Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Achieving consistent accuracy still big challenge in EEG based Motor Imagery classification since the nature of EEG signal is non-stationary, intra-subject and inter-subject dependent. To address this problems, we propose the feature extraction scheme employing statistical measurements in narrow window with channel instantiation approach. In this study, k-Nearest Neighbor is used and a voting scheme as final decision where the most detection in certain class will be a winner. In this channel instantiation scheme, where EEG channel become instance or record, seventeen EEG channels with motor related activity is used to reduce from 118 channels. We investigate five narrow windows combination in the proposed methods, i.e.: one, two, three, four and five windows. BCI competition III Dataset IVa is used to evaluate our proposed methods. Experimental results show that one window with all channel and a combination of five windows with reduced channel outperform all prior research with highest accuracy and lowest standard deviation. This results indicate that our proposed methods achieve consistent accuracy and promising for reliable BCI systems

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

Approximate credibility intervals on electromyographic decomposition algorithms within a Bayesian framework

This thesis develops a framework to uncover the probability of correctness of algorithmic results. Specifically, this thesis is not concerned with the correctness of these algorithms, but with the uncertainty of their results arising from existing uncertainty in their inputs. This is achieved using a Bayesian approach. This framework is then demonstrated using independent component analysis with electromyographic data. Blind source separation (BSS) algorithms, such as independent component analysis (ICA), are often used to solve the inverse problem arising when, for example, attempting to retrieve the activation patterns of motor units (MUs) from electromyographic (EMG) data. BSS, or similar algorithms, return a result but do not generally provide any indication on the quality of that result or certainty one can have in it being the actual original pattern and not one strongly altered by the noise/errors in the input. This thesis uses Bayesian inference to extend ICA both to incorporate prior physiological information, thus making it in effect a semi-blind source separation (SBSS) algorithm, and to quantify the uncertainties around the values of the sources as estimated by ICA. To this end, this thesis also presents a way to put a prior on a mixing matrix given a physiological model as well as a re-parametrisation of orthogonal matrices which is helpful in pre-empting floating point errors when incorporating this prior of the mixing matrix into an algorithm which estimates the un-mixing matrix. In experiments done using EMG data, it is found that the addition of the prior is of benefit when the input is very noisy or very short in terms of samples or both. The experiments also show that the information about the certainty can be used as a heuristic for feature extraction or general quality control provided an appropriate baseline has been determined