Classification of two class motor imagery task using Jaya based k-means clustering
- 1 December 2016
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC)
Abstract
The brain-computer interface (BCI), identify brain patterns to translate thoughts into action. The identification relies on the performance of the classifier. In this paper identification of electroencephalogram (EEG) based BCI for motor imagery (MI) task is done through asynchronous approach. Transferring the brain computer interface (BCI) from laboratory state to real time application desires BCI to be functional asynchronously exclusive of any time constraint. In this case, ongoing brains activities are analyzed continuously irrespective of state i.e. control or non-control. The k-means is the most commonly used and one of the best clustering technique. With k-means, there is a possibility of convergence to the local minima. In literature, it is reported that k-mean modified with optimization techniques have able to address this problem by avoiding premature convergence of local minima. In this article, we have used algorithm-specific parameter-less Jaya optimization to select the initial cluster for k-means. Though enhancement in the accuracy is marginal, it is the speed which makes Jaya based k-means to be an obvious choice for the problem under consideration, i.e. classifying motor imagery task. Supremacy of the proposed Jaya based k-means among the considered algorithm is validated through Friedman test.Keywords
This publication has 19 references indexed in Scilit:
- Jaya a novel optimization algorithm: What, how and why?Published by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based-Means ClusteringComputational Intelligence and Neuroscience, 2015
- Asynchronous BCI Based on Motor Imagery With Automated Calibration and Neurofeedback TrainingIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012
- Event-related (De)synchronization (ERD/ERS) during motor imagery tasks: Implications for brain–computer interfacesInternational Journal of Industrial Ergonomics, 2011
- Particle swarm optimization based K-means clustering approach for security assessment in power systemsExpert Systems with Applications, 2011
- Comparative Analysis of Spectral Approaches to Feature Extraction for EEG-Based Motor Imagery ClassificationIEEE Transactions on Neural Systems and Rehabilitation Engineering, 2008
- Single Trial Classification of Motor Imagination Using 6 Dry EEG ElectrodesPLOS ONE, 2007
- Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a TetraplegicComputational Intelligence and Neuroscience, 2007
- Functional brain imaging based on ERD/ERSVision Research, 2001
- K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local OptimalityIEEE Transactions on Pattern Analysis and Machine Intelligence, 1984