An Adaptive P300-Based Online Brain–Computer Interface

Abstract

The P300 component of an event related potential is widely used in conjunction with brain-computer interfaces (BCIs) to translate the subjects intent by mere thoughts into commands to control artificial devices. A well known application is the spelling of words while selection of the letters is carried out by focusing attention to the target letter. In this paper, we present a P300-based online BCI which reaches very competitive performance in terms of information transfer rates. In addition, we propose an online method that optimizes information transfer rates and/or accuracies. This is achieved by an algorithm which dynamically limits the number of subtrial presentations, according to the subject's current online performance in real-time. We present results of two studies based on 19 different healthy subjects in total who participated in our experiments (seven subjects in the first and 12 subjects in the second one). In the first, study peak information transfer rates up to 92 bits/min with an accuracy of 100% were achieved by one subject with a mean of 32 bits/min at about 80% accuracy. The second experiment employed a dynamic classifier which enables the user to optimize bitrates and/or accuracies by limiting the number of subtrial presentations according to the current online performance of the subject. At the fastest setting, mean information transfer rates could be improved to 50.61 bits/min (i.e., 13.13 symbols/min). The most accurate results with 87.5% accuracy showed a transfer rate of 29.35 bits/min.

A Self-Paced P300 Healthcare Brain-Computer Interface System with SSVEP-Based Switching Control and Kernel FDA + SVM-Based Detector

“…The FLDA classifier finds a linear decision boundary having the form of + = 0 to separate two classes [26]. The weight vector in FLDA is determined by…”

“…Therefore, P300 BCIs can provide users with more options than SSVEP ones. However, EEG typically exhibits a very low SNR [26]. Therefore, P300-based BCIs rely on multiple repetitions of a stimulation sequence in order to increase the SNR of the P300 ERP [20], which results in a long EEG recording procedure.…”

“…A boosting approach was applied to the orthogonal least square classifier in the work of Hoffmann et al [27]. Linear discriminant analysis (LDA) method was applied to classify P300 and non-P300 potentials in [26]. The Bayesian LDA (BLDA)-based P300 detector was further used in the different P300 BCIs [28,29].…”

“…LDA has also been used for dimensionality reduction in various pattern recognition problems, such as face recognition [32,33] and electromyography (EMG) classification [34]. In the BCI community, LDA is often employed as a classifier [20,26,[35][36][37]. Recently, LDA has also been introduced to EEG-based emotion recognition as a dimension reduction method [17].…”

A low-cost auditory multi-class brain-computer interface based on pitch, spatial and timbre cues

“…In the past, visual P300 responses have been widely investigated for implementing BCIs [e.g. 13,14], and in particular for creating speller applications [15,[19][20][21]. Similarly, auditory P300 responses have been used for implementing speller applications, e.g.…”