Increasing number of research activities and different types of studies in brain-computer interface (BCI) systems show potential in this young research area. Research teams have studied features of different data acquisition techniques, brain activity patterns, feature extraction techniques, methods of classifications, and many other aspects of a BCI system. However, conventional BCIs have not become totally applicable, due to the lack of high accuracy, reliability, low information transfer rate, and user acceptability. A new approach to create a more reliable BCI that takes advantage of each system is to combine two or more BCI systems with different brain activity patterns or different input signal sources. This type of BCI, called hybrid BCI, may reduce disadvantages of each conventional BCI system. In addition, hybrid BCIs may create more applications and possibly increase the accuracy and the information transfer rate. However, the type of BCIs and their combinations should be considered carefully. In this paper, after introducing several types of BCIs and their combinations, we review and discuss hybrid BCIs, different possibilities to combine them, and their advantages and disadvantages.

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BACKGROUND: Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on regular basis, has the potential to allow diagnosis at early stages. OBJECTIVE: To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans. METHODS: A set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans. RESULTS: The system achieved an average classification accuracy of 86% on the external dataset. CONCLUSIONS: Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.

In cognitive radio networks, the secondary users (SUs) switch the data transmission to another empty spectrum band to give priority to primary users (PUs). In this paper, channel switching in cognitive radio mobile ad hoc networks (CR-MANETs) through an established route is modeled. The probability of channel availability in this route is calculated based on the PU's activity, SU's mobility, and channel heterogeneity. Based on the proposed model, the channel and link availability time are predicted. These predictions are used for channel assignment in the proposed channel allocation scheme. A handoff threshold as well as a life time threshold is used in order to reduce the handoff delay and the number of channel handoffs originating from the short channel usage time. When the channel handoff cannot be done due to the SU's mobility, the local flow handoff is performed to find an appropriate node in the vicinity of a potential link breakage and transfer the current data flow to it. The local flow handoff is performed to avoid possible flow disruption and also to reduce the delay caused by the link breakage. The study reveals that the channel heterogeneity and SU's mobility must be considered as important factors, which affect the performance of the handoff management in the CR-MANETs. The results emphasize on the improvement of the route maintenance probability after using the local flow handoff. It is also stated that the amounts of handoff requirement and handoff delay are decreased after using the predicted channel usage life time and handoff threshold time.