International Conference on Brain Informatics
Articles from this conference
Disinformation in Open Online Media pp 257-266; https://doi.org/10.1007/978-3-030-86993-9_24
Neuroscience is an important area of research due to the nature of the brain and its diseases. Scientists in this field tend to ask complicated questions which are time-consuming to answer and need several resources. Analysing, representing and finally, classifying these questions assist question resolution systems to be able to tackle them more easily. To achieve its objectives, this study contains three different tasks, including an ontology-based question analysis approach to find question dimensions for representing questions and shaping categories for them; and two approaches in classifying questions, including one ontology-based and a set of statistical approaches.
Disinformation in Open Online Media pp 189-198; https://doi.org/10.1007/978-3-030-86993-9_18
From 20 March to 10 May 2020, the “stay at home” countermeasures for the Covid-19 emergency lockdown were defined in the United Kingdom (UK) as leaving home for only the following reasons: “Key worker travelling to work”, “Shopping for basic necessities”, “Any medical need” or “Exercise once a day”. Data collected from the UK Office for National Statistics through online and telephone questionnaires are an exceptional baseline data set on people behaviour during the Covid-19 pandemics. In this paper, data from demographic surveys from the UK are compared to statistical and feedback data from the Virtual Reality app called TRIPP for meditation in the experiences called Focus and Calm. Our data analysis shows that during lockdown the psychological and emotional mindset, severely challenged, has been successfully enhanced with the use of Virtual Reality.
Disinformation in Open Online Media pp 536-547; https://doi.org/10.1007/978-3-030-86993-9_48
Perkinson’s disease is a progressive degenerative disorder that comes from a recognized clinical parkinsonian syndrome. The manifestations of Parkinson’s disease include both motor and nonmotor symptoms identified as tremor, bradykinesia (slowed movements), rigidity, and postural instability. PD is marked as one of the most prevalent disorders from various researches and surveys because it has been observed in 90% of people out of 100. It is imperative to design CAD to develop an advanced model for the determination of this disease with accuracy since up to date there is no accurate clinical intervention for the diagnosis of PD. In contrast to conventional methods. Deep learning convolutional neural network tools are implied for the faster and accurate identification of PD through MRI. The purpose of this research is to contribute to the development of an accurate PD detection method. To conduct the research a public dataset NTU (National Technical University of Athens) is used. The data samples are categorized into three sets (Training, Test, and Validation). A DenseNet integrated with LSTM is applied to the MRI data samples. DenseNet is used to strengthen the feature selection ability, as each layer selects features depending on the temporal closeness of the image. The output is then fed into the LSTM layer, for discovering the significant dependencies in temporal features. The performance of the proposed DenseNet-LSTM is compared to other CNN state-of-the-art models. The proposed model outputs a training accuracy of 93.75%, testing accuracy of 90%, and validation accuracy of 93.8% respectively.
Disinformation in Open Online Media pp 57-66; https://doi.org/10.1007/978-3-030-86993-9_6
Understanding the brain function requires investigating information transfer across brain regions. Shannon began the remarkable new field of information theory in 1948. It basically can be divided into two categories: directed and undirected information-theoretical approaches. As we all know, neural signals are typically nonlinear and directed flow between brain regions. We can use directed information to quantify feed-forward information flow, feedback information, and instantaneous influence in the high-level visual cortex. Moreover, neural signals have bidirectional information flow properties and are not captured by the transfer entropy approach. Therefore, we used directed information to quantify bidirectional information flow in this study. We found that there has information flow between the scene-selective areas, e.g., OPA, PPA, RSC, and object-selective areas, e.g., LOC. Specifically, strong information flow exists between RSC and LOC. It explained that functionally coupled between RSC and LOC plays a vital role in visual scenes/object categories or recognition in our daily lives. Meanwhile, we also found weak reverse-directed information flow in the visual scenes and objects neural networks.
Disinformation in Open Online Media pp 146-156; https://doi.org/10.1007/978-3-030-86993-9_14
In online interactions, users frequently add emojis (e.g., smileys, hearts, angry faces) to text for expressing the emotions behind the communication context, aiming at a better interpretation to text especially of polysemous short expressions. Emotion recognition refers to the automated process of identifying and classifying human emotions. If text-based emoticons (i.e., emojis created by textual symbols and characters) can be directly understood by semantic-based context recognition tools used in the Web and Artificial Intelligence and robotics, image-based emojis need instead image recognition for a complete semantic context interpretation. This study aims to explore and compare systematically different classification models of emoticon pictograms collected from the Internet, with different labels according to the Ekman model of six basic emotions. A first comparison involves supervised machine learning classifiers trained on features extracted through neural networks. In the second phase, the comparison is extended to different deep learning models. Results indicate that deep learning models performed excellent, and traditional supervised algorithms also achieve very promising outcomes.
Disinformation in Open Online Media pp 310-320; https://doi.org/10.1007/978-3-030-86993-9_29
Individualized treatment is crucial for epileptic patients with different types of seizures. The difference among patients impacts the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection could ease the manual time-consuming and labour-intensive procedure for diagnose seizure in the clinical setting. In this paper, we propose a electroencephalography frequency bands selection method that exploits Natural Language Processing (NLP) features from individual’s condition and patients with same seizure types. We used Temple University Hospital (TUH) EEG seizure corpus and conducted experiments with various input data for different seizure types classified using Random Forest (RF) and Support Vector Machine (SVM). The results show that with reduced frequency bands the performance slightly deviates from the whole frequency bands, thus leading to possible resource-efficient implementation for seizure detection.
Disinformation in Open Online Media pp 25-34; https://doi.org/10.1007/978-3-030-86993-9_3
Tacit coordination games are games in which players need to coordinate with one another, for example, on how to divide resources, while they are not allowed to communicate with each other. In divergent interest tacit coordination games, their interests are not always aligned. For instance, player may need to choose between a solution that maximizes their individual profit or a solution that is perceptually more salient to both players, i.e., a focal point, that will increase the chances for successful coordination. The goal of this study was to examine the effect of two key variables, the Expected Revenue Proportions (ERP) and the player's Social Value Orientation (SVO) on the probability of realizing a focal point solution in divergent interest tacit coordination games. Our results show that there is an interaction between the expected payoff and the SVO. For example, prosocial players tend to implement a social point solution although the expected payoff is less than that of their opponent. Thus, the implementation of a focal point depends on other contextual variables such as the SVO and the expected payoff. The main contribution of this work is showing that the probability to choose a focal point solution is affected by the interaction between SVO and the expected revenue of the player. This finding may contribute to the construction of cognitive models for decision making in diverge interest tacit coordination problems.
Disinformation in Open Online Media pp 211-222; https://doi.org/10.1007/978-3-030-86993-9_20
Recent studies have shown that brain lesions following stroke can be probabilistically mapped onto disconnections of white matter tracts, and that the resulting “disconnectome” is predictive of the patient’s behavioral deficits. Disconnectome maps are sparse, high-dimensional 3D matrices that require unsupervised dimensionality reduction followed by supervised learning for prediction of the associated behavioral data. However, the optimal machine learning pipeline for disconnectome data still needs to be identified. We examined four dimensionality reduction methods at varying levels of compression and used the extracted features as input for cross-validated regularized regression to predict the associated language and motor deficits. Features extracted by Principal Component Analysis and Non-Negative Matrix Factorization were found to be the best predictors, followed by Independent Component Analysis and Dictionary Learning. Optimizing the number of extracted features improved predictive accuracy and greatly reduced model complexity. Moreover, the choice of dimensionality reduction technique was found to optimally combine with a specific type of regularized regression (ridge vs. LASSO). Overall, our findings represent an important step towards an optimal pipeline that yields high prediction accuracy with a small number of features, which can also improve model interpretability.
Disinformation in Open Online Media pp 351-365; https://doi.org/10.1007/978-3-030-86993-9_32
The neural recordings in the form of local field potentials offer useful insights on higher-level neural functions by providing information about the activation and deactivation of neural circuits. But often these recordings are contaminated by multiple internal and external sources of noise from nearby electronic systems and body movements. However, to facilitate knowledge extraction from these recordings, identification and removal of the artefacts are empirical, and various computational techniques have been applied for this purpose. Here we report a new module for artefact removal, an extension of the toolbox named SANTIA (SigMate Advanced: a Novel Tool for Identification of Artefacts in Neuronal Signals) which allows for fast application of deep learning techniques to remove said artefacts without relying on data from other channels.