A Brain Computer Interface System For Auto Piloting Wheelchair Reshmi G, N. Kumaravel & M. Sasikala Centre for Medical Electronics, Dept. of Electronics and Communication Engineering, College of Engineering, Guindy, Anna University, Chennai, India E-mail : er.reshmig@gmail.com, sasikala@annauniv.edu Abstract Brain Computer Interface (BCI) bypasses damaged neurons and provide a non-muscular communication channel for human and his environment. It measures, analyses and interprets the EEG signal and generate control signals. In this work Wavelet transform and Band Pass Filter are used for feature extraction of the relevant frequency bands from the EEG signals. Support Vector Machine is used for classifying the pre-defined movements. The classifier output used as control signal for wheelchair. Keywords: EEG signal; wavelet transform; SVM; BCI; wheelchair control; motor imagery. I. INTRODUCTION The human being is special among all living things because of its superior skills to other living kinds. Communication is one of these features which enables people to interact with each other and express themselves using speech and body language skills. However, this is not the case for all of the people. There are fatal diseases like Amyotrophic Lateral Sclerosis (ALS), brainstem stroke and multiple sclerosis, causing the condition termed as locked-in-syndrome which results in loss of ability in controlling the voluntary muscles. In this condition, the subject is conscious and the brain works properly, however the movement commands are not transmitted through the body limbs. In other words, the subject is aware of everything going around, but is not able to move any part of the body. Brain computer interface (BCI) is a developing technology based on the understanding and interpretation of the brain activity. It uses the fact that different intentions correspond to different patterns in the brain, even if they are not realized by the subject. BCI systems try to define some relationship between the brain activity and these intentions and then realize the intention without the contribution of the body. Being an alternative communication way, BCI systems produce a command for an external device from the acquired brain signals by signal processing and classification methods. The technology can be considered to be meaningful for severe motor disabled patients, since they are able to meet the main needs of the living with this technology. Many studies are ongoing with equipment s such as magneto encephalography (MEG), Functional magnetic resonance imaging (FMRI), and so on. However, MEG and FMRI do not fit interface apparatus because these are big and expensive. For this reason, generally, measurement of electrical activity recorded from electrodes placed on the scalp is used for investigation of BCI. EEG is the only non-invasive and cheap imaging technology for use in a real-time BCI system. EEG, on the other hand has better temporal resolution, is portable and cost effective. This method eliminates the conventional steering which requires the manual operation such as operating a switch, controls for moving in required direction. In this BCI system, movement intentions are classified according to the limb movements. The result of the classification can be used as a command for controlling wheelchair. Here the EEG module is used as the control to navigate the wheel chair in five directions. The user can stop the wheelchair voluntarily during movement, or let go forward, backward, move right and left. II. METHODS The subjects are seated in a chair with armrests rest, approximately 100 cm from a computer screen. 86
Electrodes are positioned according to the international 10-20 system. The block diagram is shown in Fig 1. Figure 1. Block Diagram A. Experimental Paradigm The training paradigm consisted of a repetition of audio visual cue based (synchronous) trials of five different motor imagery tasks. The Figure 2 shows the experimental set up. The experiment consists of several runs with 15 trials each after each. Each trial lasts 10 seconds. with a 21 channel RMS EEG machine. The EEG signal is sampled at the rate of 256 Hz. Monopolar montage is selected for recording EEG. Data is recorded from the 21 channels and for this work only three electrodes are used. Here ground and reference electrode is placed in forehead and right mastoid respectively. Channels selected is C3-REF, C4- REF, Cz- REF. III. FEATURE EXTRACTION Data is collected from 35 healthy subjects. The frequency range of EEG signal is mainly in the 0.3-40 Hz band. Higher frequency in EEG signal is seen as noise caused by muscle activity, blink of eyes and other noises. The time domain and frequency domain features are analyzed and compared with focus on using the results for creating a robust control scheme for wheel chair control. In this stage the EEG is analyzed using wavelet transform and band pass filter. A. Wavelet Decomposition EEG signal is decomposed into 6 levels using wavelet transform. Figure 3 shows the various EEG bands. Based on the frequencies first and second level does not have any significant information and from third, fourth and fifth level will get gamma, beta and alpha respectively. While sixth level there is one detail and one approximation band which is theta and delta respectively. Figure 2. Audio and Visual Cue The imagination movements of left hand, right hand, both hand and both leg is designated to control the direction of the wheelchair. During experiment, the subject sits in a comfortable armchair facing the computer screen. The duration of each trial is 7 seconds. During the first 2 seconds, while the screen is blank, the subject is in a relaxed state. After 2 second a cross will appear on the screen with a sound indication. Starting of 4th second a visual cue (arrow) pointing left, right, upwards or downwards appeared on the screen, indicating the imagination of the left hand or the right hand or both hands or both leg respectively. B. Data Referencing The motor imagery EEG signal occurs in electrodes overlying sensorimotor cortex. The acquisition is made Figure 3. Wavelet Decomposition For each task the EEG signal is decomposed using Daubechies10 wavelets and features are extracted. The features such as mean, standard deviation, power 87
spectral density, maximum value of coefficient in each sub band, and normalised power are extracted. Mean gives the average value of the signal, power spectral density specifies power at a particular frequency. IV. CLASSIFICATION Support Vector Machine (SVM) is primarily a classifier method that performs classification tasks by constructing hyper planes in a multidimensional space that separates cases of different class labels. The SVM must be trained, just as an Artificial Neural Network must be trained. It maps training data in the input space into a high dimensional feature space. It determines a linear decision boundary in the feature space by constructing the optimal separating hyper plane distinguishing the classes. This allows the SVM to achieve a nonlinear boundary in the input space. The support vectors are those points in the input space which best define the boundary between the classes. Potentially difficult computations in the feature space are avoided by using a kernel function, which allows computations to be performed in the input space. In this work polynomial kernel is used as kernel function. A total of 12 inputs are applied to the input node and output had 5 nodes. The extracted feature values are used for classification using the SVM. The classified output is given as the commands for navigating the wheelchair in four directions. V. RESULTS AND DISCUSSION Table 1 shows the normalised power calculated from each electrode for different task. It shows that variation of power in three electrodes with different motor imagery task. While considering the first task all the three electrodes have nearly same values; in the case of left there is a small dip in C4 while right has dip in C3. In the case of hand imagery C3, C4 are nearly same but lower than Cz and in leg imagery C3, C4 are nearly same but higher than Cz. ALPHA BAND POWER Table 1. Normalised Power TASK C3 C4 Cz RELAX 0.229359 0.226169 0.222026 LEFT 0.264571 0.24554 0.276331 RIGHT 0.27786 0.285771 0.286544 HAND 0.204989 0.203074 0.244663 LEG 0.186062 0.189044 0.172001 The Table2 gives the wavelet features for different tasks, it shows that in relaxed state all the three electrodes have higher values compared to all other tasks, in left hand imagery C4 have lower values compared to other two electrodes, in the case of right hand imagery C3 have lower values compared to C4 and Cz. While considering hand imagery C3 and C4 have lower values compared to Cz and in the case of leg imagery C3 and C4 have higher values compared to Cz. WAVELET FEATURES Table 2 Wavelet Features TASK CHANNEL MEAN STD MAX RELAX C3 20.838 34.447 186.17 RELAX C4 24.293 31.974 169.65 RELAX CZ 12.246 32.427 162.64 LEFT C3 12.078 17.864 63.03 LEFT C4 10.886 12.862 43.92 LEFT CZ 19.369 16.162 60.76 RIGHT C3 3.2932 16.617 55.76 RIGHT C4 30.944 16.982 79.54 RIGHT CZ 6.8133 18.809 71.28 HAND C3 0.5542 12.892 34.26 HAND C4 0.4681 12.346 34.62 HAND CZ 1.0103 14.045 35.02 LEG C3 37.823 32.666 127.05 LEG C4 26.253 40.005 119.34 LEG CZ 11.482 32.021 98.25 A. Power Spectrum Plot The squared FFT value of EEG signal is plotted against frequency and which will reflect the band power. The figure 5 shows the power spectrum plot of fft for different task. In figure 5 (a) shows the power spectrum plot in relaxed state, there the power of three electrodes are nearly same and high as compared to all other tasks, (b) shows the magnitude plot of the left hand imagery, here the power of C3, Cz are high compared to C4. In the case of left hand imagery right side of the brain is more active so that the power in C4 get reduced, but in 88
the case of right hand imagery left side is more active so that the power in C3 get reduced compared C4 and Cz and it is shown in (c). While considering the hand imagery power in the C3, C4 are nearly same and lower than that of the Cz it shown in (d). In the case of leg motor imagery power in the Cz is lower than that of the C3 and C4. The power spectrum plot of leg imagery is shown in the Figure 5 (e). d. Hand Imagery a. Relax b. Left Hand Imagery e. Leg Imagery Figure 5. Power Spectrum Plot B. Classification Classification accuracy of each task in same person is shown in Table 3. Table 3. Classification Accuracy(%) TASK RELAX 80 LEFT 85 RIGHT 80 HAND 72 LEG 85 SVM c. Right Hand Imagery 89
VI. CONCLUSION Detailed analysis are done through 35 healthy subjects and it is observed that motor imagery movement variations are mainly in MU(8-13 Hz) and Beta(13-26 Hz) bands. From the acquired EEG signal features such as mean, standard deviation, normalised power and power spectral density are calculated and classified using SVM. The future work is interfacing the classifier output with the microcontroller which runs the motors that moves the wheel chair in the defined direction. VII. REFERENCES 1) Damien Coyle, Jhonatan Garcia, Abdul R Satti and T Martin McGinnity, EEG-based Continuous Control of a Game using a 3 Channel Motor Imagery BCI IEEE transaction and UK Engineering and Physical Sciences Research Council (project no. EP/H012958/1), 2011. 2) G. Pfurtscheller, F.H. Lopes da Silva, Eventrelated EEG/MEG synchronization and desynchronization: basic principles, Clinical Neurophysiology,110, 1842-1857, 1999. 3) G.Pfurtscheller, Event-related synchronization (ERS):an electrophysiological correlate of cortical areas at rest, Electroencephalography and clinical Neurophysiology, 83, 62-69, 1992. 4) G. Pfurtscheller, Ch.Neuper, C.Andrew, G.Edlinger, Foot and hand area mu rhythms, International Journal of Psychophysiology 26, 121-135, 1997. 5) H.T. Wang and T. Li, Brain-actuated wheelchair based on mixture model brain computer interfaces Electronics Letters, 1st March 2012 Vol. 48 No. 5, 2012. 90