nils-herrmann/clean_scirepeval huggingface dataset:
Revealing Action Representation Processes in Audio Perception Using Fractal EEG Analysis

Electroencephalogram (EEG) recordings, and especially the Mu-rhythm over the sensorimotor cortex that relates to the activation of the mirror neuron system (MNS), were acquired from two subject groups (orchestral musicians and nonmusicians), in order to explore action representation processes involved in the perception and performance of musical pieces. Two types of stimuli were used, i.e., an auditory one consisting of an excerpt of Beethoven's fifth symphony and a visual one presenting a conductor directing an orchestra performing the same excerpt of the piece. Three tasks were conducted including auditory stimulation, audiovisual stimulation, and visual stimulation only, and the ac quired signals were processed using fractal [time-dependent fractal dimension (FD) estimation] and statistical analysis (analysis of variance, Mann-Whitney). Experimental results showed significant differences between the two groups while desychronization of the Mu-rhythm, which can be linked to MNS activation, was observed during all tasks for the musicians' group, as opposed to the nonmusicians' group who exhibited similar response only when the visual stimulus was present. The mobility of the conductor was also correlated to the estimated FD signals, showing significantly higher correlation for the case of musicians compared to nonmusicians' one. The present study sheds light upon the difference in action representation in auditory perception between musicians and nonmusicians and paves the way for better comprehension of the underlying mechanisms of the MNS.
EEG-Based Tonic Cold Pain Characterization Using Wavelet Higher Order Spectral Features

A novel approach in tonic cold pain characterization, based on electroencephalograph (EEG) data analysis using wavelet higher order spectral (WHOS) features, is presented here. The proposed WHOS-based feature space extends the relative power spectrum-based (phase blind) approaches reported so far a step forward; this is realized via dynamic monitoring of the nonlinerities of the EEG brain response to tonic cold pain stimuli by capturing the change in the underlying quadratic phase coupling at the bifrequency wavelet bispectrum/bicoherence domain due to the change of the pain level. Three pain characterization scenarios were formed and experimentally tested involving WHOS-based analysis of EEG data, acquired from 17 healthy volunteers that were subjected to trials of tonic cold pain stimuli. The experimental and classification analysis results, based on four well-known classifiers, have shown that the WHOS-based features successfully discriminate relax from pain status, provide efficient identification of the transition from relax to mild and/or severe pain status, and translate the subjective perception of pain to an objective measure of pain endurance. These findings seem quite promising and pave the way for adopting WHOS-based approaches to pain characterization under other types of pain, e.g., chronic pain and various clinical scenarios.
Hilbert-Huang Spectrum as a new field for the identification of EEG event related de-/synchronization for BCI applications

Brain Computer Interfaces (BCI) usually utilize the suppression of mu-rhythm during actual or imagined motor activity. In order to create a BCI system, a signal processing method is required to extract features upon which the discrimination is based. In this article, the Empirical Mode Decomposition along with the Hilbert-Huang Spectrum (HHS) is found to contain the necessary information to be considered as an input to a discriminator. Also, since the HHS defines amplitude and instantaneous frequency for each sample, it can be used for an online BCI system. Experimental results when the HHS applied to EEG signals from an on-line database (BCI Competition III) show the potentiality of the proposed analysis to capture the imagined motor activity, contributing to a more enhanced BCI performance.
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.
Swarm Intelligence for Detecting Interesting Events in Crowded Environments

This paper focuses on detecting and localizing anomalous events in videos of crowded scenes, i.e., divergences from a dominant pattern. Both motion and appearance information are considered, so as to robustly distinguish different kinds of anomalies, for a wide range of scenarios. A newly introduced concept based on swarm theory, histograms of oriented swarms (HOS), is applied to capture the dynamics of crowded environments. HOS, together with the well-known histograms of oriented gradients, are combined to build a descriptor that effectively characterizes each scene. These appearance and motion features are only extracted within spatiotemporal volumes of moving pixels to ensure robustness to local noise, increase accuracy in the detection of local, nondominant anomalies, and achieve a lower computational cost. Experiments on benchmark data sets containing various situations with human crowds, as well as on traffic data, led to results that surpassed the current state of the art (SoA), confirming the method's efficacy and generality. Finally, the experiments show that our approach achieves significantly higher accuracy, especially for pixel-level event detection compared to SoA methods, at a low computational cost.
A curvelet-based lacunarity approach for ulcer detection from Wireless Capsule Endoscopy images

Wireless Capsule Endoscopy (WCE) is a fairly new technology that offers a low-risk, non invasive visual inspection of the patient's digestive tract, especially the small bowel, that was previously unreachable using the traditional endoscopic methods. However, the large amount of images produced by WCE requires a highly trained physician to manually inspect them; a procedure that is time consuming and prone to human error. This was the rationale to propose a novel strategy for automatic detection of WCE images related to ulcer, one of the most common findings of the digestive tract. This paper introduces a new texture extraction method based on the Discrete Curvelet Transform (DCT), a recent multi-resolution analysis tool. Textural information is acquired by calculating the lacunarity index of DCT subbands of the WCE images. The classification step is performed by a Support Vector Machine (SVM), demonstrating promising classification accuracy (86.5%) and pointing towards further research in this field.
A Novel Emotion Elicitation Index Using Frontal Brain Asymmetry for Enhanced EEG-Based Emotion Recognition

This paper aims at providing a novel method for evaluating the emotion elicitation procedures in an electroencephalogram (EEG)-based emotion recognition setup. By employing the frontal brain asymmetry theory, an index, namely asymmetry Index (AsI), is introduced, in order to evaluate this asymmetry. This is accomplished by a multidimensional directed information analysis between different EEG sites from the two opposite brain hemispheres. The proposed approach was applied to three-channel (Fp1, Fp2, and F3/F4 10/20 sites) EEG recordings drawn from 16 healthy right-handed subjects. For the evaluation of the efficiency of the AsI, an extensive classification process was conducted using two feature-vector extraction techniques and a SVM classifier for six different classification scenarios in the valence/arousal space. This resulted in classification results up to 62.58% for the user independent case and 94.40% for the user-dependent one, confirming the efficacy of AsI as an index for the emotion elicitation evaluation.
Tempo Induction from Music Recordings Using Ensemble Empirical Mode Decomposition Analysis

Tempo and beat are among the most important features of Western music. Owing to the perceptual nature of tempo, its automatic analysis and extraction remains a difficult task for a large variety of music genres. Western music notation represents musical events using a hierarchical metrical structure distinguishing different time scales. This hierarchy is often modeled using three levels: the tatum, the tactus, and the measure. The tatum represents the shortest durational value in music that is not just an accidental phenomenon (Bilmes 1993). The tactus period is the most perceptually prominent period, and is the period at which most humans would tap their feet in time with the music (Lerdahl and Jackendoff 1983). The measure period is often related to the rate of harmonic change or the length of a repeated rhythmic pattern. This article deals with the estimation of the tempo at the tactus level. The tempo, which is the inverse of the tactus (beat) period, is expressed as the number of beats per minute (BPM). Tempo information is very important in many music information retrieval applications. Successful audio applications and music-analysis systems, such as video and audio editing software, cut-and-paste disk jockey applications, electronic instrument control, and synchronization, must be capable of analyzing and efficiently manipulating the rhythmic content of music. A review of different state-of-the art algorithms for tempo extraction from audio data is presented in the next section.
Adaptive reduction of heart sounds from lung sounds using fourth-order statistics

When recording lung sounds, an incessant noise source occurs due to heart sounds. This noise source severely contaminates the breath sound signal and interferes in the analysis of lung sounds. In this paper, an adaptive heart-noise reduction method, based on fourth-order statistics (FOS) of the recorded signal, without requiring recorded "noise-only" reference signal, is presented. This algorithm uses adaptive filtering to preserve the entire spectrum. Furthermore, the proposed filter is independent of Gaussian uncorrelated noise and insensitive to the step-size parameter. It converges fast with small excess errors and, due to the narrowband nature of heart noise (HN), it requires a very small number of taps. Results from experiments with healthy subjects indicate a local HN reduction equal to or greater than 90%.
Enhancement of explosive bowel sounds using Kurtosis-based filtering

An efficient method for the enhancement of bowel sounds (BS), based on kurtosis is presented in this paper. The proposed method employs an iterative procedure to compose a Kurtosis-based Detector (KD), for enhanced separation of explosive BS (EBS) from the background noise. Analysis of the experimental results, when applying the KD to EBS recorded from patients with gastrointestinal dysfunction, justify an efficient performance of the KD to outwit the noise presence by accurately extracting the authentic structure of EBS. Since the KD removes the undesired signal from the EBS recordings, it can easily be used for data-volume reduction when long-term recordings are employed, e.g., in sleep laboratories, intensive care units, homecare telemedical devices. Due to its simplicity, the KD can be implemented in a real-time context and can be used for continuous EBS screening.
Epione: An Innovative Pain Management System Using Facial Expression Analysis, Biofeedback and Augmented Reality-Based Distraction

An innovative pain management system, namely Epione, is presented here. Epione deals with three main types of pain, i.e., acute pain, chronic pain, and phantom limb pain. In particular, by using facial expression analysis, Epione forms a dynamic pain meter, which then triggers biofeedback and augmented reality-based destruction scenarios, in an effort to maximize patient's pain relief. This unique combination sets Epione not only a novel pain management approach, but also a means that provides an understanding and integration of the needs of the whole community involved i.e., patients and physicians, in a joint attempt to facilitate easing of their suffering, provide efficient monitoring and contribute to a better quality of life.
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).
Enhancement of the diagnostic character of bowel sounds using higher-order crossings

A new method for revealing the diagnostic character of bowel sounds (BS) was applied on BS, recorded from controls and pre-confirmed patients with gastrointestinal dysfunction and de-noised using the WTST-NST filter. Experimental results showed that the technique is suitable in discriminating the intestinal pathologies using only the information provided by the BS. The non-invasiveness and the effectiveness of the method along with its fast and easy implementation provide a feasible tool for clinical evaluation of BS.
Detection of explosive lung and bowel sounds by means of fractal dimension

An efficient technique for detecting explosive lung sounds (LS) (fine/coarse crackles and squawks) or bowel sounds (BS) in clinical auscultative recordings is presented. The technique is based on a fractal-dimension (FD) analysis of the recorded LS and BS obtained from controls and patients with pulmonary and bowel pathology, respectively. Experimental results demonstrate the efficiency of the proposed method, since it clearly detects the time location and duration of LS and BS, despite their variation either in time duration and/or amplitude. A noise stress test justifies the noise robustness of the FD-based detector, indicating its potential use in everyday clinical practice.
A fuzzy rule-based system for real-time separation of crackles from vesicular sounds

An automated way of revealing the diagnostic character of crackle by isolating them from vesicular sounds (VS) is presented in this paper. The proposed algorithm takes into account crackle nonstationarity and uses fuzzy rules in order to compose a fuzzy-based stationary-nonstationary filter (FST-NST). Applying the FST-NST filter to fine/coarse crackles, selected from three lung sound databases, the coherent structure of crackles is revealed and they are separated from VS. The resulted separation is accurate, objective, and of a high quality, since the FTST-NST filter automatically identifies the true location of crackles in the original signal and maintains their structure in its nonstationary output. Due to its simple and fast implementation it can easily be used as an on-line crackle identification system in clinical medicine.
Fuzzy logic-based risk of fall estimation using smartwatch data as a means to form an assistive feedback mechanism in everyday living activities.

This Letter aims to create a fuzzy logic-based assistive prevention tool for falls, based on accessible sensory technology, such as smartwatch, resulting in monitoring of the risk factors of falls caused by orthostatic hypotension (OH); a drop in systolic blood pressure (DSBP) >20 mmHg due to postural changes. Epidemiological studies have shown that OH is a high risk factor for falls and has a strong impact in quality of life (QoL) of the elderly's, especially for some cases such as Parkinsonians. Based on smartwatch data, it is explored here how statistical features of heart rate variability (HRV) can lead to DSBP prediction and estimation of the risk of fall. In this vein, a pilot study was conducted in collaboration with five Greek Parkinson's Foundation patients and ten healthy volunteers. Taking into consideration, the estimated DSBP and additional statistics of the user's medical/behavioural history, a fuzzy logic inference system was developed, to estimate the instantaneous risk of fall. The latter is fed back to the user with a mechanism chosen by him/her (i.e. vibration and/or sound), to prevent a possible fall, and also sent to the attentive carers and/or healthcare professionals for a home-based monitoring beyond the clinic. The proposed approach paves the way for effective exploitation of the contribution of smartwatch data, such as HRV, in the sustain of QoL in everyday living activities.
Nonlinear analysis of heart murmurs using wavelet-based higher-order spectral parameters

The aim of this study was to reveal and analyze the nonlinear characteristics of heart sounds, reflected in the quadrature phase coupling of the contained frequencies, as they evolve over time. To achieve this, the continuous wavelet transform was combined with third-order statistics/spectra in order to analyze their non Gaussian character, taking into account their non-stationarity. Heart sounds from patients with several pathologies that exhibit murmurs were drawn from a heart sound database and analyzed in the time-bi-frequency domain. The analysis results justified the efficient performance of this combinatory approach to reveal and quantify the evolution of heart murmurs nonlinearities with time
Wavelet-based Analysis of Nocturnal Snoring in Apneic Patients Undergoing Polysomnography

Snoring is a typical inspiratory sound appearing during sleep, mostly in male patients. Snoring may also occur in conjunction with a disordered sleep pattern and can be associated with a range of symptoms, including obstructive sleep apnea syndrome (OSAS). OSAS is a type of sleep apnea due to upper airway obstruction, during persistent ventilatory movements and it can result in cessation of breathing. The standard diagnosis of OSAS comprises a full nocturnal session in a sleep laboratory. Many efforts have been made towards the development of less expensive testing methods and snoring analysis constitutes one of these methods. The aim of the current study is to explore any possible relationship between snoring analysis using wavelet transform and apnea syndrome. Snoring sounds were acquired overnight in a sleep laboratory, together with the polysomnography (PSG) testing. The analysis was based on the remarks of a specialized doctor on the PSG results.
An emotion elicitation metric for the Valence/Arousal and six basic emotions affective models: A comparative study

In this work, a comparison between two different emotion elicitation procedures, the Mirror Neuron System (MNS)-based one, which refers to the affective model of the six basic emotions, and Valence/Arousal-based one, is attempted with regard to their ability to evoke human emotions. Towards that the mutual information between left and right frontal brain hemispheres was exploited in order to define a metric for their reliability. EEG recordings from 16 subjects were captured and the concluding results showed a relative superiority of the MNS-based emotion elicitation technique in contrast with the Valence/Arousal-based one.
A Multimodal Approach for the Safeguarding and Transmission of Intangible Cultural Heritage: The Case of i-Treasures

Intangible Cultural Heritage (ICH) creations include, amongst other, music, dance, singing, theatre, human skills, and craftsmanship. These cultural expressions are usually transmitted orally and/or using gestures and are modified over a period of time, through a process of collective recreation. As the world becomes more interconnected and many different cultures come into contact, local communities run the risk of losing important elements of their ICH, while young people find it difficult to maintain the connection with the cultural heritage treasured by their elders. In this paper, we present a novel holistic approach for the safeguarding and transmission of ICH that goes beyond the mere digitization of ICH content. Based on multisensory technology for the capturing of ICH, the proposed approach enables the generation of completely novel cultural content. High-level semantics are extracted from the acquired data, enabling researchers to identify possible implicit or hidden correlations between different ICH expressions or interpretation styles and study the evolution of a specific ICH. These data, coupled with other cultural resources, are accessible through the i-Treasures Web-platform, which provides the means for supporting knowledge exchange between researchers as well as know-how transmission from ICH bearers to apprentices.