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Audio-visual stimulation and relaxation. Linear and nonlinear EEG measures
... For example, Discrete Wavelet Transform (DWT) was used to extract features from EEG signals before feeding to k-NN to classify human emotion in term of disgust, happy, surprise, fear and natural with classification accuracy of 83.26% [12]. Teplan used slope of EEG linear regression to be a feature to determine the relaxation level of an individual [13][14]. Sulaiman et al. [15] used a combination of EEG Asymmetry and Spectral Centroids techniques as a feature to detect unique pattern of human stress. ...
... The default neighborhood setting is "Euclidean" and "Nearest". In this study, only k-NN distance of "Euclidean", "Cityblock" and "Cosine" along with all types of rule are used to find the object similarity in the k neighborhood as shown in equation (11), (12) and (13). (11) Equation (11) defines the formula for "Euclidean" distance. ...
... Here, X ik or X jk is either testing or training data where i and k is the index of the data. Here, the "Cityblock" distance is the summation of the difference between the data and then the results are assigned to the class that come out most frequently in the neighborhood of k. (13) Equation (13) defines the formula for "Cosine" distance. Here, X ik or X jk is either testing or training data where i and j is the index of the data. ...
This paper introduces new methods to extract stress features from electroencephalogram (EEG) signals during two cognitive states; Closed-Eyes (CE) and Open-Eyes (OE) using Relative Energy Ratio (RER), Shannon Entropy (SE) and Spectral Centroids (SC). The group with the stress features was identified and classified using k-Nearest Neighbor (k-NN). The RER in term of Energy Spectral Density (ESD) for each frequency band (delta, theta, alpha and beta) in four different groups consisted of 180 EEG data were calculated and analyzed. Then, the SE was used to confirm the pattern of stress features. Meanwhile, SC was applied to the RER of each group and then the results were selected as input features to k-Nearest Neighbor (k-NN) for the classification purposes. The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The proposed method showed promising results where the combination of RER, SE and SC techniques with the training and testing of k-NN set at 70:30 able to detect and classify the group with the unique stress features at 88.89% accuracy.
... 33-34 39 The Finite Impulse Response (FIR) band-pass filter was designed to filter frequency of 0.5 Hz to 30 Hz from EEG raw signals. These frequencies include the four EEG sub-bands which are Delta band, (0.5-4 Hz), Theta band, (4-8 Hz), Alpha band, (8)(9)(10)(11)(12)(13) and Beta, band (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The cerebral activities associated to frequency bands are shown in Table III. ...
Stress is one of the major health issues where too much stress may lead to depression, fatigue and insomnia. Various methods have been introduced by researchers to detect and analyze stress level using human physiological signals but yet to come out with a reliable indicator which able
to indicate the stress level of healthy human from their brain electrical activity; Electroencephalogram (EEG) signals. This study proposes stress index as an indicator of stress level using EEG signals. The study employs nonparametric method to extract stress features from EEG signals after
performing two tasks; do nothing and answer Intelligence Quotient (IQ) test questions. The k-Nearest Neighbor (k-NN) classifier is used to identify the stressed group using the extracted stress features. The results of the study established 3 type of indexes which represent the
stress levels (Low Stress, Moderate Stress, High Stress) with 88.89% overall classification accuracy, 86.67% classification sensitivity and 100% classification specificity. The 10-fold and leave-one-out cross validation of the classifier produced classification accuracy of 78.89% and 83.50%
respectively.
... Meanwhile, Shih and Fang [13] had produced Psychological Stress Index (PSI) using Heart Rate Interval and Plethysmography to assess human mental condition. The relaxation level of an individual could be determined by the slope of EEG linear regression in timebased measurement [14][15]. Handri et al. [1] produced two level of stress (high and low) from physiological signals such as EEG, ECG and Skin Temperature. ...
This paper presents a non-parametric method to produce stress index using Electroencephalogram (EEG) signals. 180 EEG datasets from healthy subjects were evaluated at two cognitive states; resting state (Eyes Closed) and working state (Eyes Open). In working cognitive state, subjects were asked to answer the Intelligence Quotient (IQ) test questions. The EEG datasets were categorized into 4 groups. Energy Spectral Density (ESD) ratios and Spectral Centroids (SC) from the two tasks were calculated and selected as input features to k-Nearest Neighbor (k-NN) classifier. Shannon's Entropy (SE) was used to detect and quantify the distribution of ESD due to stressors (stress factors). The stress indexes were assigned based on the results of classification, ESD ratios, SC and SE. There were 3 types of stress indexes can be assigned which represent the stress level (low stress, moderate stress and high stress) at classification accuracy of 88.89%. The regression coefficient of the SC of Beta and Alpha was 77%.
... Meanwhile, Shih and Fang [13] had produced Psychological Stress Index (PSI) using Heart Rate Interval and Plethysmography to assess human mental condition. The relaxation level of an individual could be determined by the slope of EEG linear regression in timebased measurement [14][15]. Handri et al. [1] produced two level of stress (high and low) from physiological signals such as EEG, ECG and Skin Temperature. ...
This paper presents the development of automatic vehicle plate detection system using image processing technique. The famous name for this system is Automatic Number Plate Recognition (ANPR). Automatic vehicle plate detection system is commonly used in field of safety and security systems especially in car parking area. Beside the safety aspect, this system is applied to monitor road traffic such as the speed of vehicle and identification of the vehicle's owner. This system is designed to assist the authorities in identifying the stolen vehicle not only for car but motorcycle as well. In this system, the Optical Character Recognition (OCR) technique was the prominent technique employed by researchers to analyse image of vehicle plate. The limitation of this technique was the incapability of the technique to convert text or data accurately. Besides, the characters, the background and the size of the vehicle plate are varied from one country to other country. Hence, this project proposes a combination of image processing technique and OCR to obtain the accurate vehicle plate recognition for vehicle in Malaysia. The outcome of this study is the system capable to detect characters and numbers of vehicle plate in different backgrounds (black and white) accurately. This study also involves the development of Graphical User Interface (GUI) to ease user in recognizing the characters and numbers in the vehicle or license plates.
... Meanwhile, Shih and Fang [13] had produced Psychological Stress Index (PSI) using Heart Rate Interval and Plethysmography to assess human mental condition. The relaxation level of an individual could be determined by the slope of EEG linear regression in timebased measurement [14][15]. Handri et al. [1] produced two level of stress (high and low) from physiological signals such as EEG, ECG and Skin Temperature. ...
This paper presents a non-parametric method to produce stress index using Electroencephalogram (EEG) signals. 180 EEG datasets from healthy subjects were evaluated at two cognitive states; resting state (Eyes Closed) and working state (Eyes Open). In working cognitive state, subjects were asked to answer the Intelligence Quotient (IQ) test questions. The EEG datasets were categorized into 4 groups. Energy Spectral Density (ESD) ratios and Spectral Centroids (SC) from the two tasks were calculated and selected as input features to k-Nearest Neighbor (k-NN) classifier. Shannon's Entropy (SE) was used to detect and quantify the distribution of ESD due to stressors (stress factors). The stress indexes were assigned based on the results of classification, ESD ratios, SC and SE. There were 3 types of stress indexes can be assigned which represent the stress level (low stress, moderate stress and high stress) at classification accuracy of 88.89%. The regression coefficient of the SC of Beta and Alpha was 77%.
... Negative slope would indicate less relaxes (more stress). Meanwhile, positive slope would indicate more relax (less stress) [19]. ...
This paper presents EEG Asymmetry and Spectral Centroids techniques in extracting unique features for human stress. The study involved 51 subjects (27 males and 24 females) for Close-eye state(do nothing) and 50 subjects (21 males and 29 females) for Open-eye state (perform IQ test). The subjects then were categorized into 2 groups for all EEG frequency bands (Delta, Theta, Alpha and Beta) by using EEG Asymmetry technique. The negative asymmetry was labelled as Stress group and positive asymmetry was labelled as Non-Stress group. The data in each group in term of Energy Spectral Density (ESD) were normalized by using Z-score technique to produce an index to each asymmetry group. Next, the Spectral Centroids techniques were applied to each group and EEG frequency bands to obtain Centroids values. Since there were 2 asymmetry groups per EEG frequency bands, a total of 8 Centroids values were produced for each cognitive states. The plot of Centroids for both cognitive states showed some unique patterns related to stress.
... For example, Discrete Wavelet Transform (DWT) was used to extract features from EEG signals before feeding to k-NN to classify human emotion in term of disgust, happy, surprise, fear and natural with classification accuracy of 83.26% [8]. Teplan used slope of EEG linear regression to be a feature to determine the relaxation level of an individual [9][10]. Sulaiman et al. [11] used a combination of EEG Asymmetry and Spectral Centroids techniques as a feature to detect unique pattern of human stress. ...
This paper presents the combination of electroencephalogram (EEG) power spectrum ratio and Spectral Centroids techniques to extract unique features for human stress from EEG signals. The combination of these techniques was able to improve the k-NN (k-Nearest Neighbor) classifier accuracy to detect and classify human stress from two cognitive states, Close-eye (CE) and Open-eye (OE). The EEG power spectrum in term of Energy Spectral Density (ESD) for each frequency bands (Delta, Theta, Alpha and Beta) was calculated. The ratio of EEG power spectrum and the average value of Spectral Centroids were selected as features to k-Nearest Neighbor (k-NN). The training and testing of the classifier were evaluated at 50:50 ratios and 70:30 ratios. The results showed that the combination of EEG power spectrum and Spectral Centroids techniques with the training and testing of k-NN set at 70:30 able to detect and classify the unique features for human stress at 88.89% accuracy.
Biosignals are recorded as potentials, voltages, and electrical field strengths generated by nerves and muscles. The measurements involve voltages at very low levels, typically ranging between 1 µµV and 100 mV, with high source impedances and superimposed high-level interference signals and noise. The signals need to be amplified to make them compatible with devices such as displays, recorders, or analog/ digital (A/D) converters for computerized equipments. Amplifiers suitable to measure these signals have to satisfy very specific requirements. They have to provide amplification selective to the physiological signal, reject superimposed noise and interference signals, and guarantee protection from damages through voltage and current surges for both patient and electronic equipment. Amplifiers featuring these specifications are known as biopotential amplifiers. The basic requirements and features as well as some specialized systems will be presented.
Transient changes of dynamical complexity in preictal, ictal and interictal invasive EEG recordings of an epileptic patient were characterized by linear and nonlinear measures related to entropy rates, as well as by correlation dimension and a nonlinearity index. All measures brought qualitatively equivalent results, however, the nonlinear coarse-grained entropy rate was found to be the most sensitive measure.
The mechanisms by which cognitive processes influence states of bodily arousal are important for understanding the pathogenesis and maintenance of stress-related morbidity. We used PET to investigate cerebral activity relating to the cognitively driven modulation of sympathetic activity. Subjects were trained to perform a biofeedback relaxation exercise that reflected electrodermal activity and were subsequently scanned performing repetitions of four tasks: biofeedback relaxation, relaxation without biofeedback and two corresponding control conditions in which the subjects were instructed not to relax. Relaxation was associated with significant increases in left anterior cingulate and globus pallidus activity, whereas no significant increases in activity were associated with biofeedback compared with random feedback. The interaction between biofeedback and relaxation, highlighting activity unique to biofeedback relaxation, was associated with enhanced anterior cingulate and cerebellar vermal activity. These data implicate the anterior cingulate cortex in the intentional modulation of bodily arousal and suggest a functional neuroanatomy of how cognitive states are integrated with bodily responses. The findings have potential implications for a mechanistic account of how therapeutic interventions, such as relaxation training in stress-related disorders, mediate their effects.