Abstract
This work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. Target EEG epochs and nontarget EEG ones are both mapped to a space by Wavelet. In this space, Fisher Criterion is used to measure the difference between target and nontarget ones. Only a few Daubechies wavelet bases corresponding to big differences are selected to construct a matrix, by which EEG epochs are transformed to feature vectors. To ensure the online experiments, the computation tasks are distributed to several computers that are managed and integrated by Storm so that they could be parallelly carried out. The proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters. Our method achieved higher accuracies of classification and detection. The ITRs also reflected the superiority of our method. The parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers. The average feedback time for one round of EEG epochs was 1.57 ms. The proposed method can improve the performance of P300 Speller BCI. Its parallel computing scheme is able to support fast feedback required by online experiments. The number of repeated stimuli can be significantly reduced by our method. The parallel computing scheme not only supports our wavelet feature extraction but also provides a framework for other algorithms developed for P300 Speller.
Highlights
Academic Editor: Luminita Moraru is work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller
Our method achieved higher accuracies of classification and detection. e ITRs reflected the superiority of our method. e parallel computing scheme of our method was deployed on a small scale Storm cluster containing three desktop computers. e average feedback time for one round of EEG epochs was 1.57 ms. e proposed method can improve the performance of P300 Speller brain-computer interfaces (BCIs)
E EEG-based BCIs could be fulfilled in diverse ways, which mainly include P300 Speller, sensorimotor rhythm (SMR), steady-state visual evoked potential (SSVEP), and slow cortical potential (SCP) [1,2,3,4]. ey rely on different neuroscience principles and have different features
Summary
Academic Editor: Luminita Moraru is work is intended to increase the classification accuracy of single EEG epoch, reduce the number of repeated stimuli, and improve the information transfer rate (ITR) of P300 Speller. E proposed feature extraction was compared with the typical methods by testing its performance of classifying single EEG epoch and detecting characters. E proposed method can improve the performance of P300 Speller BCI. As for ERP estimation, averaging many EEG epochs is the most common practice This method can serve the implementation of P300-based BCI, it faces very big challenges because the stimulus is needed to be repeated many times. It is hard to improve the response time and information transfer rate (ITR) of P300-based BCI if averaging EEG epochs underlies the detection of P300 ERP. Many approaches based on machine learning have been developed for P300 detection in this kind of BCIs
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