The conventional P300 Bel system for character spelling is typically composed of a paradigm that displays flashing characters and a classifier which identifies target characters. Typically a user has to type each character of a word at a time: this spelling process is slow and it can take several minutes to type an entire word. In this work, we propose a new word typing scheme by integrating a word suggestion mechanism via a dictionary search into the conventional P300-based speller. Our new P300-based word typing system consists of an initial character spelling paradigm, a smart dictionary unit to give suggestions of possible words, and the final word selection paradigm to select a word out of the suggestions. Our proposed methodology reduces typing time significantly and makes word typing more convenient. We have tested our system with four subjects and our results demonstrate an average words typing time of 1.66 minute, whereas the conventional took 2.9 minute for the same words.
The conventional P300-based character spelling BCI system consists of a character presentation paradigm and a classification system. In this paper, we propose modifications to both in order to increase the word typing speed and accuracy. In the paradigm part, we have modified the T9 (Text on Nine keys) interface which is similar to the keypad of mobile phones being used for text messaging. Then we have integrated a custom-built dictionary to give word suggestions to a user while typing. The user can select one out of the given suggestions to complete word typing. Our proposed paradigms significantly reduce the word typing time and make words typing more convenient by typing complete words with only few initial character spellings. In the classification part we have adopted a Random Forest (RF) classifier. The RF improves classification accuracy by combining multiple decision trees. We conducted experiments with five subjects using the proposed BCI system. Our results demonstrate that our system increases typing speed significantly: our proposed system took an average time of 1.83 minutes per word, while typing ten random words, whereas the conventional spelling required 3.35 minutes for the same words under the same conditions, decreasing the typing time by 45.37%.
Lately, neuromodulation of the brain is considered one of the promising applications of ultrasound technology in which low-intensity focused ultrasound (LIFU) is used noninvasively to excite or inhibit neuronal activity. In LIFU, one of critical barriers in the propagation of ultrasound wave is the skull, which is known to be highly anisotropic mechanically: this affects the ultrasound focusing, thereby neuromodulation effects. This study aims to investigate the influence of the anisotropic properties of the skull on the LIFU via finite element head models incorporating the anisotropic properties of the skull. We have examined the pressure and stress distributions within the head in LIFU. Our results show that though most of the pressure that reaches to the brain is due to the longitudinal wave propagation through the skull, the normal stress in the transverse direction of the wave propagation has the main role to control the pressure profile inside the brain more than the shear stress. The results also show that the anisotropic properties of skull contribute in broadening the focal zone in comparison to that of the isotropic skull.
Human pose estimation in real-time is a challenging problem in computer vision. In this paper, we present a novel approach to recover a 3D human pose in real-time from a single depth human silhouette using Principal Direction Analysis (PDA) on each recognized body part. In our work, the human body parts are first recognized from a depth human body silhouette via the trained Random Forests (RFs). On each recognized body part which is presented as a set of 3D points cloud, PDA is applied to estimate the principal direction of the body part. Finally, a 3D human pose gets recovered by mapping the principal directional vector to each body part of a 3D human body model which is created with a set of super-quadrics linked by the kinematic chains. In our experiments, we have performed quantitative and qualitative evaluations of the proposed 3D human pose reconstruction methodology. Our evaluation results show that the proposed approach performs reliably on a sequence of unconstrained poses and achieves an average reconstruction error of 7.46 degree in a few key joint angles. Our 3D pose recovery methodology should be applicable to many areas such as human computer interactions and human activity recognition.
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