In this paper an initial analysis is presented for the applicability of the Oculus Rift virtual reality display when used in newly proposed medical assistive system for motor disabled patients. The head-mounted display is used to give the patient a visual representation of the supposed spatial movements to be done along with a set of instructions for the sequence of basic steps. After each step from every exercise an evaluation is done for the degree of completeness by motion analyzing module and decision is made either for repetition or continuing to the next step. In timely manner overall estimation is done over the patient abilities to fulfill the entire program. The embedded sensors return valuable information for the head movement and position of the patient easing the synchronization of the forthcoming instructions. For a given variety of motor tasks the precision of the head set in terms of spatial motion over predetermined time intervals is tested. Positive results are observed and a variety of practical recommendations for using the Oculus Rift display are derived for more efficient usage.

Signals provided by the ElectroEncephaloGraphy (EEG) are widely used in Brain-Computer Interface (BCI) applications. They can be further analyzed and used for thinking activity recognition. In this paper we proposed an algorithm that is able to recognize five mental tasks using 6 channel EEG data. The main idea is to separate the raw EEG signals into several frames and compute their spectrums. Next, a second-order derivative of Gaussian is applied to extract features and an optimum Gaussian kernel parameters grid search is performed with the help of cross-validation. The extracted features are further reduced by Principal Component Analysis. The processed data is utilized to train SVM classifier which is used for mental tasks recognition afterwards. The performance of the algorithm is estimated on publically available dataset. In terms of 5 folds cross-validation we obtained an average of 82.7% recognition rate (accuracy). Additional experiments were conducted using leave-one-out cross-validation where 67.2% correct classification was reported. Comparison to several state-of-the art methods reveals the advantages of the proposed algorithm.

In this paper some aspects are presented for multilingual names database searching enhancement. They should help to find some balance between the execution time and the relevance of the results obtained. Structured form of the information used, two different structures of the database and a general algorithm for the searching are suggested. As experimental results investigation of the groupings of the most popular Romanized Bulgarian own names produced by the Soundex and Daitch-Mokotoff Soundex (D-M Soundex) algorithms are given. Then conclusion is made for their applicability.