The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface

Jin, Jing; Li, Shurui; Daly, Ian; Miao, Yangyang; Chang, Liu; Wang, Xingyu; Cichocki, Andrzej

doi:10.1109/tnsre.2019.2956488

Cited by 95 publications

(41 citation statements)

References 49 publications

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“…Thus, they obtained ten models that were trained by weighted linear discriminant analysis. When new subjects join the experiment, their result showed that new subjects matched the suitable generic model [45]. This method can be used for increasing performance and reducing calibration time in our future studies.…”

Section: Discussionmentioning

confidence: 91%

3D Input Convolutional Neural Networks for P300 Signal Detection

Oralhan

2020

IEEE Access

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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. INDEX TERMS Brain computer interface, deep Learning, human machine systems, P300 detection.

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Section: Discussionmentioning

confidence: 91%

3D Input Convolutional Neural Networks for P300 Signal Detection

Oralhan

2020

IEEE Access

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“…Finally, a further research to improve the system performance of the presented paradigms with images (face and pictures) would be also interesting. These improvements could be related to the type of classification algorithm used (Xiao et al, 2020), the creation of a generic model to decrease the calibration time (Jin et al, 2020), or even the application of hybrid systems which use different type of control signals (Xu et al, 2020).…”

Section: Future Studiesmentioning

confidence: 99%

Performance Analysis With Different Types of Visual Stimuli in a BCI-Based Speller Under an RSVP Paradigm

Ron-Angevín

Medina-Juliá

Fernández-Rodríguez

et al. 2021

Front. Comput. Neurosci.

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Brain-Computer Interface (BCI) systems enable an alternative communication channel for severely-motor disabled patients to interact with their environment using no muscular movements. In recent years, the importance of research into non-gaze dependent brain-computer interface paradigms has been increasing, in contrast to the most frequently studied BCI-based speller paradigm (i.e., row-column presentation, RCP). Several visual modifications that have already been validated under the RCP paradigm for communication purposes have not been validated under the most extended non-gaze dependent rapid serial visual presentation (RSVP) paradigm. Thus, in the present study, three different sets of stimuli were assessed under RSVP, with the following communication features: white letters (WL), famous faces (FF), neutral pictures (NP). Eleven healthy subjects participated in this experiment, in which the subjects had to go through a calibration phase, an online phase and, finally, a subjective questionnaire completion phase. The results showed that the FF and NP stimuli promoted better performance in the calibration and online phases, being slightly better in the FF paradigm. Regarding the subjective questionnaires, again both FF and NP were preferred by the participants in contrast to the WL stimuli, but this time the NP stimuli scored slightly higher. These findings suggest that the use of FF and NP for RSVP-based spellers could be beneficial to increase information transfer rate in comparison to the most frequently used letter-based stimuli and could represent a promising communication system for individuals with altered ocular-motor function.

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“…Therefore, the team used batch normalization [36] technology to normalize the input to accelerate the training speed. We normalize each dimension as equation (16):…”

Section: B、s-eegnet Model Buildingmentioning

confidence: 99%

“…Reference [15] sought to improve the neural network and integrate the weight splitting technology into the algorithm of the Back-Propagation (BP) neural network for EEG recognition and analysis, with the aim of enhancing the classification accuracy. In reference [16], the concept of general model set is introduced, and the model is trained by weighted linear discriminative analysis, which greatly shortens the training time and provides a valuable new strategy for improving the performance of BCI based on P300. The core of these methods is to extract the features of EEG signals effectively to improve the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation

Huang

Xue

et al. 2020

IEEE Access

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As one of the most important research fields in the brain-computer interface (BCI) field, electroencephalogram (EEG) classification has a wide range of application values. However, for the EEG signal, it is difficult for the traditional neural networks to capture the characteristics of the EEG signal more comprehensively from the time and space dimensions, which has a certain effect on the accuracy of EEG classification. To solve this problem, we can improve the accuracy of classification via end-to-end learning of the time and space dimensions of EEG. In this paper, a new type of EEG classification network, the separable EEGNet (S-EEGNet), is proposed based on Hilbert-Huang transform (HHT) and a separable convolutional neural network (CNN) with bilinear interpolation. The EEG signal is transformed into time-frequency representation by HHT, which allows the EEG signal to be better described in the frequency domain. Then, the depthwise and pointwise elements of the network are combined to extract the feature map. The displacement variable is added by the bilinear interpolation method to the convolution layer of the separable CNN, allowing the free deformation of the sampling grid. The deformation depends on the local, dense, and adaptive input characteristics of the EEG data. The network can learn from the time and space dimensions of EEG signals end to end to extract features to improve the accuracy of EEG classification. To show the effectiveness of S-EEGNet, the team used this method to test two different types of EEG public datasets (motor imagery classification and emotion classification). The accuracy of motor imagery classification is 77.9%, and the accuracy of emotion classification is 89.91%, and 88.31%, respectively. The experimental results showed that the classification accuracy of S-EEGNet improved by 3.6%, 1.15%, and 1.33%, respectively.

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The Study of Generic Model Set for Reducing Calibration Time in P300-Based Brain–Computer Interface

Cited by 95 publications

References 49 publications

3D Input Convolutional Neural Networks for P300 Signal Detection

3D Input Convolutional Neural Networks for P300 Signal Detection

Performance Analysis With Different Types of Visual Stimuli in a BCI-Based Speller Under an RSVP Paradigm

S-EEGNet: Electroencephalogram Signal Classification Based on a Separable Convolution Neural Network With Bilinear Interpolation

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