فصلنامه مهندسی پزشکی زیستی
سال دهم شماره 4 (زمستان 1395)

Steady State Visual Evoked Potentials (SSVEP) have been widely used in development of Brain Computer Interfaces (BCI). However, it is still a research challenge to have visual stimuli which provide strong SSVEP response while produce little eye fatigue. In this study, rectangular, sinosoidal, sawtooth waveforms applied to a LED were compared with sum of two sinusoidals and a frequency modulated waveform to determine the most appropriate visual stimulus for realization of a BCI system. Moreover, circular, ring and anti-phase two rectangular flickers were generated by Cogent toolbox on a laptop screen and compared. Experiments were performed on 12 participants to determine the SSVEP response and eye fatigue corresponding to each of these visual stimuli. Experiments with the waveforms demonstrate that sum of two sine waves generated significantly lower SSVEP amplitude, but the responses for other four waveforms were not significantly different. On the other hand, the frequency modulated waveform resulted in the least eye fatigue significantly lower from other waveforms. Therefore, considering both criteria, frequency modulated waveform can provide superor performance in a BCI system with an average response of 17.3 pV2 and 1.58 fatigue level in a 1-4 fatigue scale. Experiments with visual stimuli on LCD showed that circular stimuli provided highest and anti-phase rectangular the lowest response. But all of them produced high levels of eye fatigue. Although, Circular stimuli had the highest power (26.7pV2) but due to its related high eye fatigue (3.8) it is not recommended for practical applications. In conclusion it is recommended to use frequency modulated visual stimuli for development of practical BCI systems to satisfy both strong response and low eye fatigue criteria.

Detecting lesion borders is the first step for intelligent lesion identification in dermoscopy, therefore it may influence the accuracy and validity of the next steps of this process. Unfortunately, extracting borders is hampered by some challenges such as losses associated with irregular borders, poor contrast, and artifacts encountered in some area. In this paper, the improved version of energy function optimization technique is introduced in order to separate the skin and lesions in the processing of dermoscopy images. This technique is based on the concept of radial directions in the contour development process, which reduces the sensitivity of estimating the boundaries of lesions to the above constraints. The performance of the proposed method is evaluated on a dataset of dermoscopy images which are captured from various lesions with different sizes and boundaries. The obtained results of the proposed method are compared with some other state-of-the-art lesion detection methods by using standard parameters. Increased True Detection Rate by 6.17% in parallel with decrease in Hammoud Distance by 2.3%, both compared to the best among alternative methods shows the effectiveness of the proposed scheme in detecting lesion borders of dermoscopy images.

In this paper, we introduce a novel framework for illustrating the cardiac movements in echocardiogarphic images by utilizing temporal information and sparse representation. For the proposed method, we first derived temporal information by extracting intensity variation time curves (IVTC) assessed for each pixel. Then an over complete dictionary based on prior knowledge of the temporal signals and a set of pre-specified known functions was designed. The IVTCs can then be described as linear combinations of a few prototype atoms in the dictionary. We used the Bayesian Compressive Sensing (BCS) sparse recovery algorithm to find the sparse coefficients of the signals. By decomposing the IVTCs to different families and extracting proper features based on the sparse information, we attain the color coded images which illustrates the general movements of cardiac segments. The database consists of 21 echocardiography sequence of normal and abnormal volunteers in short axes and 4 chamber views. The results show the great achievement in global wall motion estimations.

 The Stiffness of human could have the substantial effect on motion efficiency by the influence in Stretch-Shortening cycle. The purpose of this study was to investigate the relationship between mechanics of the negative and positive phases of hopping; to identify the effect of stiffness and stretch shortening cycle on the optimal performance of the cyclic movements.  To calculate the leg stiffness, knee and ankle joint stiffness and also angular velocity of the 30 young male with the age range of 18-33 and weight range of 60-88 kg during hopping, we used motion analysis and force platform system.  the results of the Principle component analysis showed that all of the ankles related parameters were classified on the first PC.  Pearson correlation coefficient also showed that there are a significant relationship between joint stiffness, negative and positive mechanical energy and angular velocity (P<0.05, P<0.01).  Due to results of this study, it seems that during cyclic motions, the common mechanism for performing strength and power activity is not useful for having the maximum advantage from stretch shortening cycle, though the ankle joint stiffness reduction is an efficient way to decrease required energy for performing these activities.

P300 Speller as a most commonly used brain–computer interface (BCI) has been able to provide simple communication capabilities for people with severe motor or speech disabilities in order to have a better interaction with the outer world over the past years. Checker-board paradigm introduced by Townsend et al. [1] is one of the most practical alternatives for row-column paradigm, enhancing the performance of the speller by preventing row-column induced errors. In this study, we investigated the effect of substituting presentation of an emoji stimulus instead of flashing the characters in the performance of a checker-board P-300 speller. The performance of the proposed paradigm was evaluated and compared to the traditional stimuli in checker-board paradigm in an online experiment over ten healthy subjects. For each paradigm, the recorded data from an offline session was used to calibrate the speller classifier; and consequently, the classification accuracy was calculated over online sessions. The proposed paradigm, showed 14% enhancement in classification accuracy with respect to the checker-board paradigm. The results of this study obviously showed that the stimuli obtained by presenting emoji instead of character flashing, effectively improved the speller classification accuracy.

Long bone fracture is the most prevalent traumatic fractures that accures due to the strike and attacted load exertions, which one of them is the butterfly fracture. This type of fracture may happen with sudden and combined forces. Since in this type of fracture, the number of fracture lines is more than other types of fractures, developing a prohibitive method may be usfull. The present paper is aimed to investigate the effects of strain rate and use of fastener on butterfly fracture in bone samples. To this end, invivo sheep metacarpal bone samples were examined in four groups: distinguished based on different strain rates, loading conditions and boundry conditions. The first one underwent pure bending at rate of 20 mm/s. The second group and third group experience combined bending and axial compression at rate of 5 mm/s and 20 mm/s, respectively. Bone samples in the fourth group, however, sustained combined loading of bending and axial compression while their ends had been fixed. Comparison between the first and third groups significantly stated that exerting axial compression increases the number of butterfly fractured samples. Results show that at the higher strain rates, the number of butterfly fracture increases. Constraining the ends of the bone samples, on the other hand, led to dissipate the effects of combined loading and also high strain rate. Furthermore, a considerable accordance was observed based on Pearson Correlation test by amount of 0.947.

Minimum variance beamformer (MVB) and its extensions are most widely used techniques in brain source localization due to their high spatial resolution.  Unfortunately, beacause of using data covariance matrix, these methods often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. For solving this problem, Fast Fully Adaptive (FFA) algorithm was developed a few years ago. This method is a multistage adaptive processing technique drawing its inspiration from the butterfly structure of the Fast Fourier Transform (FFT) and decreasing the data requirement significantly. Unfortunately, the high sensitivity of FFA to data partitioning sequences and also its low performance in low SNRs pose a doubt on using it as a reliable localizer for short time brain activities. In this paper, a preprocessing step is proposed to enhance the FFA method. In this step, the brain is divided into separate areas, the components of each area are determined, the data is projected to each area using components of that area. After that, FFA is applied to the projected data. The performance of the enhanced FFA is compared with FFA method by using simulated ERP and real ERF data. In all simulations, enhanced FFA shows the better performance in terms of localization error (enhancement about 2-10 mm) and spread radius (enhancement about 4-9 mm). In addition, the proposed method for real ERF data shows accurate localization result with the most concentrated power spectrum, compared to FFA approach. It is noteworthy that enhanced FFA offers less sensitivity to data partitioning sequences. Emprical results illustrate that enhanced FFA can be implemented as a reliable method for localizing brain short time activities.