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Convolutional neural networks for P300 signal detection applied to brain computer interface

Riyad Mouad, Khalil Mohammed, Adib Abdellah

Journal of Automation Mobile Robotics and Intelligent Systems

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2020

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Vol. 14, No. 4

58--63

EN

A Brain‐Computer Interface (BCI) is an instrument capa‐ ble of commanding machine with brain signal. The mul‐ tiple types of signals allow designing many applications like the Oddball Paradigms with P300 signal. We propose an EEG classification system applied to BCI using the con‐ volutional neural network (ConvNet) for P300 problem. The system consists of three stages. The first stage is a Spatiotemporal convolutional layer which is a succession of temporal and spatial convolutions. The second stage contains 5 standard convolutional layers. Finally, a lo‐ gistic regression is applied to classify the input EEG sig‐ nal. The model includes Batch Normalization, Dropout, and Pooling. Also, It uses Exponential Linear Unit (ELU) function and L1‐L2 regularization to improve the lear‐ ning. For experiments, we use the database Dataset II of the BCI Competition III. As a result, we get an F1‐score of 53.26% which is higher than the BN3 model.

2

P300 based character recognition using sparse autoencoder with ensemble of SVMs

Kundu Sourav, Ari Samit

Biocybernetics and Biomedical Engineering

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2019

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Vol. 39, no. 4

956--966

EN

In this study, a brain–computer interface (BCI) system known as P300 speller is used to spell the word or character without any muscle activity. For P300 signal classification, feature extraction is an important step. In this work, deep feature learning techniques based on sparse autoencoder (SAE) and stacked sparse autoencoder (SSAE) are proposed for feature extraction. Deep feature provides the abstract information about the signal. This work proposes fusion of deep features with the temporal features, which provides abstract and temporal information about the EEG signal. These deep feature and temporal feature are partially complement of each other to represent the EEG signal. For classification of the EEG signal, an ensemble of support vector machines (ESVM) is adopted as it helps to reduce the classifiers variability. In classifier ensemble system, the score of individual classifier is not at the same level. To transform these scores into a common level, min–max normalization is proposed prior to combining them. Min-max normalization scales the classifiers' score between 0 and 1. The experiments are conducted on three standard public datasets, dataset IIb of BCI Competition II, dataset II of the BCI Competition III and BNCI Horizon dataset. The experimental results show that the proposed method yields better or comparable performance compared to earlier reported techniques.

3

Azure Machine Learning tools efficiency in the electroencephalographic signal P300 standard and target responses classification

Wójcik Grzegorz M., Kawiak Andrzej, Kwasniewicz Lukasz, Schneider Piotr, Masiak Jolanta

Bio-Algorithms and Med-Systems

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2019

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Vol. 15, no. 3

art. no. 20190031

EN

The Event-Related Potentials were investigated on a group of 70 participants using the dense array electroencephalographic amplifier with photogrammetry geodesic station. The source localisation was computed for each participant. The activity of brodmann areas (BAs) involved in the brain cortical activity of each participant was measured. Then the mean electric charge flowing through particular areas was calculated. The five different machine learning tools (logistic regression, boosted decision tree, Bayes point machine, classic neural network and averaged perceptron classifier) from the Azure ecosystem were trained, and their accuracy was tested in the task of distinguishing standard and target responses in the experiment. The efficiency of each tool was compared, and it was found out that the best tool was logistic regression and the boosted decision tree in our task. Such an approach can be useful in eliminating somatosensory responses in experimental psychology or even in establishing new communication protocols with mildly mentally disabled subjects.

4

Multi-objective binary DE algorithm for optimizing the performance of Devanagari script-based P300 speller

Chaurasiya R. K., Londhe N. D., Ghosh S.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 3

422--431

EN

P300 speller-based brain-computer interface (BCI) allows a person to communicate with a computer using only brain signals. In order to achieve better reliability and user continence, it is desirable to have a system capable of providing accurate classification with as few EEG channels as possible. This article proposes an approach based on multi-objective binary differential evolution (MOBDE) algorithm to optimize the system accuracy and number of EEG channels used for classification. The algorithm on convergence provides a set of pareto-optimal solutions by solving the trade-off between the classification accuracy and the number of channels for Devanagari script (DS)-based P300 speller system. The proposed method is evaluated on EEG data acquired from 9 subjects using a 64 channel EEG acquisition device. The statistical analysis carried out in the article, suggests that the proposed method not only increases the classification accuracy but also increases the over-all system reliabil-ity in terms of improved user-convenience and information transfer rate (ITR) by reducing the EEG channels. It was also revealed that the proposed system with only 16 channels was able to achieve higher classification accuracy than a system which uses all 64 channel's data for feature extraction and classification.

5

Classification of tactile event-related potential elicited by Braille display for brain-computer interface

Hori J., Okada N.

Biocybernetics and Biomedical Engineering

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2017

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Vol. 37, no. 1

135--142

EN

To construct brain–computer interface (BCI), an event-related potential (ERP) induced by a tactile stimulus is investigated in this paper. For ERP-based BCI, visual or auditory information is frequently used as the stimulus. In the present study, we focus on tactile sensations to reserve their visual and auditory senses for other activities. Several patterns of mechanical tactile stimulation were applied to the index fingers of both hands using two piezo actuators that were used as a braille display. Human experiments based on the oddball paradigm were carried out. Subjects were instructed to pay attention to unusual target stimuli while avoiding other frequent non-target stimuli. The extracted features were classified by applying stepwise linear discriminant analysis. As a result, an accuracy of 85% and 60% were obtained for 2- and 4-class classification, respectively. The accuracy was improved by increasing the number of electrodes even when short stimulus intervals were used.

6

Improvement of performance of Japanese P300 speller by using second display

Yamamoto Y., Yoshikawa T., Furuhashi T.

Journal of Artificial Intelligence and Soft Computing Research

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2015

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Vol. 5, No. 3

221--226

EN

Brain computer interface (BCI) is a system allows a user to control external devices or to communicate with other people using only his or her thoughts. The P300 speller is one such BCI in which users input letters. For inputting letters via the P300 speller, higher accuracy and shorter input times are needed, especially given densely populated display screens. We propose a new interface with a second display in the P300 speller that the user can switch to and from by selecting the “next” or “back” commands, therby reducing the density of displayed letters and improving the performance of the P300 speller. We show the comparison results in terms of accuracy and input times between the conventional interface and proposed interface.

7

Comparison of performance of different feature extraction methods in detection of P300

Amini Z., Abootalebi V., Sadeghi M. T.

Biocybernetics and Biomedical Engineering

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2013

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Vol. 33, no. 1

3--20

EN

The aim of this paper is to design a pattern recognition based system to detect the P300 component in the EEG trials. This system has two main blocks, feature extraction and classification. In the feature extraction block, in addition to morphological features, some new features including intelligent segmentation, common spatial pattern (CSP) and combined features (CSP + Segmentation) have also been used. Two criteria were used for the feature evaluation. Firstly, a t-test has been applied. Secondly, each of these four groups of features was evaluated by a Linear Discriminant Analysis (LDA) classifier. Afterwards, the best set of features was selected by using Stepwise Linear Discriminant Analysis (SWLDA). In the classification phase, the LDA was used as a linear classifier. The algorithm described here was tested with dataset II from the BCI competition 2005. In this research, the best result for the P300 detection was 97.4% .This result has proven to be more accurate than the results of previous works carried out in this filed.