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Neural prostheses could help disabled patients of immobility to restore movements by exploiting brain signals. In the past decade, it has been shown great progress in intracortical neural recording techniques and neural signal processing. We review two classes of methods, population vector and maximal likelihood, for decoding the activity of primary motor cortical neurons in a nonhuman primate during...
People tend to get motion sickness on a moving boat, train, airplane, car, or amusement park rides. Many previous studies indicated that motion sickness sometimes led to traffic accidents, so it becomes an important issue in our daily life. In this study, we designed a VR-based motion-sickness platform with a 32-channel EEG system and a joystick which is used to report the motion sickness level (MSL)...
A Brain-Computer Interface (BCI) system provides a convenient way of communication for healthy subjects and subjects who suffer from severe diseases such as amyotrophic lateral sclerosis (ALS). Motor imagery (MI) is one of the popular ways of designing BCI systems. The architecture of many BCI system is quite complex and they involve time consuming processing. The electroencephalography (EEG) signal...
In this paper, the functional connectivity network of motor imagery based on EEG is investigated to understand brain function during motor imagery. In particular, partial directed coherence and directed transfer function measurements are applied to multi-channel EEG data to find out event related connectivity pattern with the direction and strength. The t-test is applied to these connectivity measurements...
A major challenge for Brain Computer Interface systems (BCIs) is dealing with non-stationarity in the EEG signal. There are two types of EEG non-stationarities 1) long-term changes related to fatigue, changes in recording conditions or effects of feedback training which is addressed in classification step and 2) short-term changes related to different mental activities and drifts in slow cortical...
Recent clinical and experimental evidence suggests that the spike-wave discharges (SWD) of absence seizures result from local activity within a hyperexcitable cortical region with rapid generalization through thalamocortical networks. The cortical focus is said to react more strongly to stimulation than other areas. We seek to develop a model which is in agreement with these recent experimental findings...
In this paper, motor imagery electroencephalograph classification problem is investigated and a method which modifies the projection matrix is proposed based on common spatial pattern analysis. Exceptional samples are detected through examining the features generated by the projection matrix in the first place, which are special in terms that the projection matrix in common spatial pattern analysis...
Canonical correlation analysis (CCA) has already been used to develop an on-line Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) system with high performance and stability. In this study, we proposed a fixed-point CCA algorithm which can be implemented in the embedded processors. It allows the implementation of low power-consumption portable BCI systems without PCs...
Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statements, title-page or half title-pages, blank pages, venue maps or other general information relating to the conference that was part of the original...
The study adopts the structure of hierarchical classification to develop an automatic sleep stage classification system using forehead (Fpl and Fp2) EEG signals. The hierarchical classification consists of a preliminary wake detection rule, a novel feature extraction method based on American Academy of Sleep Medicine (AASM) scoring manual, feature selection methods and SVM. After estimating the preliminary...
Although ElectroOculoGraphic (EOG) signals have been intensively used for human-machine interfaces, none of the available eye movement recognition techniques have been objectively compared to each other. In this paper, we propose to compare two widely known techniques (the standard R. Barea (RB) and A. Bulling (AB)'s works) and a Spiking Neural Network based approach. We also suggest several potential...
Living under high stress may be unhealthy. This study explores electroencephalography (EEG) correlated with stressful circumstances by using the task-switching paradigm with feedback information. According to the behavioral and physiological evidence, acute stress created by this paradigm affected the performance of participants. Under stress, the participants responded quickly and inaccurately. The...
This study is to capture the implicit rules of dress collocation by means of neural network modelling and analyses of the trained hidden structure. First, a multi-layer network model is adapted for training, where the input data are features designed by experiments to represent the various dressing styles of our selected nine fashion brands. Then we introduce a technique to display the inner categorization...
Experimental and theoretical approaches aiming at the establishment of neural correlates of higher cognitive functions and awareness have been extensively studied in the past decade. Information-theoretical indices are useful tools in establishing quantitative metrics when analyzing data of cognitive experiments. In this work we report a systematic statistical analysis of multiple runs of ECoG measurements...
Mental rotation processes allow an agent to mentally rotate an image of an object in order to solve a given task, for example to make a decision on whether two objects presented with different rotational orientation are same or different. This article proposes a bio-constrained neural network model that accounts for the mental rotation processes based on neural mechanisms involving not only visual...
Pattern recognition has been widely studied in the field of computational intelligence. However, primates outperform existing algorithms in cognitive tasks without any difficulty and most of current methods lack enough biological plausibility. Inspired by recent biological findings, a spike-timing based computational model is described, in which information is represented by temporal codes with explicit...
Virtual Reality is a useful platform for Brain-Computer Interface (BCI) users as it offers a relatively safe and cost-effective way for BCI users to train and familiarize themselves with using BCI in a virtual environment before using it in a real-world scenario. Hence this paper presents a pilot study of a virtual navigation task, where control signals from a synchronous multi-class motor imagery-based...
Electroencephalograph (EEG) based Braincomputer Interface (BCI) research aims to decode the various movement related data generated from the motor areas of the brain. One of the issues in BCI research is the presence of redundant data in the features of a given dataset, which not only increases the dimensions but also reduces the accuracy of the classifiers. In this paper, we aim to reduce the redundant...
Technological advancements in robotics and cognitive science are contributing to the development of the field of cognitive robotics. Modern robotic platforms are able to exhibit the ability to learn and reason about complex tasks and to follow behavioural goals in complex environments. Nevertheless, many challenges still exist. One of these great challenges is to equip these robots with cognitive...
Distinguishing between literal and metaphorical language is a major challenge facing natural language processing. Heuristically, metaphors can be divided into three general types in which type III metaphors are those involving an adjective-noun relationship (e.g. “dark humor”). This paper describes our approach for automatic identification of type III metaphors. We propose a new algorithm, the Concrete-Category...
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