Epilepsy is a medical term which indicates a common neurological disorder characterized by seizures, because of abnormal neuronal activity. This leads to unconsciousness or even a convulsion. The possible etiologies should be evaluated and treated. Therefore, it is necessary to concentrate not only on finding out efficient treatment methods, but also on developing algorithm to support diagnosis. Currently, there are a number of algorithms, especially nonlinear algorithms. However, those algorithms have some difficulties one of which is the impact of noise on the results. In this paper, in addition to the use of fractal dimension as a principal tool to diagnose epilepsy, the combination between ICA algorithm and averaging filter at the preprocessing step leads to some positive results. The combination which improved the fractal algorithm become robust with noise on EEG signals. As a result, we can see clearly fractal properties in preictal and ictal period so as to epileptic diagnosis.

In recent decades, a lot of achievements have been obtained in imaging and cognitive neuroscience of human brain. Brain's activities can be shown by a number of different kinds of non-invasive technologies, such as: Near-Infrared Spectroscopy (NIRS), Magnetic Resonance Imaging (MRI), and ElectroEncephaloGraphy (EEG; Wolpaw et al., 2002; Weiskopf et al., 2004; Blankertz et al., 2006). NIRS has become the convenient technology for experimental brain purposes. The change of oxygenation changes (oxy-Hb) along task period depending on location of channel on the cortex has been studied: sustained activation in the motor cortex, transient activation during the initial segments in the somatosensory cortex, and accumulating activation in the frontal lobe (Gentili et al., 2010). Oxy-Hb concentration at the aforementioned sites in the brain can also be used as a predictive factor allows prediction of subject's investigation behavior with a considerable degree of precision (Shimokawa et al., 2009). In this paper, a study of recognition algorithm will be described for recognition whether one taps the left hand (LH) or the right hand (RH). Data with noises and artifacts collected from a multi-channel system will be pre-processed using a Savitzky–Golay filter for getting more smoothly data. Characteristics of the filtered signals during LH and RH tapping process will be extracted using a polynomial regression (PR) algorithm. Coefficients of the polynomial, which correspond to Oxygen-Hemoglobin (Oxy-Hb) concentration, will be applied for the recognition models of hand tapping. Support Vector Machines (SVM) will be applied to validate the obtained coefficient data for hand tapping recognition. In addition, for the objective of comparison, Artificial Neural Networks (ANNs) was also applied to recognize hand tapping side with the same principle. Experimental results have been done many trials on three subjects to illustrate the effectiveness of the proposed method.

Background: Functional Near-Infrared Spectroscope (fNIRs) is one of the latest technologies which utilize light in the near-infrared range to determine brain activities. Near-infrared technology allows design of safe, portable, wearable, non-invasive and wireless qualities monitoring systems. This indicates that fNIRs signal monitoring of brain hemodynamics can be value in helping to understand brain tasks. In this paper, we present results of fNIRs signal analysis to show that there exist distinct patterns of hemodynamic responses which recognize brain tasks toward developing a Brain-Computer interface.

Abstract-The sedentary lifestyle is becoming popular especially for intellectual work. Although physical inactivity lifestyle may cause many unexpected illnesses, it is complicated to build up a positive lifestyle due to the lacks of reminder systems to manage and monitor physical activities of people. This research represents an effective way for daily activity monitoring using accelerator and gyroscope sensors embedded in a smartphone. Signals were recorded from accelerator and gyroscope sensors while a user wearing the smartphone performs different activities (going downstairs, going upstairs, sitting with the phone in a pocket, driving and putting the phone on the table). The classification algorithms with k-nearestneighbor (kNN) and artificial neural network (ANN) were applied to recognize user's activities. The overall accuracy of recognizing five activities is 74% for kNN and 75.3% for ANN respectively. Based on the activities recognized during the day, users are able to manage their daily activities for a better life.

Methods of contemporary physics are increasingly important for biomedical research. For a multitude of diverse reasons there exists a gap between the practitioners of biomedicine and modern physics methodologies. In this work, the technique of surrogate data has been used as a method to test for the linearity or nonlinearity of biomedical functional near-infrared spectroscopy (fNIRS) signals observing brain activities. Throughout three different surrogate tests, the third-order autocovariance, the asymmetry resulting from time reversal, and the delay vector variance, the dynamic response of brain activities through fNIRS biomedical signals is very likely to be a nonlinear system.Key words: nonlinearity, surrogate, functional near-infrared spectroscopy, biomedical, signal analysis.Nonlinear measures such as correlation dimension, Lyapunov exponents, and nonlinear prediction error are often applied to time series with the intention of identifying the presence of nonlinear, or possibly chaotic behavior. Theiler et al. (1992) have introduced the concept of "surrogate data," which has been extensively used in the context of statistical nonlinearity testing [1,2]. The surrogate data method tests for a statistical difference between a test statistic computed for the original time series and for an ensemble of test statistics computed on linearized versions of the data, the so-called "surrogate data," or "surrogates" for short. In other words, a time series is nonlinear if the test statistic for the original data is not drawn from the same distribution as the test statistics for the surrogates. The surrogate data method was also used to test with respect to the nonstationarity of time series by J. Timmer [3].Neurophysiological and neuroimaging technologies have contributed much to our understanding of normative brain function. Functional magnetic resonance imaging (fMRI) is currently considered the "gold standard" for measuring functional brain activation. The limitations of fMRI relative to fNIRS include the requirement that participants must lie within the confines of the magnet bore, which limits its use for many applications. The readout gradients in the imaging pulse sequences also produce a loud noise [4]. fMRI is also highly sensitive to movement artifact; subject movements on the order of a few millimeters can invalidate the data. And fMRI systems are quite expensive [5].In recent years, functional near-infrared spectroscopy (fNIRS) has been introduced as a new neuroimaging modality with which to conduct functional brain-imaging studies. fNIRS technology uses specific wavelengths of light, introduced at the scalp, to enable the noninvasive measurement of changes in the relative ratios of deoxygenated hemoglobin (deoxy-Hb) and oxygenated hemoglobin (oxy-Hb) during brain activity. A wireless fNIRS system consists of personal digital assistant (PDA) software controlling the sensor circuitry, reading, saving, and sending the data via a wireless network. This technology allows the design of portable, safe, ...