Mohd Iqbal Omar

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease.

In addition to classic motor signs and symptoms, individuals with Parkinsons disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patients emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

On the analysis of EEG power, frequency and asymmetry in Parkinson’s disease during emotion processing

ObjectiveWhile Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing.MethodEEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli.ResultsBehaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli.ConclusionThese distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients.

Inter-hemispheric EEG coherence analysis in Parkinson's disease: Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities.

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinsons disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

Development of cost effective ECG data acquisition system for clinical applications using LabVIEW

The main objective of this work is to develop a portable and cost effective data acquisition (DAQ) system for clinical applications. This DAQ consists of several modules such as power supply, analog to digital converter (ADC), amplifiers, isolators, filters and interfacing circuits. The complete data acquisition circuit has been developed using This system mainly aims to collect the ECG signals of frequency between 0.05 Hz and 113 Hz with a gain of 3113. This frequency information from the ECG signal is highly useful clinical applications such as SCA prediction, cardiovascular disease (CVD) detection, etc. ECG signals will be collected from the subjects using 3 leads system and given to DAQ for recording the ECG signal. The acquired signal through this DAQ will then be transferred to the Notebook through NI6008 data acquisition card. This DAQ interface is used to convert the input analog signal to digital signal output and to save the ECG data in the notebook using Labview software. This acquired signal from Labview software is used for further clinical investigation. We also developed a Graphical User Interface (GUI) in LabVIEW software to continuously monitor the ECG signal traces and to record the ECG data with higher precision. The morphology of the acquired ECG signal in the system is highly precise and useful for clinical diagnosis. Furthermore, this proposed system is used for developing sudden cardiac arrest (SCA) prediction in our university.

Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity

Abstract Objective Non-motor symptoms in Parkinsons disease (PD) involving cognition and emotion have been progressively receiving more attention in recent times. Electroencephalogram (EEG) signals, being an activity of central nervous system, can reflect the underlying true emotional state of a person. This paper presents a computational framework for classifying PD patients compared to healthy controls (HC) using emotional information from the brains electrical activity. Approach Emotional EEG data were obtained from 20 PD patients and 20 healthy age-, gender- and education level-matched controls by inducing the six basic emotions of happiness, sadness, fear, anger, surprise and disgust using multimodal (audio and visual) stimuli. In addition, participants were asked to report their subjective affect. Because of the nonlinear and dynamic nature of EEG signals, we utilized higher order spectral features (specifically, bispectrum) for analysis. Two different classifiers namely K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used to investigate the performance of the HOS based features to classify each of the six emotional states of PD patients compared to HC. Ten-fold cross-validation method was used for testing the reliability of the classifier results. Main results From the experimental results with our EEG data set, we found that (a) classification performance of bispectrum features across ALL frequency bands is better than individual frequency bands in both the groups using SVM classifier; (b) higher frequency band plays a more important role in emotion activities than lower frequency band; and (c) PD patients showed emotional impairments compared to HC, as demonstrated by a lower classification performance, particularly for negative emotions (sadness, fear, anger and disgust). Significance These results demonstrate the effectiveness of applying EEG features with machine learning techniques to classify the each emotional state difference of PD patients compared to HC, and offer a promising approach for detection of emotional impairments associated with other neurological disorders.

Emotion processing in Parkinson's disease: an EEG spectral power study.

Objective: Although an emotional deficit is a common finding in Parkinsons disease (PD), its neurobiological mechanism on emotion recognition is still unknown. This study examined the emotion processing deficits in PD patients using electroencephalogram (EEG) signals in response to multimodal stimuli. Method: EEG signals were investigated on both positive and negative emotions in 14 PD patients and 14 aged-matched normal controls (NCs). The relative power (i.e., ratio of EEG signal power in each frequency band compared to the total EEG power) was computed over three brain regions: the anterior (AF3, F7, F3, F4, F8 and AF4), central (FC5 and FC6) and posterior (T7, P7, O1, O2, P8 and T8) regions for theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–60 Hz) frequency sub-bands, respectively. Results: Behaviorally, PD patients showed decreased performance in classifying emotional stimuli as measured by subjective ratings. EEG power at theta, alpha, beta, and gamma bands in all regions were significantly different between the NC and PD groups during both the emotional tasks, with p-values less than 0.05. Furthermore, an increase of relative spectral powers in the theta and gamma bands and a decrease of relative powers in the alpha and beta bands were observed for PD patients compared with NCs during emotional information processing. Conclusion: The results suggest the possibility of the existence of a distinctive neurobiological substrate of PD patients during emotional information processing. Also, these distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients.

Classification of muscle fatigue condition using multi-sensors

The aim of this work is to assess the muscle fatigue condition using multimodal system. Muscle fatigue is a common muscle condition which experiences in our daily activity. There were 20 subjects participated in this study. Electromyogram (EMG) (shows the electrical activity of the muscle), Mechanomyogram (MMG) (shows a mechanical activity of the muscle) and Acoustic myogram (AMG) (is audible produced when the muscle was contracted) were used in this study. EMG, MMG and AMG were recorded continuously from hamstring muscle, according to the data acquisition protocol. The recorded signals were segmented into fatigue and non-fatigue. Time domain, frequency domain and time-frequency domain features were extracted from the myograms. The extracted features were classified using k-nearest neighbor. The mean accuracy of EMG, MMG and AMG was 87.10%, 81.40% and 67.23% respectively. The mean accuracy of the multimodal system was 92.07%. In this paper, we also have discussed the effect of single myogram and multi modal myograms.

Estimation of BMI status via speech signals using short-term cepstral features

Fatness is a serious health problem worldwide because of the danger factors associated with diseases which cause permanent psychological effect. To classify normal weight, overweight, underweight and obesity, body mass index (BMI) is the most recognized and extensively used measurement. BMI measurement has its limits in some cases like overstatements in athletes, and underestimates in elderly. Thus, the paper reports the estimation of BMI (body mass index) status via speech signals using the short-term cesptral speech feature extraction methods, Mel-frequency cepstral coefficients (MFCCs) and its deltas (Delta and Delta-Delta) and Linear Prediction Coding (LPC) based Cepstral parameters (LPCs, linear prediction cepstral coefficients -LPCCs and weighted LPCCs). Two different classifiers, probabilistic neural network (PNN) and k-nearest neighbor (KNN) were used for the classification of the three BMI statuses (normal, overweight and obese). The 10-fold cross validation method was used to validate the reliability of the classifier results. PNN gives the best average BMI status classification accuracy of 87% using the short-term cepstral features.

Assessment muscle fatigue using statistical study and classification: A review

Muscle fatigue is very common muscle condition has been experienced. Different types of myograms have been used to assess muscle fatigue and there are Electromyogram, Mechanomyogram and Acoustic myogram. Each myogram has its own advantages in assessing muscle fatigue. Therefore, suitable features are needed to assess muscle fatigue. This review discusses the on statistical analysis and classification result of the myograms features that have been applied. Towards the end of the paper, challenges and future trends are discussed.