Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface

Sánchez-Reolid, Roberto; García, Arturo S.; Vicente-Querol, Miguel Á.; Fernández-Aguilar, Luz; López, María Teresa; González, Adrián Martínez

doi:10.3390/electronics7120384

Cited by 45 publications

(22 citation statements)

References 23 publications

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“…For further studies, we will also asset our result with other artificial neural networks [38], [48].…”

Section: Discussionmentioning

confidence: 98%

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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“…For further studies, we will also asset our result with other artificial neural networks [38], [48].…”

Section: Discussionmentioning

confidence: 98%

3D Input Convolutional Neural Networks for P300 Signal Detection

Oralhan

2020

IEEE Access

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“…Artificial neural networks represent a simple way to mimic the neural system of the human brain, in which, through various samples-in this case, the training samples-one can recognize data which were previously unseen, and make decisions and solve problems regarding the spatial relationship/association between input variables and the presence or absence of a certain phenomenon [34,67,68]. An MLP is based on the backpropagation algorithm-a supervised learning technique [66,69]. The neurons, represented by the variables/factors used in the analysis, are known as "input layers" and are connected to the "hidden layers" through a neural connection which holds the weights of the hidden layers.…”

Section: Multilayer Perceptron Neural Network (Mlp)mentioning

confidence: 99%

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

Popa

Peptenatu

Drăghici

et al. 2019

Water

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The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies.

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“…It is worth mentioning, finally, that polarity and sentiment detection were also exploited in face analysis [94][95][96][97][98][99], posture analysis [100], brain signals [101], and the behavioral analysis of groups of people [102]. These methodologies were tailored to specific tasks and strictly related to the video domain.…”

Section: Sentiment Analysis: Other Applicationsmentioning

confidence: 99%

A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs

et al. 2019

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Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost.

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Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface

Cited by 45 publications

References 23 publications

3D Input Convolutional Neural Networks for P300 Signal Detection

3D Input Convolutional Neural Networks for P300 Signal Detection

Flood Hazard Mapping Using the Flood and Flash-Flood Potential Index in the Buzău River Catchment, Romania

A Survey on Deep Learning in Image Polarity Detection: Balancing Generalization Performances and Computational Costs

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