Journal Brain Informatics-
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0093-y
Abstract:Although the objective depression evaluation is a hot topic in recent years, less is known concerning developing a pervasive and objective approach for quantitatively evaluating depression. Driven by the Wisdom as a Service architecture, a quantitative analysis method for rating depressive mood status based on forehead electroencephalograph (EEG) and an electronic diary log application named quantitative log for mental state (Q-Log) is proposed. A regression method based on random forest algorithm is adopted to train the quantitative model, where independent variables are forehead EEG features and the dependent variables are the first principal component (FPC) values of the Q-Log. The Leave-One-Participant-Out Cross-Validation is adopted to estimate the performance of the quantitative model, and the result shows that the model outcomes have a moderate uphill relationship (the average coefficient equals 0.6556 and the P value less than 0.01) with the FPC values of the Q-Log. Furthermore, an exemplary application of knowledge sharing, which is developed by using ontology technology and Jena inference subsystem, is given to illustrate the preliminary work for annotating data and facilitating clinical users to understand the meaning of the quantitative analysis results. This method combining physiological sensor data with psychological self-rating data could provide new insights into the pervasive and objective depression evaluation processes in daily life.
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0092-z
Abstract:This literature survey attempts to clarify different approaches considered to study the impact of the musical stimulus on the human brain using EEG Modality. Glancing at the field through various aspects of such studies specifically an experimental protocol, the EEG machine, number of channels investigated, feature extracted, categories of emotions, the brain area, the brainwaves, statistical tests, machine learning algorithms used for classification and validation of the developed model. This article comments on how these different approaches have particular weaknesses and strengths. Ultimately, this review concludes a suitable method to study the impact of the musical stimulus on brain and implications of such kind of studies.
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0091-0
Abstract:Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting and applied on a case study of the Alzheimer’s Disease Neuroimaging Initiative.
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0090-1
Abstract:We revisit the side-channel attacks with brain–computer interfaces (BCIs) first put forward by Martinovic et al. at the USENIX 2012 Security Symposium. For this purpose, we propose a comprehensive investigation of concrete adversaries trying to extract a PIN code from electroencephalogram signals. Overall, our results confirm the possibility of partial PIN recovery with high probability of success in a more quantified manner and at the same time put forward the challenges of full/systematic PIN recovery. They also highlight that the attack complexities can significantly vary in function of the adversarial capabilities (e.g., supervised/profiled vs. unsupervised/non-profiled), hence leading to an interesting trade-off between their efficiency and practical relevance. We then show that similar attack techniques can be used to threat the privacy of BCI users. We finally use our experiments to discuss the impact of such attacks for the security and privacy of BCI applications at large, and the important emerging societal challenges they raise.
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0088-8
Abstract:Detection of epileptic seizure activities from long-term multi-channel electroencephalogram (EEG) signals plays a significant role in the timely treatment of the patients with epilepsy. Visual identification of epileptic seizure in long-term EEG is cumbersome and tedious for neurologists, which might also lead to human error. Therefore, an automated tool for accurate detection of seizures in a long-term multi-channel EEG is essential for the clinical diagnosis. This study proposes an algorithm using multi-features and multilayer perceptron neural network (MLPNN) classifier. After appropriate approval from the ethical committee, recordings of EEG data were collected from the Institute of Neurosciences, Ramaiah Memorial College and Hospital, Bengaluru. Initially, preprocessing was performed to remove the power-line noise and motion artifacts. Four features, namely power spectral density (Yule–Walker), entropy (Shannon and Renyi), and Teager energy, were extracted. The Wilcoxon rank-sum test and descriptive analysis ensure the suitability of the proposed features for pattern classification. Single and multi-features were fed to the MLPNN classifier to evaluate the performance of the study. The simulation results showed sensitivity, specificity, and false detection rate of 97.1%, 97.8%, and 1 h−1, respectively, using multi-features. Further, the results indicate the proposed study is suitable for real-time seizure recognition from multi-channel EEG recording. The graphical user interface was developed in MATLAB to provide an automated biomarker for normal and epileptic EEG signals.
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0089-7
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0087-9
Abstract:Models of time perception share an element of scalar expectancy theory known as the internal clock, containing specific mechanisms by which the brain is able to experience time passing and function effectively. A debate exists about whether to treat factors that influence these internal clock mechanisms (e.g., emotion, personality, executive functions, and related neurophysiological components) as arousal- or attentional-based factors. This study investigated behavioral and neurophysiological responses to an affective time perception Go/NoGo task, taking into account the behavioral inhibition (BIS) and behavioral activation systems (BASs), which are components of reinforcement sensitivity theory. After completion of self-report inventories assessing personality traits, electroencephalogram (EEG/ERP) and behavioral recordings of 32 women and 13 men recruited from introductory psychology classes were completed during an affective time perception Go/NoGo task. This task required participants to respond (Go) and inhibit (NoGo) to positive and negative affective visual stimuli of various durations in comparison to a standard duration. Higher BAS scores (especially BAS Drive) were associated with overestimation bias scores for positive stimuli, while BIS scores were not correlated with overestimation bias scores. Furthermore, higher BIS Total scores were associated with higher N2d amplitudes during positive stimulus presentation for 280 ms, while higher BAS Total scores were associated with higher N2d amplitudes during negative stimuli presentation for 910 ms. Findings are discussed in terms of arousal-based models of time perception, and suggestions for future research are considered.
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0086-x
Abstract:Though the modern medical imaging research is advancing at a booming rate, it is still a very challenging task to detect brain tumor perfectly. Medical imaging unlike other imaging system has highest penalty for a minimal error. So, the detection of tumor should be accurate to minimize the error. Past researchers used biopsy to detect the tumor tissue from the other soft tissues in the brain which is time-consuming and may have errors. We outlined a two-stage verification-based tumor segmentation that makes the detection more accurate. We segmented the tumor area from the MR image and then used another algorithm to match the segmented portion with the ground truth image. We named this new algorithm as watershed-matching algorithm. The most promising part of our model is the status checking of the tumor by finding the area of the tumor. Our proposed model works better than other state-of-the art works on BRATS 2017 dataset.
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0085-y
Abstract:The Nash embedding theorem demonstrates that any compact manifold can be isometrically embedded in a Euclidean space. Assuming the complex brain states form a high-dimensional manifold in a topological space, we propose a manifold learning framework, termed Thought Chart, to reconstruct and visualize the manifold in a low-dimensional space. Furthermore, it serves as a data-driven approach to discover the underlying dynamics when the brain is engaged in a series of emotion and cognitive regulation tasks. EEG-based temporal dynamic functional connectomes are created based on 20 psychiatrically healthy participants’ EEG recordings during resting state and an emotion regulation task. Graph dissimilarity space embedding was applied to all the dynamic EEG connectomes. In order to visualize the learned manifold in a lower dimensional space, local neighborhood information is reconstructed via k-nearest neighbor-based nonlinear dimensionality reduction (NDR) and epsilon distance-based NDR. We showed that two neighborhood constructing approaches of NDR embed the manifold in a two-dimensional space, which we named Thought Chart. In Thought Chart, different task conditions represent distinct trajectories. Properties such as the distribution or average length in the 2-D space may serve as useful parameters to explore the underlying cognitive load and emotion processing during the complex task. In sum, this framework is a novel data-driven approach to the learning and visualization of underlying neurophysiological dynamics of complex functional brain data.
Brain Informatics, Volume 5; doi:10.1186/s40708-018-0084-z
Abstract:Epilepsy is a chronic chaos of the central nervous system that influences individual’s daily life by putting it at risk due to repeated seizures. Epilepsy affects more than 2% people worldwide of which developing countries are affected worse. A seizure is a transient irregularity in the brain’s electrical activity that produces disturbing physical symptoms such as a lapse in attention and memory, a sensory illusion, etc. Approximately one out of every three patients have frequent seizures, despite treatment with multiple anti-epileptic drugs. According to a survey, population aged 65 or above in European Union is predicted to rise from 16.4% (2004) to 29.9% (2050) and also this tremendous increase in aged population is also predicted for other countries by 2050. In this paper, seizure detection techniques are classified as time, frequency, wavelet (time–frequency), empirical mode decomposition and rational function techniques. The aim of this review paper is to present state-of-the-art methods and ideas that will lead to valid future research direction in the field of seizure detection.