P300 detection based on extraction and classification in online BCI

Hutagalung, Sutrisno Salomo; Turnip, Arjon; Munandar, Aris

doi:10.1109/ica.2013.6734042

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…Hutagalung and Munandar worked on an online data using nonlinear principal component analysis (NPCA) as a feature extraction method and backpropagation neural networks as a classification method without any of the down-sampling or averaging preprocessing steps [35].…”

Section: Other Approaches For P300 Classificationmentioning

confidence: 99%

“…If only a single trial is used, the classification accuracy is 60% using the approach that had been implemented by Hutagalung and Munandar [35] and the classification accuracy is equal to or less than 30% for the other two approaches [12] [36]. Nearest training subspace can be found using the smaller value of the calculated principal angles.…”

Section: Other Approaches For P300 Classificationmentioning

confidence: 99%

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EEG subspace analysis and classification using principal angles for Brain-Computer Interfaces

Ashari

Anderson

2014

2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces (CIBCI)

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This dissertation studies and examines different ideas using principal angles and subspaces concepts. It introduces a novel mathematical approach for comparing sets of EEG signals for use in new BCI technology. The success of the presented results show that principal angles are also a useful approach to the classification of EEG signals that are recorded during a BCI typing application. In this application, the appearance of a subject's desired letter is detected by identifying a P300-wave within a one-second window of EEG following the flash of a letter. Smoothing the signals before using them is the only preprocessing step that was implemented in this study. The smoothing process based on minimizing the second derivative in time is implemented to increase the classification accuracy instead of using the bandpass filter that relies on assumptions on the frequency content of EEG. This study examines four different ways of removing outliers that are based on the principal angles and shows that the outlier removal methods did not help in the presented situations.ii One of the concepts that this dissertation focused on is the effect of the number of trials on the classification accuracies. The achievement of the good classification results by using a small number of trials starting from two trials only, should make this approach more appropriate for online BCI applications.In order to understand and test how EEG signals are different from one subject toanother, different users are tested in this dissertation, some with motor impairments. Furthermore, the concept of transferring information between subjects is examined by training the approach on one subject and testing it on the other subject using the training subject's EEG subspaces to classify the testing subject's trials.iii

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Section: Other Approaches For P300 Classificationmentioning

confidence: 99%

Section: Other Approaches For P300 Classificationmentioning

confidence: 99%

EEG subspace analysis and classification using principal angles for Brain-Computer Interfaces

Ashari

Anderson

2014

2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces (CIBCI)

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“…[ 4 5 ] Artificial intelligence methods have been proposed to increase SNR without loss of any considerable information in P300 detection. [ 6 7 ]…”

Section: Introductionmentioning

confidence: 99%

“…[ 7 ] One of the most important techniques to detect P300 is artificial neural network. [ 6 15 16 ] Although usefulness of this idea, its basic drawback is getting stuck in local minimum which degrades its performance in P300 detection. In recent years, studies have demonstrated the potential of deep neural networks in the field of P300 detection.…”

Section: Introductionmentioning

confidence: 99%

A new method for detecting P300 signals by using deep learning: Hyperparameter tuning in high-dimensional space by minimizing nonconvex error function

2018

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Background:P300 signal detection is an essential problem in many fields of Brain-Computer Interface (BCI) systems. Although deep neural networks have almost ubiquitously used in P300 detection, in such networks, increasing the number of dimensions leads to growth ratio of saddle points to local minimums. This phenomenon results in slow convergence in deep neural network. Hyperparameter tuning is one of the approaches in deep learning, which leads to fast convergence because of its ability to find better local minimums. In this paper, a new adaptive hyperparameter tuning method is proposed to improve training of Convolutional Neural Networks (CNNs).Methods:The aim of this paper is to introduce a novel method to improve the performance of deep neural networks in P300 signal detection. To reach this purpose, the proposed method transferred the non-convex error function of CNN) into Lagranging paradigm, then, Newton and dual active set techniques are utilized for hyperparameter tuning in order to minimize error of objective function in high dimensional space of CNN.Results:The proposed method was implemented on MATLAB 2017 package and its performance was evaluated on dataset of Ecole Polytechnique Fédérale de Lausanne (EPFL) BCI group. The obtained results depicted that the proposed method detected the P300 signals with 95.34% classification accuracy in parallel with high True Positive Rate (i.e., 92.9%) and low False Positive Rate (i.e., 0.77%).Conclusions:To estimate the performance of the proposed method, the achieved results were compared with the results of Naive Hyperparameter (NHP) tuning method. The comparisons depicted the superiority of the proposed method against its alternative, in such way that the best accuracy by using the proposed method was 6.44%, better than the accuracy of the alternative method.

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A new method for P300 detection in deep belief networks: Nesterov momentum and drop based learning rate

2018

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P300 detection based on extraction and classification in online BCI

Cited by 4 publications

References 9 publications

EEG subspace analysis and classification using principal angles for Brain-Computer Interfaces

EEG subspace analysis and classification using principal angles for Brain-Computer Interfaces

A new method for detecting P300 signals by using deep learning: Hyperparameter tuning in high-dimensional space by minimizing nonconvex error function

A new method for P300 detection in deep belief networks: Nesterov momentum and drop based learning rate

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