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Classification of Human Emotions Using Multiwavelet Transform Based Features and Random Forest Technique 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 experiment is conducted on EEG database for emotions i.e., DEAP data set. In present work,using Multithe data Wavelet 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 Mean, Standard deviation, Variance, 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 Interaction) 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 spatial analysis 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 frequency domain 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 time domain 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. 2 Vol 8 (28) | October 2015 | www.indjst.org Indian Journal of Science and Technology Swati Vaid, Preeti Singh and Chamandeep Kaur Table 1 describes comparative analysis of different studies female. The music videos used as stimuli were classified 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
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