ISSN (Print) : 0974-6846 Indian Journal of Science and Technology, Vol 8(28), DOI: 10.17485/ijst/2015/v8i28/70797, October 2015 ISSN (Online) : 0974-5645 Classification of Human Emotions using Multiwavelet Transform based Features and Random Forest Technique

Swati Vaid*, Preeti Singh and Chamandeep Kaur Department of Electronics and Communication Engineering, UIET, Panjab University, Chandigarh - 160014, Punjab, India; [email protected], [email protected], [email protected]

Abstract Objective: To extract statistical features from EEG signal for human emotion recognition. Methods: Emotions, the “inner” state of a person play a vital role in analysing the state of mind. In this paper, a new method for human emotion recognition

EEG signals has been proposed. The is conducted on EEG database for emotions i.e., DEAP data set. In present work,using Multithe data set used Transform contains (MWT) EEG recording and random from forest 4 participants (ensemble (S01, technique) S02, S03, for S04) classification from 15 channel of human (FP1, emotions FP2, F3, from P3, F4, T7, T8, P4, O1, PZ, PO3, O2, P7, CP2, and C4) having 40 trails each. The paper explore the capability of proposed features namely , , , Shannon entropy, Hjorth parameters and Band power. Findings: The feature set obtained using Multi-Wavelet are then used as input for MLP, KNN, MC-SVM with Puk kernel function and Random

emotionsForest (ensemble) (happy, sad, classifier exciting, for hate)the classification from EEG signals. of emotions. Application: The classification accuracy for different emotion state happy- nitive99.8%, engagement sad 98.3%, ofexciting patients 95%, with hate impaired 96.4% motor are obtained functions; by recognitionthe proposed of emotions;method. Overall investigating 98.1% sleepfor classification disorders and of The findings are helpful in monitoring alertness, cog of features which include the temporal dynamics of brain signals in the human cognitive system can be considered. physiology. In future, efficient feature extraction algorithm using different multiwavelet functions and with a different set Keywords: Band Power, Hjorth Parameters, Human Computer Interface (HCI), Multi-Wavelet Transform (MWT), Random Forest, Shannon Entropy, Statistical Parameters

1. Introduction ) also known as BCI (Brain-Computer Interface) based analysis and classification of emotions. Emotions, the “inner” state of a person play a vital role Different emotional states can be detected by individual, in analysing the state of mind. Emotion recognition from age, gender, mental state, background and ethnicity2. Most EEG (Electroencephalogram) signals (brain signals) is a of the activation of emotion arises in right hemisphere but subject of interest for both psychologists as well as engi- left hemisphere also plays a vital role in activation of emo- neers. The diagnosis of neurological disorders has been tions. Apparently, brain might be partly or entirely engaged suggested based on automatic emotion recognition sys- to emotional processing during emotions like sadness, tem using various signals like Electromyogram (EMG), anger, happiness, disgust and fear. Thus, the results support Electrocardiogram (ECG) and facial images. Emotions the hypothesis that there are no exclusive emotion centres expressed via facial expressions and speech is commonly in the brain. But the results also indicate that the several used techniques for classification of human emotions1. brain areas are activated during emotion processing in a EEG signals play an important role in detecting the emo- well-defined and specific dynamic process3. Monitoring tional states for developing the HCI (Human-Computer of brain signals can be done by various medical methods

*Author for correspondence Classification of Human Emotions using Multiwavelet Transform based Features and Random Forest Technique

like Electroencephalography (EEG), functional Magnetic Several methods have been suggested in literature to Resonance Imaging (fMRI), Magnetic Resonance Imaging ­diagnose the hidden dynamical features and abrupt (MRI), Single Photo Emission Computed Tomography changes that can take place. (SPECT), NIRS (Near-Infrared Spectroscopy) and EROS The interpretation of the signal implies three impor- (Event-Related Optical Signal). tant aspects. The spectral analysis of the signal determines In recent years, HCI has evolved as a blessing for the dominant frequencies in the EEG. The temporal anal- patients with impaired motor functions4,5. HCI acts as a ysis of the EEG keeps a record of normal and abnormal communication channel between human brain and out- wave shapes in the signal and also presence and absence side world like computer system. It allows its users to of these rhythms. The estimates the distri- control external devices which are independent of periph- bution of these rhythms over the different brain regions6. eral nerves and muscles with brain activities. The channel These interpretations have been on the basis of time can be considered as the only way through which people and analysis. Event-Related Potentials affected by motor disabilities can communicate their (ERPs) and SCP (Slow Cortical Potential) components thoughts. The aim of HCI is to interpret brain activity into have reflected emotional states in the time-domain analy- digital form which acts as a command for a computer. sis7. The ERP components varying from short to middle HCI system can broadly be divided into four phase. latencies have shown a correlation with valence, includ- These are Signal Acquisition, Signal Pre-Processing ing unpleasant (sad and stressed) and pleasant (happy (monitoring and enhancing acquired signals), Feature and elated), whereas with the ERP components varying Extraction & Selection and Classification. Figure 1 shows from middle to long latencies have shown a correlation a general model of HCI system with different phases. with arousal including inactive (uninterested, bored) and Signal acquisition is the foremost step in BCI. It active (alert, excited)8. In frequency-domain, the spectral includes measuring brain activity effectively for efficient power of different frequency bands corresponds to differ- communication. The electrical signals which are -gener ent emotional states. ated by human intentions are measured using different At present, feature extraction techniques/methods for electrodes, placing on the scalp. Then these signals are non-stationary EEG signals include: (1) FFT (Fast Fourier digitized. Next phase is signal pre-processing. The purpose Transform), (2) ARM (Auto Regression Method), (3) DWT of this phase is to enhance signal quality without any lose (Discrete Wavelet Transform), (4) WPD (Wavelet Packet of information. Further processing is done to detect and Decomposition), (5) MWT (Multi-Wavelet Transform). de-noise data so as to enhance the embedded signal infor- In9, the features based on Fourier spectral were computed mation. Aim of feature extraction is to describe the signal using Welch method. The main demerit of the method “features” which represent a unique property. Features can was that it uses only frequency domain information, the be linear as well non-linear. The extracted and selected information is not considered. According features are assigned a class through classification. The to the researchers time domain information combined class helps in identification of mental states of a patient, with the frequency domain feature can lead to more whether in a happy, melancholy, calm or surprise state. accuracy of the classifier. Burke et al.5 explored paramet- One of the key challenges in current BCI research ric model Autoregressive (AR) model with an exogenous is how to extract features of random time-varying EEG input signal to ARX model. The classifier used was Linear signals and classify the signals as accurately as ­possible4. Discriminant Analysis (LDA). The exogenous input was made of the Bereitschafts potential (an event related poten- tial prior to the movement). The classification accuracy for AR and ARX method was observed to be 52.8 4.8% and 79.1 3.9% across the subjects. EEG measurement was done at channels C3 and C4. A new method for updating feature extractors using adaptive common spatial patterns (ACSP) has been proposed in10. Support Vector Machine (SVM) classifier and 15 electrodes (F3, Fz, F4, Fc1, Fc2, Pz, P3, P4, PO3, PO4, C3, Cz, C4, Cp1, Cp2,) covering the Figure 1. Model of a HCI system. cortical region were used for signal analysis.

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Table 1 describes comparative analysis of different­studies female. The music videos used as stimuli were­classified into for analysis of EEG signals for emotion recognition. different emotions utilizing the affective tags as per their record. In addition to that, the emotional content of each 2. Methodology video has been assessed by around 14–16 volunteers8. When the DEAP dataset was created, a band pass filter In this work, data was collected from dataset prepared by from 4-45 Hz was applied. Eye blinking data made avail- Queen Mary University of London. This dataset commonly able separately was utilized to eliminate the EOG artifacts known as the DEAP dataset consists of 32 participants from the signals. Since the 50 Hz got removed due to the while watching 40 different kinds of music videos. frequency cutoff kept at 45 Hz, separate notch filter at 50 The data in this dataset was acquired with a 32 channel Hz was not required during the signal conditioning stage. BioSemi acquisition system. EEG signals of the participants The delta band was not included in the were acquired using Biosemi Active Two system at a sam- because it has been proved that the low frequency band is pling rate of 512 Hz, which was down sampled to 128 Hz not necessary for the emotion prediction task11. before processing. All the participants were healthy, aged The EEG data stream was collected using 32 Ag/ between 19 and 32 and also half of the total participants were AgCl electrodes arranged in accordance to the 10–20

Table 1. Studies on emotion recognition from EEG signal Author Method Electrode Selected Features Classes Classifier Remarks Bajaj et al.1 MWT Fp1/Fp2 and F3/F4 Ratio of the norms, 4 (MC-LS- 84.79% Shannon and normalized SVM) Morlet Renyi entropy wavelet kernel function Jenke et al.12 DWT P9, P5, P7, P1, F7, FC2, Statical parameters, 6 Naive Bayes 36.5% CP2, CP1, F5 C2, Cz, Hjorth, CP5, CP3, and P3. Non-Stationarity Index, Prominent electrodes: Fractal Dimension, C2, Cz, P9, F7, CP5, Higher Order Crossing, and CP3 STFT power, Higher Order Spectra, MSC Estimate, HHS power, DWT bior3.3, DWT db4, diff. Asymmetry, ratio. Asymmetry WPD FP1, AF3, F7, P7, P3, Shannon Entropy, 4 MC-SVM 94.097% Vijayan et al.13 Pz, PO3, O1, CP2, C4, Cross-correlation, Auto- T8 and FC6 regressive modeling Mehmood Using EEGLAB, Fp1, Fp2, F3, F4, Fz, Hjorth Parameters 5-2 SVM For 5(30%), et al.14 spectral analysis F7, F8, C3, C4, Cz, T7, 2(70%) T8, P3, P4, P7, P8, O1, and O2 AlZoubi PSD F3-F4, C3-C4, Fz-PO, PSD 10 KNN (K=3) 66.74% et al.15 Cz-PO, F3-Cz, Fz-C3, Nasehi et al.16 DWT FP1, FP2, F3, and F4 Gabor-based features 6 PNN 64.78% Petrantonakis hybrid(adaptive Fp1, Fp2, and a bipolar HOC-Based Features 6 SVM 83.33 et al.17 filtering(EMD&GA) channel of F3 and F4 Murugappan DWT(db4) 64 electrode System, all Energy, RMS, REE, 5 KNN, LDA 83.26% using et al.18 channels, 24 channels, LREE, ALREE, power KNN, 75.21% 8channels using LDA MWT: Multi Wavelet Transform DWT: Discrete Wavelet Transform WPD: Wavelet Packet Decomposition MC-LS-SVM: Multi Class Least Square Support Vector Machine KNN: k-NearestNeighbour LDA: Linear Discriminant Analysis PNN: Probabilistic Neural Network

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­international system. In our work, 15 EEG channels ΨΦ(tH) = ∑ kt (2 − k) (2) namely FP1, FP2, F3, P3, F4, T7, T8, P4, O1, PZ, PO3, O2, k P7, CP2 and C4 were used for conducting our studies. Here, the coefficients G[k] and H[k] are matrices. G[k] and H[k] represent low-pass and high-pass filters for 2.1 Multi-Wavelet Transform (MWT) multiwavelet filter bank. The multiplicity r is generally 2 Multi-, which are the wavelets having various for most of the multiwavelets. In this study, Geronimo, scaling functions, are the recently introduced and pre- Hardin and Massopust (GHM) is considered for multiple ferred ones over single wavelet/scalar wavelet in the scaling functions and multiwavelets. The GHM dilation areas like signal/image classification, compression and and translation equations for this system have following de-noising of non-stationary signal20–22. In Multiwavelets, four coefficients: multiple scaling and wavelet functions are used rather  3 42  3  than single functions as in case of single wavelets. It yields   0  5  the property of having more degree of freedom for gener- GG=  5 5 ,,=   01 −−1 3  9 ating multiwavelets. Therefore, simultaneous gathering of  1     properties like vanishing moments (higher order), sym- 10 2 10  10 2  metry, orthogonality and compact supporting is possible  0 0   00 as opposed to case of scalar wavelets. There are two forms =   =   G2  9 −3, G3  −1  (3) of Multiwavelets: 1) Orthogonal type like Geronimo- 0 10 2 10  10 2  Hardin-Massopust (GHM), Symmetric Asymmetric (SA4) and Chui-Lian (CL); and 2) Bi-Orthogonal type such as  −−1 3   9  Bi-Orthogonal Hermite (Bih52S). The multiwavelets have    −1 10 2 10  10 2 some unique characteristics that cannot be obtained with HH01= , =  , 21  1 32  −9  scalar wavelets . It motivates us to use multiwavelet trans-    0  form of EEG signals for classification of human emotions.  10 10   10   9 −3  In the present work 3-level MWT has been employed to  −1    0 carry the experiment. Multiwavelets are also based upon 10 2 10    HH23= , = 10 2 (4) Multi Resolution Analyses (MRA), like wavelets. It gives  9 −32      −10 sixteen sub bands after one level of decomposition as  10 10  compared to four sub bands in Wavelet decomposition20. The standard Multi-Resolution Analysis (MRA) for scalar The GHM scaling functions have short support of wavelet uses single scaling function denoted as φ(t) and [0, 1] and [0, 2]. The scaling functions are symmetric and single wavelet ψ(t). The integer translates and dilates of the the exhibit second order approximation. scaling function are represented as φ(t−k) and φ(2jt − k) respectively. The multiwavelet is the extension of scalar 2.2 Measured Features wavelet where multiple scaling functions and associated The features namely, Mean, Variance, Standard deviation, multiple wavelets are used. In case of multiwavelet, a basis Shannon entropy measure, Hjorth parameter and Band- for the subspace Vo is formed by translating r scaling power have been measured from sub-signals obtained

functions denoted by φ1(t − k), φ2(t − k), ..., φr(t − k). The from the multiwavelet decomposition of EEG signals. multiwavelet can be considered as vector-valued wavelets These features are briefly described as follows: which satisfy the condition of two-scale relationship with involvement of matrices rather than scalars. The scaling 2.2.1 Mean

vector-valued function is represented as Φ(t) = [φ1(t), Mean represents the center of a set of value. The Mean is T φ2(t), ..., φr(t)] , T representing transpose and associated calculated for each and every sub-band signals. r-wavelets Ψ(t) = [ψ1(t), ψ2(t), ..., ψr(t)] satisfies the follow- +∞ 1 1 2 ing matrix dilation and matrix wavelet equations : µ= ∑ xt( ) T −∞ ΦΦ()tG= ∑ kt (2 − k) (1) k where, x(t) is signal and T is number of time-samples.

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2.2.2 Standard Deviation (SD) for the duration of a trial. The frequency band ranges of It is a simple measurement of the variability of a data set EEG signals are varying slightly between studies. In our which is calculated as root-mean-square (RMS) deviation studies it is: theta (θ) (4–8 Hz), alpha (α) (8–12 Hz), beta of values from the mean19. (β) (13–30 Hz), and gamma (γ) (30–45 Hz).

T 1 2 σ= xt( ) − m 2.3 Classification T ∑() t =1 In the present work, classification of the signals is ­carried through Random Forest, a very efficient algorithm in 2.2.3 Variance ensemble learning. Ensemble learning is the method Variance is measurement of the dispersion of a data points which has been recently developed, using multiple learning around their mean value. The variance can be expressed as models to increase predictive performance and accuracy. Ensemble classifiers combine a number of classifiers or ((xt))− µ 2 σ2 = ∑ learners to improve the classification results. As the num- T ber of classes in a multi-class problem rise, the number of training sets (of high dimensionality) becomes compara- 2.2.4 Shannon Entropy Measure tively smaller. The classifiers which are made trained on The Shannon entropy is a measure of uncertainty of the small training sets become biased, the variance is large signal. It can be defined as: due to the insufficient estimation of the related param- eters thus, such classifiers are termed as ‘weak’ classifiers. T =−   Ensemble classifier builds many such weak classifiers, E ∑ppkklog   t =1 known as base learners and combines the results of these classifiers to yield an outcome23–25. Some commonly used 2.2.5 Hjorth Parameters ensemble techniques/methods are Bagging (Bootstrap Hjorth Parameters are based on statistical calculations aggregating), Boosting (AdaBoost), Random Subspaces which used to characterize the characteristics of EEG (RanSub) and Random Forest. signal in the time domain. These are also known as Ensemble techniques are also suited for the Normalized Slope Descriptors (NSDs) include activity, classification of EEG signals for the following two reasons. mobility and complexity12. (i) The dimensionality of the EEG is often high and one of the pre-requisites of BCI is to train the classifier as fast T 2 xt− m as possible, thus, the training set also must be small. (ii) ∑ = ()( ) (i) Activity(A) = t 1 EEG is a time-varying signal, and thus, it becomes haz- T ardous to employ a single trained classifier to recognize VarX()(t) the classes of the unknown (incoming) features. (ii) Mobility(M)= , where X(t) is the time Varx()(t) 2.3.1 Random Forest derivative of x(t) Random Forest (RF) developed by Leo Breiman has been MX()(t) proved to be a powerful approach with excellent perfor- (iii) Complexity(C)= mance in classification tasks26. Introducing both bagging Mx()(t) and random variable selection for tree building, RF uti- lizes an ensemble of classification trees, which are built on Since activity is just the square of standard deviation, the bootstrap sample technique of the data. At each split, we omit this feature in the present work. variable candidate set is randomly selected from the whole variable set. Randomness is inserted by growing each tree 2.2.6 Band Power on different random subsamples and determining splitter The most popular features in the context of emotion partly at random. Each tree is grown fully to obtain a low- ­recognition from EEG are power features from different bias. Both bagging and random variable selection assure frequency bands. This assumes stationarity of the signal the low correlation for individual trees.

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3. Results and Discussion 4. Conclusion and Future Scope

Offline classification of four major emotions (Happy, Sad, Due to natural limitations like time dependency, large Exciting and Hate) was achieved using DEAP dataset. In dimensions of feature vector set, doubtfulness; challenge present work, the data set used contains EEG recording for the engineers is to make fast and correct decisions from 4 participants (S01, S02, S03, S04) from 15 chan- for recognition of emotions from EEG signal. In the nel (FP1, FP2, F3, P3, F4, T7, T8, P4, O1, PZ, PO3, O2, present work, we explore the capability of proposed P7, CP2 and C4) having 40 trails each. For classifying features (Mean, Standard deviation, Variance, Shannon the features, Random Forest algorithm is considered in entropy, Hjorth parameters and Band power) derived the present work. Also, the proposed algorithm is com- from MWT, an approach for classification of human pared with the performance of a basic algorithm i.e. MLP emotions from EEG signals. The data set was collected classifier, KNN classifier with k = 2 with Euclidian dis- from baseline datasets i.e. DEAP data set. Firstly, the tance measure, and MC-SVM with Puk Kernel. EEG signals were decomposed into several sub-signals The classification accuracy (%) obtained from differ- through MWT (3-level) using repeated-row pre-pro- ent classifiers for emotion recognition from EEG signals cessing. In addition, the multiwavelet decomposition is tabulated in Table 2. contains two or more scaling and wavelet functions, the From the Table 2, the classification accuracy (%) for filters i.e., low-pass and high-pass are matrices instead MLP, KNN (k=2), MC-SVM with Puk kernel function of scalars. The feature vector set obtained are then and Random Forest for emotion classification with GHM used as input for MLP, KNN, MC-SVM with Puk ker- multi-wavelet. The classification accuracy for different nel function and Random Forest (ensemble) classifier emotion state happy 99.8%, sad 98.3%, exciting 95%, hate for the classification. The experimental results indicate 96.4% are obtained by the proposed method. It has been that Random Forest has provided classification accu- observed that the classification accuracy for the happy racy of 98.1% for classification of emotions from EEG state is greater than the other states. The confusion matrix signals. The EEG signal processing based methodology of Random Forest is shown in Table 3. for emotion classification may be improved further. In future, efficient feature extraction algorithm using dif- ferent multiwavelet functions and with a different set of Table 2. Classification accuracy (%) with different features which include the temporal dynamics of brain classifiers for emotion recognition signals in the human cognitive system can be consid- METHOD HAPPY SAD EXCITING HATE AVERAGE ered. Research can further be carried on development (%) of a unified algorithm which incorporates various bio- MLP 89.5 25.7 4.0 7.2 46.3 logical signals such as Electroencephalogram (EEG), KNN 100.0 64.3 38.6 28.3 69.6 Electrooculogram (EOG) and Electromyogram (EMG), MC-SVM 93.6 26.8 12.4 12.5 50.5 so as to implement more natural human computer inter- Puk Kernel face systems. Random 99.8 98.3 95.0 96.4 98.1 Forest (Proposed 5. References work) 1. Bajaj V, Pachori RB. Detection of human emotions using features based on the multiwavelet transform of EEG sig- Table 3. The confusion matrix for Random Forest nals. Brain-Computer Interfaces. 2015; 74:215–40. 2. Subha DP, Joseph PK, Acharya RU, Lim CM. EEG sig- HAPPY SAD EXCITING HATE nal analysis: A survey. Journal of Medical. 2010 Apr; 34 HAPPY 99.8 0 .2 0 (2):195–212. SAD 1.5 98.3 .2 0 3. Davidson RJ. Anterior cerebral asymmetry and the EXCITING 4.04 .72 95 .24 nature of emotion. Brain and Cognition. 1992 Sep; 20(1):125–51. HATE 2.87 .30 .55 96.38 4. Sun S, Zhou J. A review of adaptive feature extraction and Accuracy (%) 99.8 98.3 95 96.38 classification methods for EEG-based brain-computer

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