Abstract
Commercial Brain Computer Interface applications are currently expanding due to the success of widespread dis-semination of low cost devices. Reducing the cost of a traditional system requires appropriate resources, such as proper software tools for signal processing and characterization. In this paper, a methodology for classifying a set of attention and meditation brain wave signal patterns is presented by means of unsupervised signal feature clustering with batch Self-Organizing Maps (b-SOM) and supervised classification by Support Vector Machine (SVM). Previous research on this matter did not combine both methods and also required an important amount of computation time. With the use of a small square neuron grid by b-SOM and an RBF kernel SVM, a well delimited classifier was obtained. The recognition rate was 70% after parameter tuning. In terms of optimization, the parallel b-SOM algorithm reduced drastically the computation time, allowing online clustering and classification for full length input data.
Highlights
Commercial Brain Computer Interface applications are currently expanding due to the success of widespread dissemination of low cost devices
One of the end goals of this research is to provide a set of classifiers that can be implemented on very low time computational performance in order to be implemented on high end commercial embedded hardware with portability capabilities that could be useful for different available low cost EEG-based commercial based Brain Computer Interface (BCI) tools
Three kinds of classification algorithms have proved to be efficient in the context, such as Support Vector Machines (SVM), dynamic classifiers and combinations of classifiers
Summary
Commercial Brain Computer Interface applications are currently expanding due to the success of widespread dissemination of low cost devices. A methodology for classifying a set of attention and meditation brain wave signal patterns is presented by means of unsupervised signal feature clustering with batch Self-Organizing Maps (bSOM) and supervised classification by Support Vector Machine (SVM) Previous research on this matter did not combine both methods and required an important amount of computation time. One of the end goals of this research is to provide a set of classifiers that can be implemented on very low time computational performance in order to be implemented on high end commercial embedded hardware with portability capabilities (e.g. processors that are used in tablets, with high definition video processing qualifications) that could be useful for different available low cost EEG-based commercial based BCI tools. Combining classifiers outperform single classifier implementations[16]
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call