Abstract
P300 signal is an endogenous event related potential component. It is mostly elicited from the frontal to parietal brain lobes. Electroencephalography is used for acquiring P300 signal from scalp. P300 signal is used for brain-computer interface systems. P300 based brain-computer interface systems are preferable since they have high overall performance. The most significant overall performance indicator is information transfer rate for P300 based brain-computer interface systems. P300 signal detection accuracy and P300 detection time are using for information transfer rate calculation. Hence, P300 signal classification accuracy is important for getting higher information transfer rate. In this study, it is aimed to investigate P300 detection model for higher classification accuracy. Thus, it is proposed 3-dimensional input convolutional neural network model for P300 detection. Moreover, the proposed model was applied with region based P300 speller which constituted audio and visual stimuli. In experiments, the participants were asked to spell desired words in two sessions which were offline and online session. Linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and the proposed method were compared in both online and offline sessions. It is reached highest average classification accuracy rate with the proposed method in both sessions. According to the online session result, average classification accuracy was 94.22% in 3-dimensional input convolutional neural network model. Furthermore, average information transfer rate was 5.53 bit/min in 3-dimensional input convolutional neural network model. We have also applied methods on BCI competition III-dataset II for 2 participants “A” and “B” for evaluating performance of algorithms. The proposed method had higher classification accuracy rate than linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and multi-classifier convolutional neural network which was used in other study on same dataset.
Highlights
Brain-computer interface (BCI) systems can be used by healthy or disable people for sending commands to external devices without using neuro-muscular system
We proposed a novel 3-dimensional input convolutional neural network (3D input Convolutional Neural Networks (CNNs)) for P300 detection
We used stepwise linear discriminant analysis (SWLDA), Support vector machine (SVM), and 2D input CNN for comparing the P300 detection result with the proposed method in experiment
Summary
Brain-computer interface (BCI) systems can be used by healthy or disable people for sending commands to external devices without using neuro-muscular system. Commands are produced via BCI that operated by brain activities [1]. BCI systems can be defined according to the measurement methods of brain activities. The most commonly used BCI systems are based on EEG. Some of EEG based BCI systems use event-related potential (ERP) to produce commands. ERPs are the pattern of brain activities that can be measured by EEG [7]. In BCI systems, Oddball Paradigm is most commonly used in order to elicit P300 wave [9]. According to Oddball Paradigm both standard stimuli with high probability and target stimuli with low probability are presented to user randomly.
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