BIOSIGNALS 2016 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 3
Title:

Quantitative Estimation of Long-living Fluorescent Molecules from Temporal Fluorescence Intensity Data Corrupted by Nonzero-mean Noise
Authors:

Sofia Startceva, Jerome G. Chandraseelan, Ari Visa and Andre S. Ribeiro
Abstract: We present a new quantitative method of estimation of fluorescent molecule numbers from time-lapse, single-cell, fluorescence microscopy data. Its main aim is to eradicate backward propagation of noise, which is present in previous methods. The method is first validated using Monte Carlo simulations. These tests show that when the time-lapse data are corrupted with negative noise, the method obtains significantly more precise results than current techniques. The applicability of the method is demonstrated on novel time-lapse, single-cell measurements of fluorescently tagged ribonucleic acid (RNA) molecules. Interestingly, we find that the intervals inferred by the new method have the same mean but reduced variability when compared to the previously existing method, which, in accordance to human observers, is a more accurate estimation.
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Paper Nr: 5
Title:

Epileptic Seizure Prediction in Scalp EEG using One Dimensional Local Binary Pattern based Features
Authors:

Thasneem Fathima, Paul Joseph K. and M. Bedeeuzzaman
Abstract: Seizure prediction will deeply improve the quality of life of epileptic patients. In this paper, a new method of automatic seizure prediction is presented using one dimensional local binary pattern (1D-LBP) based features in scalp electroencephalogram (EEG). In the feature extraction stage, the preictal and interictal EEG signals were transformed to the 1D-LBP domain and histogram features were extracted. These features were submitted to two different types of classifiers: linear discriminant analysis (LDA) and support vector machine (SVM). In order to reduce the false prediction rate (FPR), a simple post processing stage was also incorporated. The classification using SVM showed improvement over LDA in terms of sensitivity, prediction time and FPR. The proposed method was evaluated using the scalp EEG recording from 13 patients with a total number of 47 seizures. It could achieve a sensitivity of 96.15%, an average prediction time of 51.25 minutes with an FPR of 0.463.
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Paper Nr: 15
Title:

Analysis of an Electrocardiographic Multilead System by Means of Artificial Neural Networks - Study of Repolarization During Premature Ventricular Stimulation
Authors:

Drago Torkar and Pedro David Arini
Abstract: The ventricular repolarization dispersion (VRD) has been shown to increase with premature stimulation. Moreover, several differences between left ventricular and right ventricular, such as the anatomic properties and fibrillation threshold have been reported. However, few data exist regarding the influence of the site of stimulation on modulation of VRD measure by electrocardiographic. In the present work, several ECG indices of VRD, as a function of the coupling interval and the site of stimulation, were studied in an isolated heart rabbit preparation (n=18), using ECG multilead (5 rows x 8 columns) system with Artificial Neural Networks. In both ventricles, results have shown significant decreases in early repolarization duration, while in the left ventricle we have found significant increases of transmural dispersion. Also, we have observed that when the premature stimuli were applied to the left ventricle, the ventricular repolarization dispersion changes were detected using only one preferential electrode (row1-column3). When stimuli were elicited at the right ventricle, changes of VRD were detected by three electrodes (row3-column1, row2-column1 and row3-column8). Finally, a different ventricular repolarization dispersion was found as a function of the site of stimulatio
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Paper Nr: 16
Title:

Fine-Grained Prediction of Cognitive Workload in a Modern Working Environment by Utilizing Short-Term Physiological Parameters
Authors:

Timm Hörmann, Marc Hesse, Peter Christ, Michael Adams, Christian Menßen and Ulrich Rückert
Abstract: In this paper we present a method to predict cognitive workload during the interaction with a tablet computer. To set up a predictor that estimates the reflected self-reported cognitive workload we analyzed the information gain of heart rate, electrodermal activity and user input (touch) based features. From the derived optimal feature set we present a Gaussian Process based learner that enables fine-grained and short term detection of cognitive workload. Average inter-subject accuracy in 10-fold cross validation is 74.1 % for the fine-grained 5-class problem and 96.0 % for the binary class problem.
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Paper Nr: 20
Title:

Continuous Real-time Heart Rate Monitoring from Face Images
Authors:

Tatsuya Mori, Daisuke Uchida, Masato Sakata, Takuro Oya, Yasuyuki Nakata, Kazuho Maeda, Yoshinori Yaginuma and Akihiro Inomata
Abstract: A real-time monitoring method of heart rate (HR) from face images using Real-time Pulse Extraction Method (RPEM) is described and corroborated for the theoretical efficacy by investigating fundamental mechanisms through three kinds of experiments; (i) measurement of light reflection from face covered by copper film, (ii) spectroscopy measurement and (iii) simultaneous measurement of face images and laser speckle images. The investigation indicated the main causes of brightness change are both the green light absorption variation by the blood volume changes and the face surface reflection variation by pulsatory face movements. RPEM removes the motion noise from the green light absorption variation and the effectiveness is ensured by comparing with the pulse wave of the ear photoplethysmography. We also applied RPEM to continuous real-time HR monitoring of seven participants during office work under non-controlled condition, and achieved HR measured rate of 44 % to the number of referential ECG beats while face is detected, with RMSE = 6.7 bpm as an average result of five days.
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Paper Nr: 33
Title:

Extraction of Temporal Gait Parameters using a Reduced Number of Wearable Accelerometers
Authors:

Mohamed Boutaayamou, Vincent Denoël, Olivier Brüls, Marie Demonceau, Didier Maquet, Bénédicte Forthomme, Jean-Louis Croisier, Cédric Schwartz, Jacques G. Verly and Gaëtan Garraux
Abstract: Wearable inertial systems often require many sensing units in order to reach an accurate extraction of temporal gait parameters. Reconciling easy and fast handling in daily clinical use and accurate extraction of a substantial number of relevant gait parameters is a challenge. This paper describes the implementation of a new accelerometer-based method that accurately and precisely detects gait events/parameters from acceleration signals measured from only two accelerometers attached on the heels of the subject’s usual shoes. The first step of the proposed method uses a gait segmentation based on the continuous wavelet transform (CWT) that provides only a rough estimation of motionless periods defining relevant local acceleration signals. The second step uses the CWT and a novel piecewise-linear fitting technique to accurately extract, from these local acceleration signals, gait events, each labelled as heel strike (HS), toe strike (TS), heel-off (HO), toe-off (TO), or heel clearance (HC). A stride-by-stride validation of these extracted gait events was carried out by comparing the results with reference data provided by a kinematic 3D analysis system (used as gold standard) and a video camera. The temporal accuracy ± precision of the gait events were for HS: 7.2 ms ± 22.1 ms, TS: 0.7 ms ± 19.0 ms, HO: −3.4 ms ± 27.4 ms, TO: 2.2 ms ± 15.7 ms, and HC: 3.2 ms ± 17.9 ms. In addition, the occurrence times of right/left stance, swing, and stride phases were estimated with a mean error of −6 ms ± 15 ms, −5 ms ± 17 ms, and −6 ms ± 17 ms, respectively. The accuracy and precision achieved by the extraction algorithm for healthy subjects, the simplification of the hardware (through the reduction of the number of accelerometer units required), and the validation results obtained, convince us that the proposed accelerometer-based system could be extended for assessing pathological gait (e.g., for patients with Parkinson’s disease).
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Paper Nr: 35
Title:

Spontaneous Pupillary Oscillation Signal Analysis Applying Hilbert Huang Transform
Authors:

Fabiola M. Villalobos-Castaldi, José Ruiz-Pinales, Nicolás C. Kemper-Valverde, Mercedes Flores-Flores, Laura G. Ramírez-Sánchez and Metztli G. Ortiz-Hernández
Abstract: This paper proposes a new application of the Hilbert-Huang transform (HHT). Pupillogram recordings were analyzed through the non-traditional HHT to investigate patterns in the time-frequency parameters of Spontaneous Pupillary Oscillation (SPO) signals. The traditional Fourier transform is only useful for linear stationary signals analysis, but the HHT was designed for the analysis of non-linear and non-stationary signals. However, the HHT is a more suitable tool to study SPO signals which are fundamentally non-stationary. The intrinsic properties of the Spontaneous Pupillary Oscillation signals were highlighted by the HHT scheme and the results showed that SPO waves present local and intermittent variations through the time span. The numerical parameters demonstrated that it is a wide inter-subject variation in the intrinsic time-frequency parameters contribution from each yielding mode to the total signal content.
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Paper Nr: 36
Title:

A Simulation-based Methodology to Test and Assess Designs of Mechatronic Neural Interface Systems
Authors:

Samuel Bustamante, Juan C. Yepes, Vera Z. Pérez and Julio C. Correa
Abstract: Neural interface systems (NISs) are widely used in rehabilitation and upper limb prosthetics. These systems usually involve robots, such as robotic exoskeletons or electric arms, as terminal devices. We propose a methodology to assess the feasibility of implementing these kind of neural interfaces by means of an online kinematic simulation of the robot. It allows the researcher or developer to make tests and improve the design of the mechatronic devices when they have not been built yet or are not available. Moreover, it may be used in biofeedback applications for rehabilitation. The simulation makes use of the CAD model of the robot, its Denavit–Hartenberg parameters, and biosignals recorded from a human being. The proposed methodology was tested using surface electromyography signals acquired from the upper limb of a 25-year-old healthy male. Both real-time and prerecorded signals were used. The robot simulated was the commercial robotic arm KUKA KR6. The tests proved that the online simulation can be effectively implemented and controlled by means of a biosignal.
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Paper Nr: 51
Title:

Deviation-based Dynamic Time Warping for Clustering Human Sleep
Authors:

Chiying Wang, Sergio A. Alvarez, Carolina Ruiz and Majaz Moonis
Abstract: In this paper, we propose two versions of a modified dynamic time warping approach for comparing discrete time series. This approach is motivated by the observation that the distribution of dynamic time warping paths between pairs of human sleep time series is concentrated around the path of constant slope. Both versions use a penalty term for the deviation between the warping path and the path of constant slope for a given pair of time series. In the first version, global weighted dynamic time warping, the penalty term is added as a post-processing step after a standard dynamic time warping computation, yielding a modified similarity metric that can be used for time series clustering. The second version, stepwise deviation-based dynamic time warping, incorporates the penalty term into the dynamic programming optimization itself, yielding modified optimal warping paths, together with a similarity metric. Clustering experiments over synthetic data, as well as over human sleep data, show that the proposed methods yield significantly improved accuracy and generative log likelihood as compared with standard dynamic time warping.
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Paper Nr: 54
Title:

Direct Speech Generation for a Silent Speech Interface based on Permanent Magnet Articulography
Authors:

Jose A. Gonzalez, Lam A. Cheah, James M. Gilbert, Jie Bai, Stephen R. Ell, Phil D. Green and Roger K. Moore
Abstract: Patients with larynx cancer often lose their voice following total laryngectomy. Current methods for post-laryngectomy voice restoration are all unsatisfactory due to different reasons: requires frequent replacement due to biofilm growth (tracheo-oesoephageal valve), speech sounds gruff and masculine (oesophageal speech) or robotic (electro-larynx) and, in general, are difficult to master (oesophageal speech and electro-larynx). In this work we investigate an alternative approach for voice restoration in which speech articulator movement is converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of articulatory and audio signals. To capture articulator movement, small magnets are attached to the speech articulators and the magnetic field generated while the user `mouths' words is captured by a set of sensors. Parallel data comprising articulatory and acoustic signals recorded before laryngectomy are used to learn the mapping between the articulatory and acoustic domains, which is represented in this work as a mixture of factor analysers. After laryngectomy, the learned transformation is used to restore the patient's voice by transforming the captured articulator movement into an audible speech signal. Results reported for normal speakers show that the proposed system is very promising.
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Paper Nr: 66
Title:

iFR: Interactively Pose Corrected Face Recognition
Authors:

Simon Nash, Mark Rhodes and Joanna Isabelle Olszewska
Abstract: Although face recognition applications are growing, robust face recognition is still a challenging task due e.g. to variations in face poses, facial expressions, or lighting conditions. In this paper, we propose a new method which allows both automatic face detection and recognition and incorporates an interactive selection of facial features in conjunction with our new pose-correction algorithm. Our resulting system we called iFR successfully recognizes faces across pose, while being computationally efficient and outperforming standard approaches, as demonstrated in tests carried out on publicly available standard datasets.
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Short Papers
Paper Nr: 10
Title:

Estimation of Working Memory Load using EEG Connectivity Measures
Authors:

Sylvie Charbonnier, Raphaelle Roy, Radka Doležalová, Aurélie Campagne and Stéphane Bonnet
Abstract: Working memory load can be estimated using features extracted from the electroencephalogram (EEG). Connectivity measures, that evaluate the interaction between signals, can be used to extract such features and therefore provide information about the interconnection of brain areas and electrode sites. To our knowledge, there is no literature regarding a direct comparison of the relevance of several connectivity measures for working memory load estimation. This study intends to overcome this lack of literature by proposing a direct comparison of four connectivity measures on data extracted from a working memory load experiment performed by 20 participants. These features are extracted using pattern-based or vector-based methods, and classified using an FLDA classifier and a 10-fold cross-validation procedure. The relevance of the connectivity measures was assessed by statistically comparing the obtained classification accuracy. Additional investigations were performed regarding the best set of electrodes and the best frequency band. The main results are that covariance seems to be the best connectivity measure to estimate working memory load from EEG signals, even more so with signals filtered in the beta band. point.
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Paper Nr: 12
Title:

Correlation-based Method for Measuring the Duration of Motor Unit Action Potentials
Authors:

Armando Malanda, Ignacio Rodríguez, Luis Gila, Iñaki García-Gurtubay, Javier Navallas and Javier Rodríguez
Abstract: We present a novel automatic method for measuring the duration of motor unit action potentials (MUAPs) and compare it with two state-of-the-art automatic duration methods on normal and pathological MUAPs. To this end we analyzed 313 EMG recordings from normal and pathological muscles during slight contractions. A “gold standard” of the duration positions (start and end markers) was obtained for each MUAP from the manual measurements determined by two expert electromyographists. The results of the novel method were compared to those obtained by the two automatic methods using the “gold standard” duration measures for the different groups of normal and pathological MUAPs. Several statistical tests were applied and showed that the novel method provided closer duration positions to the “gold standard” and fewer gross aberrant errors than those obtained by the two other methods in the four MUAP groups, being significantly different in many of the cases.
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Paper Nr: 14
Title:

Summary Processing of Radiophysical Complex MRTHR Signals - Multifractal Analisys of the Brain Microwave Radiation and Heart Rate Variability
Authors:

Vladimir Kublanov, Vasilii Borisov and Anton Dolganov
Abstract: The principles of processing signals of Radiophysical complex MRTHR for studying the role of autonomic regulation in the formation of the brain microwave radiation during the treatment process are presented. The feature of this complex is the possibility of registration and analysing the non-stationary short-term time series of the brain microwave radiation and heart rate variability signals. The processing is implemented via the method of multifractal cross-correlation analysis. The results of the fluctuation and cross-correlation Hurst exponent estimations of these signals are shown. The estimates for a group of relatively healthy patients have low levels of systemic discrepancy. For the patients group with ischemic stroke before treatment the systematic discrepancy of estimations are significantly larger than those of healthy patients. After rehabilitation course, the discrepancy between these estimates are reduced.
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Paper Nr: 28
Title:

Smart Monitoring of User’s Health at Home: Performance Evaluation and Signal Processing of a Wearable Sensor for the Measurement of Heart Rate and Breathing Rate
Authors:

Sara Casaccia, Filippo Pietroni, Andrea Calvaresi, Gian Marco Revel and Lorenzo Scalise
Abstract: Nowadays, the monitoring of users’ health status is possible by means of smart sensing devices at low-cost and with high measuring capabilities. Wearable devices are able to acquire multiple physiological and physical waveforms and are equipped with on-board algorithms to process these signals and extract the required quantities. However, the performance of such processing techniques should be evaluated and compared to different approaches, e.g. processing of the raw waveforms acquired. In this paper, the authors have performed a metrological characterization of a commercial wearable monitoring device for the continuous acquisition of physiological quantities (e.g. Heart Rate - HR and Breathing Rate - BR) and raw waveforms (e.g. Electrocardiogram - ECG). The aim of this work is to compare the performance of the on-board processing algorithms for the calculation of HR and BR with a novel approach applied to the raw signals. Results show that the HR values provided by the device are accurate enough (±2.1 and ±2.8 bpm in static and dynamic tests), without the need of additional processing. On the contrary, the implementation of the dedicated processing technique for breathing waveform allows to compute accurate BR values (±2.1 bpm with respect to standard equipment).
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Paper Nr: 37
Title:

Body Location Independent Activity Monitoring
Authors:

Carina Figueira, Ricardo Matias and Hugo Gamboa
Abstract: Human Activity Recognition (HAR) is increasingly common in people’s daily lives, being applied in health areas, sports and safety. Because of their high computational power, small size and low cost, smartphones and wearable sensors are suitable to monitor user’s daily living activities. However, almost all existing systems require devices to be worn in certain positions, making them impractical for long-term activity monitoring, where a change in position can lead to less accurate results. This work describes a novel algorithm to detect human activity independent of the sensor placement. Taking into account the battery consumption, only two sensors were considered: the accelerometer (ACC) and the barometer (BAR), with a sample frequency of 30 and 5 Hz, respectively. The signals obtained were then divided into 5 seconds windows. The dataset used is composed of 25 subjects, with more than 7 hours of recording. Daily living activities were performed with the smartphone worn in 12 different positions. From each window a set of statistical, temporal and spectral features were extracted and selected. During the classification process, a decision tree was trained and evaluated using a leave one user out cross validation. The developed framework achieved an accuracy of 94.5±6.8 %, regardless the subject and device’s position. This solution may be applied to elderly monitoring, as a rehabilitation tool in physiotherapy fields and also to be used by ordinary users, who just want to check their daily level of physical activity.
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Paper Nr: 39
Title:

Sensory Glove and Surface EMG with Suitable Conditioning Electronics for Extended Monitoring and Functional Hand Assessment
Authors:

Giovanni Saggio, Giancarlo Orengo and Alberto Leggieri
Abstract: We propose and evaluate a new method for measuring and discriminating among flexion, extension, abduction and adduction movements of hand fingers. In particular, flex sensors allowed registering flexion-extension movements, whereas data from multi-channel surface electromyography (sEMG) electrodes allowed discriminating adduction-abduction movements of thumb, index and middle fingers. An electronic interface was designed to acquire and pre-process signals feeding a Personal Computer (PC), running indigenously made routines for data recording, visualization and storing. A novel test for repeatability and reproducibility was also proposed and successfully adopted.
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Paper Nr: 43
Title:

Modeling of an Insect Proprioceptor System based on Different Neuron Response Times
Authors:

Daniel Rodrigues de Lima, Michel Bessani, Philip Newland and Carlos Dias Maciel
Abstract: This paper analyzes neuronal spiking signals from the Desert Locust Femorotibial Chordotonal Organ (FeCO). The data comes from records of the insect neuronal response due to external stimulation. We measured the Inter-Spike Interval (ISI) and calculated Transfer Entropy for investigate different FeCO responses. ISI is a technique that measures the time between two spikes; and transfer entropy is a theoretical information measure used to find dependencies and causal relationships. We also use survival functions to assemble FeCO models. Furthermore, this work uses and compares results of two approaches, one with transfer entropy and other with ISI measures. The results indicate evidence to support the existence of more than one type of FeCO neuron.
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Paper Nr: 46
Title:

Comparison of a Sensorized Garment and Activity Trackers with a Mobile Ergospirometry System Concerning Energy Expenditure
Authors:

Sven Feilner, Andreas Huber, Christian Sauter, Dirk Weishäupl, Michael Hettchen, Wolfgang Kemmler, Christian Weigand and Christian Hofmann
Abstract: Energy expenditure is an important parameter during the performance of physical activity. An algorithm is presented calculating the burnt calories by three given parameters: heart rate, respiration rate and movement. These three vital parameters are provided by the FitnessSHIRT system which was developed by the Fraunhofer IIS. A study was performed to compare the calculated values of the energy expenditure with a reference system based on ergospirometry, an on-body monitoring system and two commercially available activity trackers. Compared to the reference system the developed algorithm, based on the parameters derived by the FitnessSHIRT, reaches a deviation of 18.0 % during running and 18.9 % during cycling.
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Paper Nr: 53
Title:

Feasibility Study of Inertial Sensor-based Joint Moment Estimation Method During Human Movements - A Test of Multi-link Modeling of the Trunk Segment
Authors:

Takashi Watanabe and Jun Kodama
Abstract: The conventional method of estimating joint moments needs kinematic data measured with a 3D optical motion measurement system and ground reaction forces measured with force plate. However, the conventional method is limited generally to laboratory use because of the required measurement systems. Therefore, we proposed a convenient method to estimate joint moments from measurements only with inertial sensors for application to clinical evaluation of motor function of paralyzed and elderly subjects. In this paper, multi-link modeling of the trunk was examined for reliable estimation of joint moments only from measured data with inertial sensors attached on the body. Body segment parameters (segment length and mass, center of mass location and moments of inertia) were calculated from anthropometric data. Experimental test with 3 healthy subjects showed that segmented trunk model estimated joint moments better than a rigid trunk model for squat and sit-to-stand movements. The estimation results were not different largely between the 5-link model that modeled the trunk by 3 segments and the 4-link model that modeled the trunk by 2 segments. However, trunk modeling for 4-link model was suggested to be appropriate when the upper and the middle trunk segments of the 5-link model were modeled as one segment.
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Paper Nr: 55
Title:

Evaluation of Gait Parameters Determined by InvestiGAIT against a Reference System
Authors:

Katja Orlowski, Harald Loose, Falko Eckardt, Jürgen Edelmann-Nusser and Kerstin Witte
Abstract: The purpose is to investigate the validity of an inertial-sensor based gait analysis system (InvestiGAIT) consisting of off-the-shelf sensors and an in-house capturing and analyzing software. The gait of five persons with transfermoral limb loss were captured with the inertial system (Shimmer sensors) and the motion capture system (Vicon) integrating two force plates chosen as reference system in this study. Eleven gait parameters are determined from the data of the captured gait sequences. These gait parameters were compared descriptively and statistically using boxplots, Bland-Altman-plots, including the mean of difference (MOD) and the limits of agreement (LoA), the standard error of the mean (SEM), the Wilcoxon test and the Pearson’s correlation coefficient. A complete validity of the gait parameters was not assumed due to the different measurement methods and the impact of the IMU sensor attachment (on the lower shank above the ankle). For the sound and the amputated leg four gait parameters show no significant difference (stride duration, cadence, velocity, stride length). All the other parameters have a p-value smaller than 0.05. Most of the gait parameters have a small MOD, SEM and LoA. These values show a very small absolute difference between the gait parameters of both systems. Based on the results the InvestiGAIT system can be assumed as valid and suitable for follow-up investigations of human gait in research projects or the clinical environment. Nevertheless, further investigations with healthy subjects and a sensor attachment on the subjects’ shoe are planned.
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Paper Nr: 59
Title:

A Novel Method for Disentangling Strategies from Visual Search
Authors:

Vicente Pallarés, Lorena Rami and Laura Dempere-Marco
Abstract: The process of actively scanning a visual scene while looking for something in a cluttered environment is known as visual search. In this work, we show that it is possible to disentangle the strategies pursued by subjects to solve visual tasks by investigating dynamical aspects inherent to eye-tracking data. A novel method is proposed to characterize visual search strategies in a generalized N-dimensional feature domain, which allows us to investigate spatial-temporal aspects of the search as well as the subjects’ reliance on visual cues. In order to validate the proposed method, we have developed an experimental paradigm based on a double conjunction search in which one the visual cues is systematically manipulated, which can induce featurebased strategies in the observers. On the basis of the preliminary evidence presented here, we argue that this characterization of visual search strategies opens new avenues to assess cognitive function and its relation to normal aging.
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Paper Nr: 60
Title:

Estimation of the Average Gait Velocity based on Statistical Stride Parameters of Foot Sensor Data
Authors:

Harald Loose, Katja Orlowski and Laura Tetzlaff
Abstract: The paper deals with the estimation of gait parameters based on data acquired by inertial measurement units (IMU) placed at the middle foot (metatarsus). The developed method described in (Loose and Orlowski, 2015) is robust against a wide spectrum of the gait speed. The gait parameters (stride duration, length, velocity, distance) are calculated stride by stride with excellent quality. This paper is focused on experimental data acquired during walking on treadmill with a speed profile. First the robustness of the method is shown and quantified using statistical characteristics of each speed level and the whole walking distance. Second the determined speed profiles are evaluated against the adjusted speed profile and an alternative camera based measurement. Third the influence of the walking speed on various physical and statistical stride parameters is discussed. Fourth a model to estimate the walking speed as a function of the root mean square of the magnitude of the angular velocity vector is proposed and evaluated. The rms is calculated for the acquired sensor data after stride detection for the whole stride. The proposed method is applicable to any IMU applied to the metatarsus.
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Paper Nr: 64
Title:

Toward a Computational Model of Actin Filament Networks
Authors:

Andrew Schumann
Abstract: Actin is one of the most important proteins responsible for a reaction of cells to external stimuli (stresses). There are monomeric actin or G-actin and polimeric actin or F-actin. Monomers of G-actin are connected into double helical filaments of F-actin by the processes of nucleation, polymerization, and depolymerization. Filaments are of 7-8 nm in diameter. They are of several microns in length. Furthermore, filaments can be organized as complex networks of different forms: unstable bunches (parallel unbranched filaments), trees (branched filaments), stable bunches (cross-linked filaments). Actin filament networks can be considered a natural computational model of cells to perform different responses to different external stimuli. So, in this model we have inputs as different stresses and outputs as formations and destructions of filaments, on the one hand, and as assemblies and disassemblies of actin filament networks, on the other hand. Hence, under different external conditions we observe dynamic changes in the length of actin filaments and in the outlook of filament networks. As we see, the main difference of actin filament networks from others including neural networks is that the topology of actin filament networks changes in responses to dynamics of external stimuli. For instance, a neural network is a sorted triple (N,V,w), where N is the set of neurons/processors, V is a set of connections among neurons/processors, and w is a weight for each connection. In the case of actin filament networks we deal with a variability of filaments/processors. Some new filaments/processors can appear in one conditions and they can disappear in other conditions. The same situation when the computational substratum changes during the time of computations is faced in the so-called swarm computing, e.g. in slime mould computing. In this paper we propose a swarm computing on the medium of actin filament networks.
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Paper Nr: 9
Title:

3D Single Point Imaging Technology for Tracking Multiple Fish
Authors:

Mohammadmehdi Saberioon, Petr Cisar and Jan Urban
Abstract: Image based tracking like video tracking has shown potential in aquaculture behavioural studies in past decade. Image based tracking is allowing to have higher spatial and temporal resolution in compared to most conventional methods such as hand scoring, tagging and telemetry. They also permit to have more information about the environment rather than other methods. Most studies about trajectory are based on tracking in two dimensional (2D) environments; however, organisms are mostly included in three dimensional (3D) environments which influence ecological interactions extensively. Furthermore, in 2D image analysis, occlusion of fish is a frequent problem for analysis of fish tracking and ultimately their behaviour. Recently, new hardware based on single point 3D imaging technology have been developed which can provide 3D single points in real-time by combining a colour video camera, infrared video camera with an infrared projector. The main objective of this study was to develop a multiple fish tracking system in 3D space based on the current available 3D imaging technology. Developed system could accurately (98%) track multiple Tilapia (Oreochromis niloticus) which was freely swimming in an aquarium. This study contributes to feasibility of new sensors to monitor fish behaviours in 3D space.
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Paper Nr: 13
Title:

Smartphone-based Device for Checking Mental Status in Real Time
Authors:

Mayumi Oyama-Higa, Wenbiao Wang, Shigeo Kaizu, Terufumi Futaba and Taira Suzuki
Abstract: In this article, we present a smartphone-based device for checking mental status in real time, which for the first time enables real-time check-up of mental status with a smartphone. With this device, by measuring pulse waves, two important mental health indicators can be visualized at the same time: the largest Lyapunov exponent obtained from non-linear analysis of pulse waves, and the autonomic nerve balance. Before the development of this device, the measurement of these indicators had already been conducted in thousands of experiments, and their relationship with individual’s mental status had been intensively studied in recent years. This device enables users to conduct the measurement and capture the mental status dynamically, without the limitation of place and time. It has the potential application in preventing accidents due to failure of emotional management. The device is convenient to use and cost-effective.
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Paper Nr: 23
Title:

A Simple Algorithm for Topographic ICA
Authors:

Ewaldo Santana, Allan Kardec Barros, Christian Jutten, Eder Santana and Luis Claudio Oliveira
Abstract: A number of algorithms have been proposed which find structures that resembles that of the visual cortex. However, most of the works require sophisticated computations and lack a rule for how the structure arises. This work presents an unsupervised model for finding topographic organization with a very easy and local learning algorithm. Using a simple rule in the algorithm, we can anticipate which kind of structure will result. When applied to natural images, this model yields an efficient code for natural images and the emergence of simple-cell-like receptive fields. Moreover, we conclude that the local interactions in spatially distributed systems and local optimization with norm L2 are sufficient to create sparse basis, which normally requires higher order statistics.
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Paper Nr: 24
Title:

Electrocardiogram Signal Analysing - Delineation and Localization of ECG Component
Authors:

Ouadi Beya, Mohamad-Mazen Hittawe, Nacira Zegadi, Eric Fauvet and Olivier Laligant
Abstract: In this paper, we develop a new approach based on nonlinear filtering scheme (NLFS) on cardiac signal to evaluate a robust single-lead electrocardiogram (ECG) delineation system and waves localization method based on nonlinear filtering approach. This system is built in two phases, in the first phase, we proposed a mathematical model for detecting ECG features like QRS complex peak, P and T-waves onsets and ends from noise free of synthetic ECG signal. Later, we develop a theoretical model to obtain real approach for detecting these features from real noisy ECG signals. Our method has been evaluated on electrocardiogram signals of QT-MIT standard database, the QRS peak achieve sensitivity (Se) of 98.88 and a positive productivity (P+) of 98.43. For P-onset, P-end, T-end evaluations, this approach provides Sensitivity (Se) of 75.16, 71, and 90.7 respectively. Mean and standard deviation have been computed for differences between the automatic and manual annotations.
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Paper Nr: 25
Title:

Nonlinear Model for Complex Neurons in Biological Visual Visions
Authors:

Sasan Mahmoodi and Nasim Saba
Abstract: Complex cells in biological visual vision are well known to be nonlinear. In this paper, it is demonstrated that these nonlinear complex cells can be modelled under some certain conditions by a biologically inspired model which is nonlinear in nature. Our model consists of cascaded neural layers accounting for anatomical evidence in biological early visual visions. In the model proposed in this paper, the axons associated with the complex cells are considered to operate nonlinearly. We also consider the second order interaction receptive maps as directional derivatives of the complex cell's kernel along the direction of orientation tuning. Our numerical results are similar to the biologically recorded data reported in the literature.
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Paper Nr: 27
Title:

Chaos Analysis of Transcranial Doppler Signals for Feature Extraction
Authors:

Ali Ozturk
Abstract: In this study, chaos theory tools were used for feature extraction from Transcranial Doppler (TCD) signals. The surrogates data sets of the TCD signals which were used for the nonlinearity analysis were extracted as the first feature set. The nonlinear cross prediction errors which were used for the stationary analysis were also extracted for the TCD signals as another feature set. The chaotic invariant features like correlation dimension, maximum Lyapunov exponent, recurrence quantification measures etc. give quantitative values of complexity of the TCD signals. The correlation dimension and maximum Lyapunov exponent were already used as features for classification of TCD signals in the literature. As another chaotic feature set, the statistical quantitative values were extracted from the recurrence plots. The correct calculation of the time delay and the minimum embedding dimension is crucial to correctly estimate all of the chaotic features. These two data were calculated via mutual information and false nearest neighbours approaches, respectively. The space-time separation plots were used in order to find the ideal dimension of Theiler window w which is another important value for the correct estimate of chaotic measures. The reconstructed chaotic attractors with 3-D embedding and 1-step time delay represent the visual phase space portrait of the TCD signals. The attractors were also suggested as another candidate feature set.
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Paper Nr: 29
Title:

Detection of P300 based on Artficial Bee Colony
Authors:

Süleyman Abdullah Aytekin and Tuba Kiyan
Abstract: A Brain-Computer Interface (BCI) is a system that allows users to communicate with their environment through cerebral activity. P300 signal, which is used widely in BCI applications, is produced as a response to a stimulus and can be measured in the parietal lobe of the brain. In this paper, an approach which is a swarm intelligence technique, called Artificial Bee Colony (ABC) together with Multilayer Perceptron (MLP) is used for the detection of P300 signals to achieve high accuracy. The system is based on the P300 evoked potential and is tested on four healthy subjects. It has two main blocks, feature extraction and classification. In the feature extraction block, Power Spectrum Density (PSD) is used whereas ABC was employed to train Multi Layer Perceptron (MLP) in the classification part. This method is compared to other methods such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). The best result that is achieved in this work is 99.8%.
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Paper Nr: 38
Title:

Automated Detection of Mind Wandering: A Mobile Application
Authors:

Marcus Cheetham, Cátia Cepeda and Hugo Gamboa
Abstract: There is growing interest in mindfulness-based training of attention. A particular challenge for novices is learning to sustain focused attention while ensuring that the mind does not wander. This paper presents the development of a tool for the automated detection of episodes of mind wandering (MW), on the basis of biosignals, while normal healthy participants engaged in brief mindfulness-based training (BMT) of attention. BMT required five 20-minute training sessions on consecutive days and entailed practice of breath-focused attention, a typical exercise in mindfulness-based techniques of stress-reduction. Heart rate, respiratory rate, electrodermal and electromyographic activity were measured, and participants pressed a button to indicate the subjective detection of MW during training. The data showed that BMT did not influence our measures of stress but BMT was effective in reducing the frequency of subjectively detected MW events. The algorithm for offline detection of MW achieved an accuracy of 85%. Based on this algorithm, a mobile application was developed for automated MW detection in real-time. The application requires the use of easily placeable sensors, provides a new approach to the real-time MW detection, and could be developed further for use in MW-related investigations and interventions (such as mindfulness-based training of focused attention).
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Paper Nr: 40
Title:

Hybrid SSVEP/P300 BCI Keyboard - Controlled by Visual Evoked Potential
Authors:

Felipe Alberto Capati, Rodrigo Prior Bechelli and Maria Claudia F. Castro
Abstract: This paper presents a two stage Brain Computer Interface (BCI) keyboard system that consumes Electroencephalography (EEG) signals based on two evoked potential detection methods: P300 and Steady-State Visual Evoked Potential (SSVEP). In order to develop a practical daily use EEG system, signals were captured with a standard low cost Emotiv-EPOC system and processed using OpenViBE platform. Fast Fourier Transform (FFT) and sample average were used as feature extraction methods while Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) were used as classifiers.
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Paper Nr: 44
Title:

Application of Myo Armband System to Control a Robot Interface
Authors:

Gabriel Doretto Morais, Leonardo C. Neves, Andrey A. Masiero and Maria Claudia F. Castro
Abstract: This paper discusses the application of myoelectric signals to control electronic devices aiming the development of a digital controlling interface with Myo Gesture Control Armband System. Through this interface it is possible to control the movement of a robot and its interaction with the environment, in this case the robot being PeopleBot, a robot designed for home necessities. Thus, allowing an assessment on the operation of controlling devices with myoelectric signals and Inertial Measurement Unit (IMU), the advantages and disadvantages of working with this technology are discussed.
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Paper Nr: 47
Title:

Physics-based and Retina-inspired Technique for Image Enhancement
Authors:

Mohamed Sedky, Ange A. Malek Aly and Tomasz Bosakowski
Abstract: This paper develops a novel image/video enhancement technique that integrates a physics-based image formation model, the dichromatic model, with a retina-inspired computational model, multiscale model of adaptation. In particular, physics-based features (e.g. Power Spectral Distribution of the dominant illuminant in the scene and the Surface Spectral Reflectance of the objects contained in the image are estimated and are used as inputs to the multiscale model for adaptation. The results show that our technique can adapt itself to scene variations such as a change in illumination, scene structure, camera position and shadowing and gives superior performance over the original model.
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Paper Nr: 58
Title:

Guess the Number - Applying a Simple Brain-Computer Interface to School-age Children
Authors:

Lukáš Vařeka, Tomáš Prokop, Jan Štěbeták and Roman Mouček
Abstract: Although research into brain-computer interfaces is more common in recent years, studies concerning large groups of specific subjects are still lacking. This paper describes a simple brain-computer interface (BCI) experiment that was performed on a group of over 200 school-age children using the technique and methods of event related potentials. In the first phase, experimental data were recorded in various elementary and secondary schools, mainly in the Pilsen region of the Czech Republic. The task was to guess the number between 1 and 9 that the measured subject thinks on. Concurrently, a human expert made a decision about the target number based on averaged P300 waveforms observed on-line. In the second phase, an application for automatic classification was developed for off-line data. A small subset of the data was used for training; the rest of the data was used to evaluate the accuracy of classification. Two feature extraction methods were compared; subsampling and discrete wavelet transform for feature extraction. Multi-layer perceptron was used for classification. The human expert achieved the accuracy of 67.6%, while some of the automatic algorithms were able to significantly outperform the expert; the maximum classification accuracy reached 77.2%.
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Paper Nr: 63
Title:

Circadian Dynamics of High Frequency Oscillations in Patients with Epilepsy
Authors:

Jirí Balach, Petr Jezdik, Radek Janca, Roman Cmejla, Pavel Krsek, Petr Marusic and Premysl Jiruska
Abstract: High frequency oscillations (HFOs) are novel biomarker of epileptogenic tissue. HFOs are currently used to localize the seizure generating areas of the brain, delineate the resection and to monitor the disease activity. It is well established that spatiotemporal dynamics of HFOs can be modified by sleep-wake cycle. In this study we aimed to evaluate in detail circadian and ultradian changes in HFO dynamics using techniques of automatic HFO detection. For this purpose we have developed and implemented novel algorithm to automatic detection and analysis of HFOs in long-term intracranial recordings of six patients. In 5/6 patients HFO rates significantly increased during NREM sleep. The largest NREM related increase in HFO rates were observed in brain areas which spatially overlapped with seizure onset zone. Analysis of long-term recording revealed existence of ultradian changes in HFO dynamics. This study demonstrated reliability of automatic HFO detection in the analysis of long-term intracranial recordings in humans. Obtained results can foster practical implementation of automatic HFO detecting algorithms into presurgical examination, dramatically decrease human labour and increase the information yield of HFOs.
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