Using EEG and NIRS for brain-computer interface and cognitive performance measures: a pilot study

This study addresses two important problem statements, namely, selection of training datasets for online Brain-Computer Interface (BCI) classifier training and determination of participant concentration levels during an experiment. The work also attempted a pilot study to integrate electroencephalograms (EEGs) and Near Infra Red Spectroscopy (NIRS) for possible applications such as the BCI and for measuring cognitive levels. Two experiments are presented, the first being a mathematical task interleaved with rest states using NIRS only. In the next, integration of the EEG-NIRS with reference to P300-based BCI systems as well as the experimental conditions designed to elicit the concentration levels (denoted as ON and OFF states here) during the paradigm, are presented. The first experiment indicates that NIRS can be used to differentiate a concentrated (i.e., mental activity) level from the rest. However, the second experiment reveals statistically significant results using the EEG only. We present details about the equipment used, the participants as well as the signal processing and machine learning techniques implemented to analyse the EEG and NIRS data. After discussing the results, we conclude by describing the research scope as well as the possible pitfalls in this work from a NIRS viewpoint, which presents an opportunity for future research exploration for BCI and cognitive performance measures

Systematic Review of Experimental Paradigms and Deep Neural Networks for Electroencephalography-Based Cognitive Workload Detection

This article summarizes a systematic review of the electroencephalography (EEG)-based cognitive workload (CWL) estimation. The focus of the article is twofold: identify the disparate experimental paradigms used for reliably eliciting discreet and quantifiable levels of cognitive load and the specific nature and representational structure of the commonly used input formulations in deep neural networks (DNNs) used for signal classification. The analysis revealed a number of studies using EEG signals in its native representation of a two-dimensional matrix for offline classification of CWL. However, only a few studies adopted an online or pseudo-online classification strategy for real-time CWL estimation. Further, only a couple of interpretable DNNs and a single generative model were employed for cognitive load detection till date during this review. More often than not, researchers were using DNNs as black-box type models. In conclusion, DNNs prove to be valuable tools for classifying EEG signals, primarily due to the substantial modeling power provided by the depth of their network architecture. It is further suggested that interpretable and explainable DNN models must be employed for cognitive workload estimation since existing methods are limited in the face of the non-stationary nature of the signal.Comment: 10 Pages, 4 figure

Unique Brain Network Identification Number for Parkinson's Individuals Using Structural MRI

We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding brain networks of individual subject. To realize this objective, we employed T1-weighted structural MRI of 180 Parkinson's disease (PD) patients from National Institute of Mental Health and Neurosciences, India. We parcellated each subject's brain volume and constructed individual adjacency matrix using correlation between grey matter (GM) volume of every pair of regions. The unique code is derived from values representing connections of every node (i), weighted by a factor of 2^-(i-1). The numerical representation UBNIN was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. This model may be implemented as neural signature of a person's unique brain connectivity, thereby useful for brainprinting applications. Additionally, we segregated the above dataset into five age-cohorts: A:22-32years, B:33-42years, C:43-52years, D:53-62years and E:63-72years to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox-based MATLAB functions. For each age-cohort, a decreasing trend was observed in mean clustering coefficient with increasing sparsity. Significantly different clustering coefficient was noted between age-cohort B and C (sparsity: 0.63,0.66), C and E (sparsity: 0.66,0.69). Our findings suggest network connectivity patterns change with age, indicating network disruption due to the underlying neuropathology. Varying clustering coefficient for different cohorts indicate that information transfer between neighboring nodes change with age. This provides evidence on age-related brain shrinkage and network degeneration.Comment: 9 pages, 5 figures,1 algorithm, 1 main table, 1 appendix tabl

Enhanced target detection using P300 and gamma band analysis

We present a novel method which uses the P300amplitudes and Gamma band energy for enhanced targetdetection in P300 paradigms which are being researchedtowards a brain biometric system. The novelty of thiswork lies in the usage of both P300 amplitudes alongwith gamma band features which were calculated fromaveraged waveforms using thirty trials (Target and Non-target) for eight channels. P300 amplitudes wascalculated in the 300-600ms range and Gamma bandenergy was computed in the 30-50 Hz using Shannonenergy for target and non-target. A simple decisionmaker was used to classify the obtained feature vectors.Initial results confirm the possibility of using gamma band energy along-with P300 analysis in an oddball paradigm for better classification accuracies. This studyoffers motivation warranting further need to explore thelinks between P300 and Gamma band in P300 paradigms

Exploiting the P300 paradigm for cognitive

Abstract: Automatic identification of a person's individuality is an important issue today. Brain Computer Interfaces (BCI) which uses EEG as a modality is a promising area for cognitive biometrics. A BCI system could be used to recognise a sequence (say letters, colours or images) by the user. This sequence could form a 'BrainWord', which could be used for authentication in a multimodal environment with other technologies for high security applications. In this work, we studied several variations of the well-known P300 BCI paradigm. The influence of irrelevant stimuli during a task was studied by considering the popular Rapid Serial Visual Paradigm (RSVP). The variation in spatial locations of the presentation stimuli during a task was studied, by designing a Spatially Varying Paradigm. Comparison of classification accuracies and bit rates for eight participants from a BCI perspective, highlights that RSVP paradigm could be exploited effectively for biometrics

Genetic Markers of White Matter Integrity in Schizophrenia Revealed by Parallel ICA

It is becoming a consensus that white matter integrity is compromised in schizophrenia (SZ), however the underlying genetics remains elusive. Evidence suggests a polygenic basis of the disorder, which involves various genetic variants with modest individual effect sizes. In this work, we used a multivariate approach, parallel independent component analysis (P-ICA), to explore the genetic underpinnings of white matter abnormalities in SZ. A pre-filtering step was first applied to locate 6527 single nucleotide polymorphisms (SNPs) discriminating patients from controls with a nominal uncorrected p-value of 0.01. These potential susceptibility loci were then investigated for associations with fractional anisotropy (FA) images in a cohort consisting of 73 SZ patients and 87 healthy controls (HC). A significant correlation (r = −0.37, p = 1.25 × 10−6 ) was identified between one genetic factor and one FA component after controlling for scanning site, ethnicity, age, and sex. The identified FA-SNP association remained stable in a 10-fold validation. A 5000-run permutation test yielded a p-value of 2.00 × 10−4 . The FA component reflected decreased white matter integrity in the forceps major for SZ patients. The SNP component was overrepresented in genes whose products are involved in corpus callosum morphology (e.g., CNTNAP2, NPAS3, and NFIB) as well as canonical pathways of synaptic long term depression and protein kinase A signaling. Taken together, our finding delineates a part of genetic architecture underlying SZ-related FA reduction, emphasizing the important role of genetic variants involved in neural development

Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia

Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development

Genome-wide significant linkage of schizophrenia-related neuroanatomical trait to 12q24

The insula and medial prefrontal cortex (mPFC) share functional, histological, transcriptional and developmental characteristics and they serve higher cognitive functions of theoretical relevance to schizophrenia and related disorders. Meta-analyses and multivariate analysis of structural magnetic resonance imaging (MRI) scans indicate that gray matter density and volume reductions in schizophrenia are the most consistent and pronounced in a network primarily composed of the insula and mPFC. We used source-based morphometry, a multivariate technique optimized for structural MRI, in a large sample of randomly ascertained pedigrees (N = 887) to derive an insula-mPFC component and to investigate its genetic determinants. Firstly, we replicated the insula-mPFC gray matter component as an independent source of gray matter variation in the general population, and verified its relevance to schizophrenia in an independent case-control sample. Secondly, we showed that the neuroanatomical variation defined by this component is largely determined by additive genetic variation (h2 = 0.59), and genome-wide linkage analysis resulted in a significant linkage peak at 12q24 (LOD = 3.76). This region has been of significant interest to psychiatric genetics as it contains the Darier’s disease locus and other proposed susceptibility genes (e.g. DAO, NOS1), and it has been linked to affective disorders and schizophrenia in multiple populations. Thus, in conjunction with previous clinical studies, our data imply that one or more psychiatric risk variants at 12q24 are co-inherited with reductions in mPFC and insula gray matter concentration

Classification of hierarchically clustered and homomorphic segmented heart sounds using neural networks

Cardiac auscultation is widely used by physicians to evaluate cardiac functions in patients and detect the presence of abnormalities. Phonocardiogram signals (PCG) are heart signals which contain vital information about the heart and can be used effectively in diagnosing various pathological conditions of heart valves. Computer- based analysis of heart sounds can be used for diagnostic purposes. This present study embarks on the development of an automatic diagnostic system for characterization of phonocardiogram signals which were hierarchically clustered and homomorphically segmented and classified using neural networks. There are three core parts to the system: (1) segmentation, (2) feature extraction, (3) classification.Master of Science (Biomedical Engineering