non-invasive BCIs. The basic architecture of the BCI technology shows in below figure [10]. Page 15 of 59 Figure 2.2 (general architecture of BCI [10]) Invasive BCIs This method uses a chip which is implanted directly into brain during neurosurgery. This implanted chip contains hundreds if electrons and scientists are able to detect hundreds of firing neurons in the brain. Moreover, this method provides highest quality signals to BCI devices. However, in real world this technology limited for animals such as rats and monkeys in laboratories [10] Non-invasive BCIs This method reads brain activities other than direct neural contact via pins. Electroencephalography (EEG) is the first and most common method use in non- invasive BCIs. This system uses electrodes to pick up brain signals, where the electrodes are place against the scalp. This system is user friendly, portable and low 7

4 Signal processing Figure 3.12 (Brain signals represent in time domain [20]) For each particular frequency sound, 4-5 sets test and control data has recorded and captured brain signals represent in time domain as shown in figure Figure 3.13 (Brain signals represent in frequency domain [20]) Fast Fourier Transform (FFT) has used to convert signals from time domain into Page 26 of 59 frequency domain because signals visualise more clearly and further analysis purposes. There are 4 set of brain signal for each particular frequency Figure 3.14 (filtered brain signals of volunteer 1 [20]) Matlab convolution method has used to made signals smooth by adding a low pass filter. The figure 3.14 Shows first person s brain responds to different sound frequencies. 18

5 Signal processing In this case, used a band pass filter to filter the EEG signals and it eliminated the unwanted artifacts. Extract EEG signals This research has used three different techniques to extract EEG signals in order to identify which method is more relevant and accurate to this experiment. Wavelet transforms method this project has used five levels Wavelet Packet decomposition technique to extract EEG signals [21]. Fast Fourier Transform (FFT) after extracting the EEG signal using FFTs, top 10 amplitude values have taken from each channel [21]. Principal Component Analysis (PCA) - basically this method used to dimensionally decrease the real data to first n Eigen values. This case contains 4 eigenvectors relates to 4 eigenvalues in reducing order for each channel [21]. Signal classification In this experiment, multi-layer Perceptron Neural Network trained by a standard back propagation algorithm was used for classification. Moreover, recorded data has separated into testing and training groups. This system executes 220 trials for each task (30 testing trials, 170 training trials and 20 validation trials) [21]. Page 31 of 59 Send controlling command to the robotic arm There are many test has conducted to find out most optimal configuration for the neural network. The most optimal classifications have sent to robotic control unit to control the arm. Experiment results Figure 3.20 (Classification Rates for the 3 arm movements using (WT), (FFT) and (PCA) Feature Extraction methods [21]) 23

6 Figure 4.7 (random subject s EEG data plot in Matlab) The MUSE headband take reading from four electrodes, according to this each sample contains four channels data. Fast Fourier Transform analyse Fast Fourier Transform used to extract the EEG data. FFT is mathematical technique for transforming a time domain function into frequency domain function. The below graph is represents a random subject s channel one data plot in Matlab. Page 37 of 59 Figure 4.8 (random subject s channel one magnitude spectrum) According to the figure 4.8, it doesn t clearly show the amplitude variations. After examined the four channels EEG data figured out FFT Vector one value is too high comparing to rest of values. As a result of that FFT vector one value assumed as zero throughout whole further signal processing sections. 29

7 The above figure 4.13 demonstrates the subject one power spectrum on a log scale. The process will show in the Appendix-A section one Data protection This research has recorded five subjects brainwaves. The recoding process carried out according to the Brunel University code of research ethics guidelines. Moreover, all subjects were requested to sign participant s consent form. It delivers a clear idea about this experiment and subjects have rite to withdrawn anytime their brainwave samples. During the recording process used two personal computers, although brainwave samples store in a one personal computer and no one have access to view the brainwave data samples except people who relates to this experiment. Moreover, recorded EEG data does not store in a portable device or online applications such as Dropbox as a backup. Furthermore, participants identities will not publish and keep remain as secret among people who relates to this experiment. The data captured remain with Udara Sampath and will be kept until July 2015 after which they will be deleted upon request. The data will not be shared with anyone outside Brunel University London. It is believed that the information captured cannot be used for diagnosis of medical conditions or for identification among people in databases with more than 10 subjects. Page 40 of 59 This chapter has explained about the hardware structure of the Muse Headband and the electrode placement of the device according to international electrode system. Moreover, it clearly discussed about the EEG data capturing and recording process. This project has used FFT to extract the EEG signals and it clearly demonstrated all important steps of the further signals processing. Finally, it discussed about the EEG data protection procedure. 32

8 The above figure 5.1 and 5.2 represents the single side magnitude spectrum of the subject one test and reference sample. In the analysis process, this research only used all subjects single side magnitude spectrum to find the distance gap between reference and test sample. All subjects reference and test magnitude spectrum graphs demonstrate in the Appendix B Channel one data Euclidean distance analyses Test\ Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 1Test e e e e e+04 Subject 2 Test e e e e e+04 Subject 3 Test e e e e e+04 Subject 4 Test e e e e e+04 Subject 5 Test e e e e e+04 Table 5.1 (Channel one Euclidean distance comparison) The table 5.1 represents the channel one Euclidean distance gaps between the Page 42 of 59 reference sample and test sample for all subjects. Moreover, the red coloured data represent the minimum distance gap between reference sample and test sample relates to each column. According to the table 5.1 column one (subject one reference column), it clearly show that subject one received the minimum distance. According to this column one result, it can concluded that same subject s brainwaves are similar in different conditions (relax and play game). Nevertheless, according column two, the minimum distance gap received between the subject two reference and subject three test samples. Furthermore, according to the column three, the minimum distance gap received between the subject three reference and subject five test samples. According to columns four and five, the minimum distance gaps received respectively between subject four reference and subject five test samples, subject five reference and subject one test samples. According to the table 5.1, only subject one received the minimum distance gap between reference and test sample 34

9 [10] Jianjun Wang,Thomas Fuhlbrigge Biao Zhang, "A Review of the Commercial Brain-Computer Interface Technology from Perspective of Industrial Robotics," in International Conference on Automation and Logistics, Macau, 2010, pp [11] MohamedMoshrefi-Torbati, MartynHill, Catherine M.Hill, Paul R.White Shayan Motamedi-Fakhr, "Signal processing techniques applied to human sleep EEG signals A review," Biomedical Signal Processing and Control, vol. 10, no. University of Southampton UK, pp , January [12] Md. Wasiur Rahman, A. B. M. Aowlad Hossain Manjurul Ahsan Riheen, "Selection of Proper Frequency Band and Compatible Features for Left and Right Hand Movement from EEG Signal Analysis," in Computer and Information Technology, Khulna, 2014, pp [13] C. Castillo, C. J.Toledo J. Garcia, "Frequency-change analysis of nonlinear system using spectrum (Fast Fourier Transform) theory," in Electrical Engineering, Computing Science and Automatic Control,CCE,2009 6th International Conference on, Mexico City, 2009, pp [14] Steven Smith, Digital Signal Processing: A Practical Guide for Engineers and Scientists, 1st ed. Burlington, United States of America : Elsevier Science, Page 50 of 59 [15] I. Farkas, T. Ujbanyi, P. Dukan, A. Kovari J. Katona, "Evaluation of the NeuroSky MindFlex EEG headset brain waves data," in 12th International Symposium on Applied Machine Intelligence and Informatics, Herl'any, 2014, pp [16] D.Suganyadevi B.Sabarigiri, "Multi-Channel Electroencephalogram (EEG) Signal Acquisition and its Effective Channel selection with De-noising Using AWICA for Biometric System," International Journal of Engineering and Technology, vol. 6, no. 2, pp , April-May [17] Andrew Campbell et al., "NeuroPhone: Brain-Mobile Phone Interface using a Wireless EEG Headset," in second ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds, New York, 2010, pp

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