A Convolutional Neural Network Approach for a P300-based Brain-Computer Interface for Disabled and Healthy Subjects

Flores, Christian; Flores, Victor Humberto Orbegoso; Achanccaray, David; Andreu-Pérez, Javier

doi:10.1109/ceec.2018.8674229

Cited by 3 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Flores et al, Sharma et al, and Singh et al used the CNN method with kind of machine learning algorithms. They found that the CNN model outperformed other works [ 26 – 28 ].…”

Section: Introductionmentioning

confidence: 91%

3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User

Oralhan

Khayyat

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

This research was aimed at presenting performance of 3-dimensional input convolutional neural networks for steady-state visual evoked potential classification in a wireless EEG-based brain-computer interface system. Overall performance of a brain-computer interface system depends on information transfer rate. Parameters such as signal classification accuracy rate, signal stimulator structure, and user task completion time affect information transfer rate. In this study, we used 3 types of signal classification methods that are 1-dimensional, 2-dimensional, and 3-dimensional input convolutional neural network. According to online experiment with using 3-dimensional input convolutional neural network, we reached average classification accuracy rate and average information transfer rate as 93.75% and 58.35 bit/min, respectively. This both results significantly higher than the other methods that we used in experiments. Moreover, user task completion time was reduced with using 3-dimensional input convolutional neural network. Our proposed method is novel and state-of-art model for steady-state visual evoked potential classification.

show abstract

“…Flores et al, Sharma et al, and Singh et al used the CNN method with kind of machine learning algorithms. They found that the CNN model outperformed other works [ 26 – 28 ].…”

Section: Introductionmentioning

confidence: 91%

3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User

Oralhan

Khayyat

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…In this current study, we wanted to research whether our proposed model outperform compared to other methods which were widely using in P300 detection or not. Besides, in literature there are some researches that compared the CNN models between machine learning algorithms for P300 detection [34]- [37]. Moreover Liu et.…”

Section: Discussionmentioning

confidence: 99%

“…Flores et al compared the CNN model with various machine learning methods. Their results with CNN model outperformed other related works [37]. Liu et al developed novel a CNN by combining Batch Normalization and Droupout methods.…”

Section: Introductionmentioning

confidence: 91%

3D Input Convolutional Neural Networks for P300 Signal Detection

Oralhan

2020

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Transformer-based spatial-temporal feature learning for P300

Zhang

Song

et al. 2022

2022 16th ICME International Conference on Complex Medical Engineering (CME)

View full text Add to dashboard Cite

A Convolutional Neural Network Approach for a P300-based Brain-Computer Interface for Disabled and Healthy Subjects

Cited by 3 publications

References 20 publications

3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User

3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User

3D Input Convolutional Neural Networks for P300 Signal Detection

Transformer-based spatial-temporal feature learning for P300

Contact Info

Product

Resources

About