Input interface using event-related potential P3

Abstract

This paper refers to a basic study toward the goal of developing a simple and easy-to-use input interface based on P3 components of visual, event-related potentials. Because contamination from eye movements and eye blinks is a problem, a method for removing eye movement artifacts from electroencephalogram (EEG) signals by applying an independent component analysis un-mixing matrix was proposed and implemented. Input character decisions were executed using a support vector machine (SVM) for judging the P3 existence of a single stimulus. The performances were compared while varying the number of channels of EEG signals, the types of feature vectors, and the ratio of the number of data used for training the SVM. The results indicated that three EEG signal channels (Fz, Cz, Pz) were enough to remove artifacts related to eye blinks and vertical eye movements and could be used to make a decision about input characters. The number of trials necessary to decide the input characters was ten on average. The best ratio achieved for the number of training data of targets and non-targets was 1∶2. These results should be confirmed using a larger number of data sets.

Preliminary Investigation of the Passively Evoked N400 as a Tool for Estimating Speech-in-Noise Thresholds

“…It is unlikely that an N400-based SIN thresholding process will be realized in the clinic so long as the N400 must be derived using conventional signal averaging. If, however, the the single trial approach to ERP detection that is used for brain-computer interfaces (e.g., Boutani & Ohsuga, 2012;Chen, Guan, & Liu, 2011;Guo, Gao, Modulation of Passive N400 by SNR 27 & Hong, 2010;Heinrich, Dickhaus, Rothenberger, Heinrich, & Moll, 1999;Iyer & Zouridakis, 2007;Zou, Zhang, Yang, & Zhou, 2010) advances to the point of clinical utility, this may become a possibility. In the absence of such innovations, testing at a single level and using the electrophysiological response as a predictor of the SNR50, as in ongoing research using the N1 (Billings et al, 2013;Billings et al, 2015), is one among a variety of conceivable practical approaches to reduce the length of the test while using signal-averaging.…”