Real-time analysis on ensemble SVM scores to reduce P300-Speller intensification time

Abstract

Abstract-In most Brain-Computer Interface systems, especially the P300-Speller, there must be a harmonized balance between the accuracy and the spelling time. One major drawback of the classical 36-choice P300-Speller is the slow rate of character elicitation. This paper aims to propose a real-time signal processing method to decrease the spelling time by exploiting the score margins of the ensemble Support Vector Machine classifiers during real-time P300-Speller flashes, rather than just getting the classifiers' highest scores. Our experiments were conducted on a large dataset of the BCI Competition III and resulted in a successful character rate of over 96% with just approximately 15 to 20 seconds for each character spelling session. As compared with the fixed 31.5 seconds of the best original approach of the competition, our proposed method significantly reduces the required spelling time by over 30% while maintaining the desired classification accuracy.Index Terms-EEG signal processing, P300 brain-waves, brain computer interface, brain-wave-controlled applications.

Optimal length-constrained segmentation and subject-adaptive learning for real-time arrhythmia detection

“…We will briefly summarize both SVM and ISVM as black boxes (i.e., with just inputs and outputs without regards to the internal structure) for notational convenience. This method was reported to provide promising result promising results in the context of subject-adaptive brain-computer interface (BCI) [29][30][31][32].…”