Discovery
ANALYSIS
The International Daily journal ISSN 2278 – 5469 EISSN 2278 – 5450 © 2015 Discovery Publication. All Rights Reserved
Mental Tasks Classification using EEG signal, Discrete Wavelet Transform and Neural Network Publication History Received: 02 September 2015 Accepted: 04 October 2015 Published: 06 November 2015
Page
38
Citation Padmanabh Lanke, Rajveer Shastri, Shashank Biradar. Mental Tasks Classification using EEG signal, Discrete Wavelet Transform and Neural Network. Discovery, 2015, 48(221), 38-41
Mental Tasks Classification using EEG signal, Discrete Wavelet Transform and Neural Network Padmanabh Lanke1 , Rajveer Shastri2, Shashank Biradar3 Department of Electronics VPCOE Baramati Savitribai Phule University of Pune India
[email protected]
I.
INTRODUCTION
Classification of mental tasks using Electroencephalogram (EEG) signal analysis is gaining much of interest amongst the researchers. Mental tasks classification using EEG signal analysis provides a new way of communication for paralyzed individuals who cannot communicate with external word because of nerve injuries. Though they cannot make any voluntary muscle actions, EEG signal for particular action is generated if they are able to think about that action. We can capture their EEG signal for that particular action and can help them to communicate. Brain Computer Interface System helps to converts thoughts into an actions. Main components of EEG based BCI are EEG signal acquisition, Feature extraction, Feature Selection and Classification. BCI connects brain to machine extracts features classifies tasks and takes action related to that task. By using BCI paralyzed individuals can control electronic devices by their thoughts only without involvement of any muscular activity [1]. EEG based BCI helps to improve the quality of life of paralyzed individual. EEG is the recording of the electrical activity of brain. Brain generates EEG signal which is related to particular mental activity. EEG signal undergoes change in amplitude as well as frequency for different mental activity.
In 1977 Neep Hazarika et.al [5] used wavelet transform and neural network for classification of EEG signal (Normal, Schizophrenia, and Obsessive Compulsive Disorder). Wavelet coefficients are used as features and feed forward neural network is used for classification. For classification of imagined left and right hand movement in 2008 Aihua Zhang et.al [6] used Power Spectral Entropy as a feature extraction technique and time variable linear classifier maximum classifier accuracy obtained was 92%. To classify happy and sad emotions J. Rendi et.al used wavelet coefficients for extracting the features Extreme Learning Machine and Support Vector Machine for classification and overall classifier accuracy obtained was 84.67% for classifying happy and sad emotions. In 2013 energy as a feature to classify five mental task and overall classifier accuracy obtained is 90.75% using MLPNN. To classify motor imaginary tasks in 2014 researches used DWT for feature extraction and features selected were energy, entropy, mean and variance, KNN with PCA is for classification and overall accuracy obtained is 70%. Rest of the paper arranged as section II discusses data collection. Section III discusses analysis of EEG signal using DWT. Section IV tells about feature extraction selection from an EEG signal. Section V is about design of neural network
39
Keywords— Brain Computer Interface, Feature extraction, DWT, MLPNN, PNN.
Numerous EEG feature extraction methods for classification of mental tasks have been reported [2] presents the classification for all five mental tasks together, as well as the task pair classification. There are multiple options when it comes to choosing suitable feature extraction methods and classifiers. F. Lotte et.al (2007) surveyed the most common classification algorithms used in BCI research. They also displayed classification results for papers dealing with EEG classification grouped by specific datasets (and BCI types) [3]. In modern signal processing techniques, Short Time Fourier Transform (STFT) and Discrete Wavelet Transform (DWT) are widely used for non-stationary signal analysis. However, the time-frequency resolution obtained from DWT extract more useful information from non-stationary signal compared to Fourier Transform or STFT [4].
Page
Abstract—This paper describes the classification of five mental tasks using Discrete wavelet transform (DWT) and an artificial neural network (ANN) technique together. In this paper we classified five mental tasks Baseline, Multiplication, Rotation, Counting and Letter composition. DWT is used for extracting the features from EEG signals which represents these five mental tasks. Daubechies wavelet of order 2 (db2) is used and EEG signal is decomposed up to level five. Features selected were Energy, Entropy, Mean and Standard deviation. These features were selected from decomposition level (D2-D5 and A5). These features were used to train the classifier. Two classifier were used multilayer perceptron neural network (MLPNN) and probabilistic neural network (PNN). Both classifier were compared. Overall accuracy of classifying five mental tasks of MLPNN classifier is 92% and of PNN classifier is 100%.
1. 2. 3. 4. 5.
Baseline task: Subjects were asked to relax. Multiplication: Subjects were asked to do multiplication of two numbers mentally. Letter Composition: Subjects were asked to compose letter mentally. Rotation: Subjects were asked to imagine the cube is rotating. Counting: Subjects were asked to count numbers mentally.
III. FEATURE EXTRACTION: EEG signal is non-stationary in nature therefore Fourier transform and Short Time Fourier Transform (STFT) are not useful for analyzing EEG signal [8]. Discrete Wavelet Transform (DWT) provides localization in time and frequency and suited for non-stationary signals. DWT provides good time resolution at high frequency and good frequency resolution at low frequency [9]. DWT is used for extracting the features from EEG signal which represents particular mental activity. DWT analyses the signal at different frequency with different resolution by decomposing the signal into approximation and detail information. DWT employs two sets of filters high pass and low pass filter. It follow Quadrature Mirror Filter (QMF) condition. High pass filter and low pass filter is followed by down-sampler provides detail coefficients and approximation coefficients respectively. The smoothening feature of db2 is more appropriate to detect changes in EEG signal. Hence wavelet coefficients were computed using db2 wavelet. EEG signal has five wave-groups theta (0-4Hz), delta (4-8Hz), alpha (8-13Hz), beta (13-30Hz) and gamma (above 30Hz). EEG signal does not contains any useful information above
IV. FEATURE SELECTION: The process explained in above section is the technique used for extracting the features from EEG signal i.e. DWT which uses db2 and 5 level of decomposition is used for extracting features from EEG signals. Features are used for discriminate each EEG signal of particular mental task from another EEG signal. Extracted wavelet coefficients itself represents the feature of that particular EEG signal which is related to a specific mental activity. To reduce dimensionality of extracted feature vectors, statistics over the set of wavelet coefficients were used [10]. The following features were used to represent the time frequency distribution of EEG signal. 1. 2. 3. 4.
Energy of wavelet coefficients in each sub-band. Entropy of wavelet coefficients in each sub-band. Mean of wavelet coefficients in each sub-band. Standard deviation of wavelet coefficients in each subband.
Therefore these features are selected from each sub-band at each decomposition level i.e. features are selected from D1D5 and A5. Every EEG signal which represents one of five mental activities is undergone the process explained above and features are extracted from them. V. CLASSIFICATION APPROACH: ANNs are considered to be good classifiers due to their inherent features such as adaptive learning, robustness and self-organization. ANNs are practically useful in situations where enough data are available for training and where simpler classification algorithm fails [11]. ANNs are good for pattern classification. For classification of non-linear data multilayer perceptron neural networks are preferred. MLPNN uses back-propagation algorithm. MLPNN uses supervised learning mechanism. MLP has input layer followed by hidden layer and output layer. Hidden layers are useful for nonlinear classification of data. Here no. of hidden layers selected are 2. Five output classes are defined corresponding to five mental tasks. Learning rate is selected as 0.9. Minimum square error is used as cost function. Extracted features are applied as input vector to MLPNN. MLP is trained using NNtool in Matlab. PNN is radial basis function network is a kind of important deformation. The network composed by input and output
40
II. DATA COLLECTION Data base used for this project is generated by Keirn and Aunon using standard 10-20% electrode system. The database can downloaded from http://www.cs.colostate.edu/anderson. This data contains EEG signal of 5 tasks namely Baseline, Multiplication, Letter Composition, Rotation and Counting. 5 tasks were done by 5 subjects mentally, without doing any muscular activity and 5 trials had been taken of each task. 5 subjects were seated in sound controlled room with dim lightning and noiseless fan for ventilation. Electrode cap was used to record EEG signal from c3, c4, p3, p4, o1, o2 and EOG. Electrodes were connected through a bank of Grass 7P511 amplifier and band-pass filter from 0.1-100 Hz. Data was recorded at 250Hz sample rate with lab master 12 bit ADC. Each EEG signal is 10 sec long and has 2500 samples. Description of tasks:
30Hz. EEG signal in Anderson’s data is sampled at 250Hz, therefore maximum frequency component present in EEG signal is 125Hz. 5 level decomposition of EEG signal will give us five frequency bands. In this work EEG signal is decomposed up to 5 level using db2 wavelet. Detail wavelet coefficients (D1-D5) and approximation (A5) coefficients are produced, these coefficients are nothing but features of EEG signals which are related five mental tasks.
Page
for classification of five mental tasks. Section VI is for result and discussion and section VII is for conclusion.
layer, radial grassroots layers. PNN is often used in pattern recognition. PNN is also simulated using Matlab. VI. RESULTS AND DISCUSSION Each EEG signal (which represents different metal task) is divided into 1 sec segment. 1 sec segment contains 250 samples of data. Each segment is decomposed up-to 5 level using db2. From each decomposition level features are selected i.e. from D1-D5 and A5 features are selected. 4 features (Energy, Entropy, Mean and standard deviation) from each decomposition level are detected. Therefore total 4*5=20 features are selected from one segment and 10*20=200 features are extracted from one EEG signal. Similarly 200 features are extracted from all EEG signals which are related to particular mental tasks. Data set is created which has 200 features of all subjects and tasks. This feature matrix is used as input to train the Neural Network. Designed neural network has 200 input neurons and 5 output neurons. Neural network performance is measured by using MSE. No. of neurons in first hidden layer are 20 and in second hidden layer are 15. Neural network is trained and simulated. Overall classifier accuracy obtained is 92% for MLPNN and 100% obtained for PNN.
[5] N. Hazarika, et.al, “Classification of EEG Signal Using the Wavelet Transform”, IEEE Proceedings on Digital Signal Processing,Vol. 1, Jul 1997, pp. 97-89. [6]A. Zhang, “Feature Extraction of EEG Signals Using Power Spectral Entropy”, IEEE International Conference on BioMedical Informatics, May 2008, Vol. 2 pp. [7] D. Upadhyay, “Classification of EEG Signals under Different Mental Tasks using Wavelet Transform and Neural Network with one step scecant algorithm”, IJSET, April 2013, Vol. 2, pp. 256-259. [8] E. Mohamed, et.al, “Enhancing EEG Signals in Brain Computer Interface Using Wavelet Transform”, IJIEE, Vol. 4, May 2014, pp. 234-238. [9] F. Vaneghi, “A Comparative Approach to EEG Feature Extraction Methods”, IEEE transaction on Neural System and Rehabilitation Engineering, Vol 16, 2012, pp. 252-256. [10] P. Jahankhani, et.al, “EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks”, IEEE Modern Computing, 2006, pp 120-124. [11]V. Khare, et.al, “Classification of EEG Signals based on Neural Network to Discriminate Five Mental States”, IEEE, Vol.1, 2009.
VII. CONCLUSION Overall classifier accuracy obtained is good i.e. 92%. This shows that features which are selected are strong enough to classify data correctly. PNN requires less training time than MLPNN and PNN can be used for online classification where MLPNN cannot be used for online classification of mental tasks..
References [1] T. Vaughan, “Guest Editorial Brain-Computer Interface Technology: A Review of the Second International Meeting”, IEEE Transactions on Neural System and Rehabilitation Engineering, Vol.11, June 2003, pp. 94-109. [2] P. Lanke, et.al, “EEG Signal Analysis for Mental Tasks Detection”, IJACET, Vol.2, Feb 2014, pp. 66-74.
Page
[4] J. Rendi, et.al, “Discrete Wavelet Transform Coefficients for Emotion Recognition from EEG Signals”, IEEE Engineering in Medicine and Biology Society, Sept 2012, pp. 2251-2254
41
[3] F. Lotte, et.al, “Study of Electroencephalographic Signal Processing and Classification Techniques Towards the use of Brain Computer Interfaces in Virtual Reality Applications”, Jan 2009 .
