Higher Order Crossings Research Articles

Evaluating the Causal Effect of Multimedia and Affective Temperament in Felt Emotion and Liking

In this paper, we propose an approach to evaluate the causal effect of videos on subjects who watched movies from the LIRIS-ACCEDE dataset and from whom the following information was collected: affective temperaments, gender, and electroencephalography (EEG) signals. The affective temperament was obtained by analyzing their answers to the AFECT questionnaire. Evidence was collected from specialized literature to design a Structural Causal Model to be subjected to Do-Calculus Causal Inference. Video concepts were extracted to characterize the major video content after k-means clustering. Information from 15 volunteers was analyzed and the effects of video content, affective temperament, and gender on emotion response and liking were computed. Higher Order Crossings (HOC) were extracted from EEG signals and the features were clustered and used as intermediate evidence of affective influence. This research provides answers for the following questions about the specific watched videos: (i) How does gender affect the felt emotion and liking? (ii) How does the affective temperament of a person affect felt emotion and liking? and (iii) How does the content of a video affect felt emotion and liking? The main contribution of this paper is in the proposed methodology which can be applied to any similar dataset to investigate the causal relationships of video content and affective temperament on the emotion of the audience.

Higher Order Crossings Analysis of Signals Over Graphs

We propose the extension of the Higher Order Crossings (HOC) analysis concept for signals over graphs. The HOC over graphs sequence, ( $HOC_g$ ), is defined and its efficacy on graph signal discrimination is investigated. Compared with state of the art graph signal discrimination algorithms, the proposed $HOC_g$ combined with a Support Vector Machine classifier achieves fast and similar or better performance over five different benchmark data sets.

EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention.

To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Twenty adolescents with ASD participated in a six-session virtual reality driving simulator-based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist's rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins () were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k-nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention.

Towards a Practical Subject-Independent Affective State Recognition Based on Time-Domain EEG Feature Extraction

This work presents an evaluation of two time domain-based features, i.e., fractal dimension (FD) and higher-order crossings (HOC), for the subject-independent EEG-based recognition of four affective states as a preliminary step towards a practical real-time affective brain computer interface. EEG data were acquired from an experiment targeting the elicitation of four emotions using affective sounds. Features were computed for each electrode individually and tested in terms of classification using the k-nearest neighbors classifier. Results show that the valence of affective states can be recognized effectively, when the arousal level is specified. Moreover, an above chance level classification accuracy was achieved using a single electrode for the four affective states recognition. Both FD and HOC performed similarly, while the best classification rates were achieved from frontal electrode locations.

Strong polarization mode coupling in microresonators.

We observe strong modal coupling between the TE00 and TM00 modes in Si3N4 ring resonators revealed by avoided crossings of the corresponding resonances. Such couplings result in significant shifts of the resonance frequencies over a wide range around the crossing points. This leads to an effective dispersion that is one order of magnitude larger than the intrinsic dispersion and creates broad windows of anomalous dispersion. We also observe the changes to frequency comb spectra generated in Si3N4 microresonators due to polarization mode and higher-order mode crossings and suggest approaches to avoid these effects. Alternatively, such polarization mode crossings can be used as a tool for dispersion engineering in microresonators.

Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain

This paper aims at developing adaptive methods for electroencephalogram (EEG) signal segmentation in the time-frequency domain, in order to effectively retrieve the emotion-related information within the EEG recordings. Using the multidimensional directed information analysis supported by the frontal brain asymmetry in the case of emotional reaction, a criterion, namely asymmetry index , is used to realize the proposed segmentation processes that take into account both the time and frequency (in the empirical mode decomposition domain) emotionally related EEG components. The efficiency of the -based “emotional” filters was justified through an extensive classification process, using higher-order crossings and cross-correlation as feature-vector extraction techniques and a support vector machine classifier for six different classification scenarios in the valence/arousal space. This resulted in mean classification rates from 64.17% up to 82.91% in a user-independent base, revealing the potential of establishing such a filtering for reliable EEG-based emotion recognition systems.

Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis

This paper aims at providing a new feature extraction method for a user-independent emotion recognition system, namely, HAF-HOC, from electroencephalograms (EEGs). A novel filtering procedure, namely, Hybrid Adaptive Filtering (HAF), for an efficient extraction of the emotion-related EEG-characteristics was developed by applying Genetic Algorithms to the Empirical Mode Decomposition-based representation of EEG signals. In addition, Higher Order Crossings (HOCs) analysis was employed for feature extraction realization from the HAF-filtered signals. The introduced HAF-HOC scheme incorporated four different classification methods to accomplish a robust emotion recognition performance. Through a series of facial-expression image projection, as a Mirror Neuron System-based emotion elicitation process, EEG data related to six basic emotions (happiness, surprise, anger, fear, disgust, and sadness) have been acquired from 16 healthy subjects using three EEG channels. Experimental results from the application of the HAF-HOC to the collected EEG data and comparison with previous approaches have shown that the HAF-HOC scheme clearly surpasses the latter in the field of emotion recognition from brain signals for the discrimination of up to six distinct emotions, providing higher classification rates up to 85.17 percent. The promising performance of the HAF-HOC surfaces the value of EEG signals within the endeavor of realizing more pragmatic, affective human-machine interfaces.

Emotion Recognition From EEG Using Higher Order Crossings

Electroencephalogram (EEG)-based emotion recognition is a relatively new field in the affective computing area with challenging issues regarding the induction of the emotional states and the extraction of the features in order to achieve optimum classification performance. In this paper, a novel emotion evocation and EEG-based feature extraction technique is presented. In particular, the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation. In addition, higher order crossings (HOC) analysis was employed for the feature extraction scheme and a robust classification method, namely HOC-emotion classifier (HOC-EC), was implemented testing four different classifiers [quadratic discriminant analysis (QDA), k-nearest neighbor, Mahalanobis distance, and support vector machines (SVMs)], in order to accomplish efficient emotion recognition. Through a series of facial expression image projection, EEG data have been collected by 16 healthy subjects using only 3 EEG channels, namely Fp1, Fp2, and a bipolar channel of F3 and F4 positions according to 10-20 system. Two scenarios were examined using EEG data from a single-channel and from combined-channels, respectively. Compared with other feature extraction methods, HOC-EC appears to outperform them, achieving a 62.3% (using QDA) and 83.33% (using SVM) classification accuracy for the single-channel and combined-channel cases, respectively, differentiating among the six basic emotions, i.e., happiness, surprise, anger, fear, disgust, and sadness. As the emotion class-set reduces its dimension, the HOC-EC converges toward maximum classification rate (100% for five or less emotions), justifying the efficiency of the proposed approach. This could facilitate the integration of HOC-EC in human machine interfaces, such as pervasive healthcare systems, enhancing their affective character and providing information about the user's emotional status (e.g., identifying user's emotion experiences, recurring affective states, time-dependent emotional trends).

Using higher‐order crossings to distinguish liver regeneration indices in hepatectomized diabetic and non‐diabetic rats

Diabetes mellitus is implicated in several liver diseases; hence, its potential affection to liver regenerative capacity is an open research question. So far, only sporadic studies have addressed this issue, mainly using basic statistical techniques. The current study evaluated the ability of a novel technique, namely higher-order crossings (HOC), based on liver DNA biosynthesis and thymidine kinase (TK) enzymatic activity data, to discriminate liver regeneration processes between hepatectomized diabetic and non-diabetic rats. We used 251 adult male rats, divided in two groups; diabetic by Alloxan injection and non-diabetic control, subjected to 70% partial hepatectomy and killed at different time intervals post-partial hepatectomy (PH) (0-240 h). The rate of tritiated thymidine (3HTdR) incorporation into hepatic DNA and the enzymatic activity of liver TK were estimated and, after proper interpolation, were analyzed using HOC sequences. Changes of the latter were captured and used as a means for linear discrimination between the two groups. Ninth-order HOC estimated for post-PH (24, 28, 40, 44, 72 and 84 h) exhibited linear discrimination for the rate of 3HTdR incorporation, whereas second-order HOC estimated for (44-72 h) post-PH exhibited linear discrimination for the TK enzymatic activity data. Fuzzy logic-based c-means cluster analysis of HOC provided distinct areas of group categorization (100% accuracy) for diagnostic distinctions (P < 0.001). The data grouping pointed out by the HOC-based analysis revealed an onset delay in the liver regeneration process when Alloxan diabetes was present (P < 0.05). Our results suggest that HOC have the potential to linearly discriminate between experimentally induced diabetic and non-diabetic liver regeneration post-PH processes, based on two liver regeneration indices, capturing the delay seen in the liver regeneration process due to Alloxan diabetes, fostering their use as an efficient classification tool. In this way, HOC could be used as an advanced, easily implemented and user-friendly method to thoroughly analyze liver regeneration processes.

Discrimination analysis of discontinuous breath sounds using higher-order crossings.

The paper evaluates the performance of an automatic discrimination analysis (DA) method used to discriminate efficiently the types of discontinuous breath sound (DBS), i.e. fine crackles (FCs), coarse crackles (CCs) and squawks (SQs); this may lead to more accurate characterisation of the pulmonary acoustical changes due to the related pathology. Based on higher-order crossings (HOCs), the proposed method, HOC-DA, captured the differences in the oscillatory patterns of FCs, CCs and SQs, which are only exposed when higher (> 1) crossings are employed. Prior to HOC-DA, wavelet-based de-noising of DBSs was employed to eliminate the effects of the vesicular sound (background noise) from their oscillatory pattern. The HOC-DA was applied to 157 discontinuous breath sounds corresponding to 16 cases included in three lung sound databases. Results showed that the HOC-DA efficiently separated FCs from CCs, SQs from CCs (both with an accuracy of 100%), and SQs from FCs (accuracy of 80%), with the optimum order ranging from 9 to 11. When compared with other classification tools, the HOC-DA resulted in high discrimination accuracy without involving high computational complexity. Owing to its simplicity, it could be implemented in a real-time context and be used in clinical medicine as a module of an integrated intelligent patient evaluation system.

A fast algorithm for detecting contractions in uterine electromyography.

Discusses a method of higher-order zero crossings, which was found to be an effective tool for signal analysis. This method mirrors the spectral characteristics of certain Gaussian and non-Gaussian processes. The authors use this method to discriminate between contraction segments interspersed in a uterine EMG recorded from the abdominal surface. They also describe a schematic circuit that can be implemented for fast on-line detection of contraction segments.

Time Series Analysis by Higher Order Crossings.

Time Series Analysis by Higher Order Crossings.

Tie Series Analysis by Higher Order Crossings

(1995). Tie Series Analysis by Higher Order Crossings. Technometrics: Vol. 37, No. 2, pp. 235-235.

Time Series Analysis by Higher Order Crossings

Time Series Analysis by Higher Order Crossings

Book Reviews

Abstract Abstract Large-Sample Methods in Statistics: An Introduction With Applications. Pranab K. Sen and Julio M. Singer. New York: Routledge, Chapman, and Hall, 1993. xii + 382 pp. $59.95. Reviewed by Jayaram Sethuraman Florida State University Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. Phillip Good. New York: Springer-Verlag, 1994. × + 228 pp. $39.95. Reviewed by Reza Modarres The George Washington University The Theory of Linear Models. B. Jørgensen. New York: Chapman and Hall, 1993. xii + 226 pp. $49.95. Reviewed by G. U. H. Seeber University of Innsbruck Alternative Methods of Regression. David Birkes and Yadolah Dodge. New York: John Wiley, 1993. xi + 228 pp. $49.95. Reviewed by Arnold J. Stromberg University of Kentucky Private Lives And Public Policies: Confidentiality and Accessibility of Government Services. George T. Duncan, Thomas B. Jabine, and Virginia A. de Wolf (eds.). Washington, D.C.: National Academy Press, 1993, xiii + 274 pp. $34.95. Reviewed by Fritz Scheuren The George Washington University Phantom Risk: Scientific Inference and the Law. K. R. Foster, D. E. Bernstein, and P. W. Huber (eds.). Cambridge, MA: The MIT Press, 1993. × + 457 pp. $39.95. Reviewed by Joseph L. Gastwirth The George Washington University Planning Pharmaceutical Clinical Trials: Basic Statistical Principles. William M. Wooding. New York: John Wiley, 1994. xx + 539 pp $69.95. Reviewed by James Schwenke Kansas State University Methodology of Clinical Drug Trials (2nd ed.). Alain Spriet, Thérèse Dupin-Spriet, and Pierre Simon. (Translated by Robert Coluzzi, John Young, Richard Edelstein, and Michael Wein-traub.) Basel, Switzerland: Karger, 1994. vii + 269 pp. $113.75. Reviewed by Ralph B. D'Agostino Boston University Statistics in the Pharmaceutical Industry (2nd ed.). C. R. Buncher and J. Y. Tsay (eds.). New York: Marcel Dekker, 1993. xviii +563 pp. $150. Reviewed by Christy Chuang-Stein The Upjohn Company Problems and Snapshots From the World of Probability. Gunnar Blom, Lars Holst, and Dennis Sandell. New York: Springer-Verlag, 1994. xii + 240 pp. $32. Reviewed by Lennart Ràde Chalmers University of Technology Practical Risk Theory for Actuaries. C. D. Daykjn, T. Pentikainen, and M. Pesonen. London: Chapman and Hall, 1993. xxi + 546 pp. £35. Reviewed by Christina Hipp Universität Karlsruhe Markov Decision Processes: Discrete Stochastic Dynamic Programming. Martin L. Puterman. New York: John Wiley 1994. 649 pp. $79.95. Reviewed by Kasra Hazeghi ETH-Zurich Time Series Analysis by Higher Order Crossings. Benjamin Kedem. Piscataway, NJ: IEEE Press, 1994. 318 pp. $69.95. Reviewed by Pablo Marshall Universidad Catolìca de Chile Time Series Models (2nd ed.). Andrew C. Harvey. Cambridge, MA: MIT Press, 1993. xvii + 308 pp. $38.30. Reviewed by Greta M. Ljuno Massachusetts Institute of Technology Outliers in Statistical Data (3rd ed.). Vic Barnett and Toby Lewis. New York: John Wiley, 1994. 584 pp. $79.95. Reviewed by Karen Kafadar University of Colorado-Denver Elements of Dual Scaling: An Introduction to Practical Data Analysis. Shizuhiko Nishisato. Hillsdale, NJ: Lawrence Erlbaum, 1994. xiv + 381 pp. $79.95. Reviewed by Jacqueline J. Meulman Leiden University Design and Analysis of Experiments, Volume 1: Introduction to Experimental Design. Klaus Hinkelmann and Oscar Kempthorne. New York: John Wiley, 1994. xvi + 495 pp. $49.95. Reviewed by J. Richard Trout Rutgers, The State University of New Jersey Methods for Experimental Design: Principles and Applications for Physicists and Chemists. J. L. Goupy. Amsterdam: Elsevier Science Publishers, 1993. xvi + 449 pp. $185.75. Reviewed by Robert W. Mee University of Tennessee Reliability Improvement With Design of Experiments. Lloyd W. Condra. New York: Marcel Dekker, 1993. $65. Reviewed by K. B. Kulasekera Clemson University

Time Series Analysis by Higher Order Crossings.

Time Series Analysis by Higher Order Crossings.

Approximations of first-passage times for differentiable processes based on higher-order threshold crossings

Methods for calculating approximations of the first-passage probability of differentiable non-narrow band processes based on higher-order threshold crossings are discussed. Two of them use factorial moments of the number of crossings into the failure region, including a new method based on a Gram-Charlier series expansion of the distribution of the number of exits. Several numerical schemes for the evaluation of factorial moments are investigated. The methods are studied for three examples. The examples show that the Gram-Charlier series expansion converges faster towards the exact solution than Rice's “in- and exclusion” series. However, it is difficult to quantify the error made by the proposed method. Further, it is shown that for engineering applications, the Poisson assumption as modified by Ditlevsen such that the initial conditions are taken into account, provides excellent results in almost all cases.

Time Series Analysis of Higher Order Crossings (B. Kedem)

Time Series Analysis of Higher Order Crossings (B. Kedem)

AN ITERATIVE FILTERING ALGORITHM FOR NON‐FOURIER FREQUENCY ESTIMATION

Abstract. We study an iterative filtering method to estimate frequencies of random Gaussian sinusoids in white noise. The method uses higher order crossings and takes advantage of a fixed point to guide the use of bandpass filtering in an attempt effectively to increase the signal‐to‐noise ratio. At each iteration the expected zero‐crossing rate is estimated from independent time series. Convergence occurs with any prespecified probability less than one.

Higher-order crossings: a new acoustic emission signal processing method : National Bureau of Standards, Gaithersburg, PB89-173488, 8 pp. (1988)

Higher-order crossings: a new acoustic emission signal processing method : National Bureau of Standards, Gaithersburg, PB89-173488, 8 pp. (1988)