Early detection of intention to move, at self-paced voluntary movements from the activities of neural current sources on the motor cortex, can be an effective approach to brain-computer interface (BCI) systems. Achieving high sensitivity and pre-movement negative latency are important issues for increasing the speed of BCI and other rehabilitation and neurofeedback systems used by disabled and stroke patients and helps enhance their movement abilities. Therefore, developing high-performance extractors or beamformers is a necessary task in this regard. In this paper, for the sake of improving the beamforming performance in well reconstruction of sources of readiness potential, related to hand movement, one kind of surface spatial filter (spherical spline derivative on electrode space) and the linearly constrained minimum variance (LCMV) beamformer are utilized jointly. Moreover, in order to achieve better results, the real head model of each subject was created, using individual head MRI, and was used in beamformer algorithm. Also, few optimizations were done on reconstructed source signal powers to help our template matching classifier with detection of movement onset for five healthy subjects. Our classification results show an average true positive rate (TPR) of 77.1% and 73.1%, false positive rate (FPR) of 28.96% and 28.74% and latency of -512.426 ±396. 7ms and - 360.29 ±252. 16 ms from signals of current sources of motor cortex and sensor space respectively. It can be seen that the proposed method has reliable sensitivity and is faster in prediction of movement onset and more reliable to be used for online BCI in future.

In this paper, we introduce mathematical models based on multi-way data construction and analysis with a goal of simultaneously separating and localizing the sources in the brain by analysis of scalp electroencephalogram (EEG) data. we address the problem of EEG source separation and localization through a 3-way tensor analysis. We represent multi-channel EEG data using a third-order tensor with modes: space (channels), time samples and number of segments. Then we demonstrate that multi-way analysis techniques, in particular PARAFAC2, can successfully separate and localize disjoint sources within the brain. Also we used this method for separation of maternal and fetal ECG signals.

Background: Updating the statistical shape model (SSM) used in image guidance systems for the treatment of back pain using pre-op computed tomography (CT) and intraop ultrasound (US) is challenging due to the scarce availability of pre-op images and the low resolution of the two imaging modalities. Methods:A new approach is proposed here to update SSMs based on the sparse representation of the preoperative MRI images of patients as well as CT images, followed by displaying the injection needle and 3D tracking view of the patients' spine.Results: The statistical analysis shows that updating the SSM using the patients' available MRI images (in more than 95% of the cases) instead of CT images (in less than 5%) will help maintain the required accuracy of needle injection based on the evaluation of injection in different parts of the phantom. Conclusion:The results show that using the proposed model helps reduce the dosage and processing time significantly while maintaining the precision required for the pain procedures. K E Y W O R D Sback pain, image guidance systems, preoperative MRI images, statistical shape model

In this paper a new method based on particle filtering for separating and tracking event related-potential (ERP) subcomponents in different trials is presented. The latency and amplitude of each ERP subcomponent is formulated in the state space model. Based on some knowledge about ERP subcomponents, a constraint on the state space variables is provided to prevent the generation of invalid particles and also make use of a small number of particles which are most effective especially in high dimensions. The method is applied on the simulated and real P300 data. The algorithm has the ability of tracking P300 subcomponents i.e. P3a and P3b, in single trials even in the low signal-to-noise ratio situations.