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

RFNet: Riemannian Fusion Network for EEG-based Brain-Computer Interfaces

This paper presents the novel Riemannian Fusion Network (RFNet), a deep neural architecture for learning spatial and temporal information from Electroencephalogram (EEG) for a number of different EEG-based Brain Computer Interface (BCI) tasks and applications. The spatial information relies on Spatial Covariance Matrices (SCM) of multi-channel EEG, whose space form a Riemannian Manifold due to the Symmetric and Positive Definite structure. We exploit a Riemannian approach to map spatial information onto feature vectors in Euclidean space. The temporal information characterized by features based on differential entropy and logarithm power spectrum density is extracted from different windows through time. Our network then learns the temporal information by employing a deep long short-term memory network with a soft attention mechanism. The output of the attention mechanism is used as the temporal feature vector. To effectively fuse spatial and temporal information, we use an effective fusion strategy, which learns attention weights applied to embedding-specific features for decision making. We evaluate our proposed framework on four public datasets from three popular fields of BCI, notably emotion recognition, vigilance estimation, and motor imagery classification, containing various types of tasks such as binary classification, multi-class classification, and regression. RFNet approaches the state-of-the-art on one dataset (SEED) and outperforms other methods on the other three datasets (SEED-VIG, BCI-IV 2A, and BCI-IV 2B), setting new state-of-the-art values and showing the robustness of our framework in EEG representation learning

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