Abdennaceur Kachouri

A comparison of methods for separation of transient and oscillatory signals in EEG

Brain oscillations constitute a prominent feature of electroencephalography (EEG), in both physiological and pathological states. An efficient separation of oscillation from transient signals in EEG is important not only for detection of oscillations, but also for advanced signal processing such as source localization. A major difficulty lies in the fact that filtering transient phenomena can lead to spurious oscillatory activity. Therefore, in the presence of a mixture of transient and oscillatory events, it is not clear to which extent filtering methods are able to separate them efficiently. The objective of this study was to evaluate methods for separating oscillations from transients. We compared three methods: finite impulse response (FIR) filtering, wavelet analysis with stationary wavelet transform (SWT), time-frequency sparse decomposition with Matching Pursuit (MP). We evaluated the quality of reconstruction and the results of automatic detection of oscillations intermingled with transients. The emphasis of our study was on epileptic signals and single channel processing. In both simulations and on real data, FIR performed generally worse than the time-frequency methods. Both SWT and MP showed good results in separation and detection, each method having its advantages and its limitations. The SWT had good results in separation and detection of transients due to the time invariance property, but still did not completely resolve the frequency overlap for the oscillation during the time-frequency thresholding. The MP provides a sparse representation, and gave good results for simulated data. However, in the real data, we observed distortions introduced by the subtractive approach, and departure from dictionary waveforms. Future directions are proposed for overcoming these limitations.

Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis.

A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse representation of EEG segments obtained by the recently introduced discrete tunable Q-factor wavelet transform (TQWT). Tuning the Q-factor provides a convenient and elegant tool to naturally decompose the signal into an oscillatory and a transient component. The actual detection step relies on thresholding (i) the transient component to reveal K-complexes and (ii) the time-frequency representation of the oscillatory component to identify sleep spindles. Optimal thresholds are derived from ROC-like curves (sensitivity vs. FDR) on training sets and the performance of the method is assessed on test data sets. We assessed the performance of our method using full-night sleep EEG data we collected from 14 participants. In comparison to visual scoring (Expert 1), the proposed method detected spindles with a sensitivity of 83.18% and false discovery rate (FDR) of 39%, while K-complexes were detected with a sensitivity of 81.57% and an FDR of 29.54%. Similar performances were obtained when using a second expert as benchmark. In addition, when the TQWT and MCA steps were excluded from the pipeline the detection sensitivities dropped down to 70% for spindles and to 76.97% for K-complexes, while the FDR rose up to 43.62 and 49.09%, respectively. Finally, we also evaluated the performance of the proposed method on a set of publicly available sleep EEG recordings. Overall, the results we obtained suggest that the TQWT-MCA method may be a valuable alternative to existing spindle and K-complex detection methods. Paths for improvements and further validations with large-scale standard open-access benchmarking data sets are discussed.

Efficient and secure chaotic S-Box for wireless sensor network

Information security using chaotic dynamics is a novel topic in the wireless sensor network WSN research field. After surveying analog and digital chaotic security systems, we give a state of the art of chaotic S-Box design. The substitution tables are nonlinear maps that strengthen and enhance block crypto-systems. This paper deals with the design of new dynamic chaotic S-Boxes suitable for implementation on wireless sensor nodes. Our proposed schemes are classified into two categories: S-Box based on discrete chaotic map with floating point arithmetic cascading piecewise linear chaotic map and a three-dimensional map and S-Box based on discrete chaotic map with fixed-point arithmetic using discretized Lorenz map and logistic-tent map. The security analysis and implementation process on WSN are discussed. The proposed methods satisfy Good S-Box design criteria and exceed the performance of Advanced Encryption Standard static S-Box in some cases. The energy consumption of different proposals and existing chaotic S-Box designs are investigated via a platform simulator and a real WSN testbed equipped with TI MSP430f1611 micro-controller. The simulations and the experimental results show that our proposed S-Box design with fixed-point arithmetic Lorenz map has the lowest energy-consuming profile compared with the other studied and proposed S-Box design. Copyright

Using a Kinect WSN for home monitoring: Principle, network and application evaluation

Tracking people and gesture recognition become an application increasingly exploited for control and supervision especially with the invasion of networks and communication systems. In this paper we propose a wireless sensor network using Kinect video sensor to control and supervise the state of ill or old person into a smart home. We discuss the use of a WiFi network with mesh topology and the application of a routing protocol like OLSR or 802.11s standard. For each node, we investigate the response time and the link quality. We try to transport Kinect data in our network using USB over IP encapsulation. Results show that it is difficult to disseminate data from the sensor in the mesh WiFi network because of the high flow rate required which is about 180Mbits/s and open prospects for using another wireless standard.

On dynamic chaotic S-BOX

The substitution table or S-Box is considered as the core of the block ciphers. The good design of the S-Box can increase the cipher security and simplicity.

Assisting people with disabilities through Kinect sensors into a smart house

The control of patients and persons with disabilities has always been a major concern in hospitals as in homes. Research works in this field aim to control and surround patients with comfort, safety in their homes while respecting their privacy. In this paper, we propose a monitoring system based on Kinect sensors to control and monitor elderly people into a smart house. The system recognizes gestures and communicates them through a network. We test some hand gestures and how it can be recognized with this sensor.

Mapping the dynamic repertoire of the resting brain

The resting state dynamics of the brain shows robust features of spatiotemporal pattern formation but the actual nature of its time evolution remains unclear. Computational models propose specific state space organization which defines the dynamic repertoire of the resting brain. Nevertheless, methods devoted to the characterization of the organization of brain state space from empirical data still lack and thus preclude comparison of the hypothetical dynamical repertoire of the brain with the actual one. We propose here an algorithm based on set oriented approach of dynamical system to extract a coarse-grained organization of brain state space on the basis of EEG signals. We use it for comparing the organization of the state space of large-scale simulation of brain dynamics with actual brain dynamics of resting activity in healthy subjects. The dynamical skeleton obtained for both simulated brain dynamics and EEG data depicts similar structures. The skeleton comprised chains of macro-states that are compatible with current interpretations of brain functioning as series of metastable states. Moreover, macro-scale dynamics depicts correlation features that differentiate them from random dynamics. We here propose a procedure for the extraction and characterization of brain dynamics at a macro-scale level. It allows for the comparison between models of brain dynamics and empirical measurements and leads to the definition of an effective coarse-grained dynamical skeleton of spatiotemporal brain dynamics.

Developement of Matlab-based Graphical User Interface (GUI) for detection of high frequency oscillations (HFOs) in epileptic patients

High-Frequency Oscillations (HFOs) in the 80-500 Hz band are important biomarkers of epileptogenic brain areas and could have a central role in the process of epileptogenesis and seizure genesis. Visual marking of HFOs is highly time consuming and tedious especially for long electroencephalographic (EEG) recordings. Automated HFO detection methods are potentially more efficient, repeatable and objective. Therefore, numerous automatic HFOs detection methodshave been developed. To evaluate and compare the performance of these algorithms in an intuitive and user-friendly framework accessible to researchers, neurologists and students, it is useful to implement the various methodsusing a dedicated Graphical User Interfaces (GUI). In this paper we describe a GUI-based tool that contains three HFOs detection methods. It allows the user to test and runthree different methods based respectively on FIR filter, Complex MORLET Wavelet and matching pursuit (MP). We also show how the GUI can be used to measure the performance of each method. Generally, high sensitivity entrains high false-positive detection rates. For that, the developed GUI contains a supplementary module that allows an expert(e.g. neurologist) to reject false detected events and only save the clinically relevant (true) events. In addition, the GUI presented here can be used to perform classification, as well as estimation of duration, frequency and position of different events. The presented software is easy to use and can easily be extended to include further methods. We thus expect it to become a valuable clinical tool for diagnosis of epilepsy and research purposes.

A Transient Signal Extraction Method of WO 3 Gas Sensors Array to Identify Polluant Gases

Electronic nose is a system, which can determine the fingerprint of gas sample by a sensor array coupled to pattern recognition system. In this paper, a sensor array based on WO3 gas sensor has been described, and a feature extraction technique, including integral and primary derivative, is reported, which leads to higher classification performance as compared to the classical features: fractional resistance change ARs and ARf . The sensor array has been exposed to ozone, ethanol, acetone, and a mixture of ozone and ethanol while keeping the temperature constant. The data array has been normalized and auto scaled then analyzed with the principal component analysis. Results indicate that successful classifications have been gotten in the discrimination of three kinds of oxidizing and reducing gas with the proposed feature extraction method using support vector machine which shows that 97.5% were correctly discriminated with integral and primary derivate comparing the classical variable with 85%. The results of data analysis implied that coupling the extracted features in the same database would be interesting and more robust then the standards features.

Meet Spinky: An Open-Source Spindle and K-Complex Detection Toolbox Validated on the Open-Access Montreal Archive of Sleep Studies (MASS)

Sleep spindles and K-complexes are among the most prominent micro-events observed in electroencephalographic (EEG) recordings during sleep. These EEG microstructures are thought to be hallmarks of sleep-related cognitive processes. Although tedious and time-consuming, their identification and quantification is important for sleep studies in both healthy subjects and patients with sleep disorders. Therefore, procedures for automatic detection of spindles and K-complexes could provide valuable assistance to researchers and clinicians in the field. Recently, we proposed a framework for joint spindle and K-complex detection (Lajnef et al., 2015a) based on a Tunable Q-factor Wavelet Transform (TQWT; Selesnick, 2011a) and morphological component analysis (MCA). Using a wide range of performance metrics, the present article provides critical validation and benchmarking of the proposed approach by applying it to open-access EEG data from the Montreal Archive of Sleep Studies (MASS; O’Reilly et al., 2014). Importantly, the obtained scores were compared to alternative methods that were previously tested on the same database. With respect to spindle detection, our method achieved higher performance than most of the alternative methods. This was corroborated with statistic tests that took into account both sensitivity and precision (i.e., Matthew’s coefficient of correlation (MCC), F1, Cohen κ). Our proposed method has been made available to the community via an open-source tool named Spinky (for spindle and K-complex detection). Thanks to a GUI implementation and access to Matlab and Python resources, Spinky is expected to contribute to an open-science approach that will enhance replicability and reliable comparisons of classifier performances for the detection of sleep EEG microstructure in both healthy and patient populations.