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Numerous studies have been conducted on the connection between music and the brain, and it has been established that listening to music directly affects brain activity and stimulation. The potential benefits of music therapy, which uses music as a tool for healing and fostering well-being, have come to light in a number of circumstances. However, there is a gap in understanding the effects of Indian classical music (ICM) on the brain and its therapeutic applications. In this work, the authors propose a systematic approach for identifying brain regions evoked to live ICM stimuli, considering input and output uncertainties. The brain responses are captured through 24 channel Electroencephalogram (EEG) cap, which is utilized to allocate electrodes to different regions of the brain. The proposed region specific near-automated framework based on eigen perturbation framework provides a measure to capture the time evolution of brain activity for the melodic transition or transition from a raga to relaxation triggered by Indian classical music. This identification is relevant in understanding dynamic changes in brain responses during musical experiences providing a more comprehensive perception and processing of ICM in the human brain. This automated approach can help integrate it into evidence-based music therapy for cognitive, emotional, and psychological conditions. Probabilistic analysis based on extensive experimental studies with live Indian classical vocal stimuli brings forth many interesting results that are worth delving into for future directions in music therapy. The findings of this study provides evidence indicating ragas activate different brain regions based on listener's musical knowledge.

To date, regional brain atrophy unfolded using neuroimaging methods is observed to be the signature of Parkinson's disease (PD). In addition, graph theory-based studies are proving altered structural connectivity in PD. This motivated us to employ regional grey matter volume of PD patients (N=70) for comparative network analysis with an equal number of age- and gender-matched healthy controls (HC). In the current study, normalized grey matter maps obtained from structural magnetic resonance imaging (sMRI) were parcellated into 56 ROI (regions of interest) for construction of symmetric matrix using partial correlation between every pair of regional grey matter volumes. Sparsity thresholding was used to binarize the matrices and MATLAB functions from brain connectivity toolbox were employed to obtain the connectivity metrics. We observed PD with a significantly lower clustering coefficient as well as local efficiency at higher sparsities (above 0.9 and 0.84, respectively) with p0.05. The right fusiform gyrus was found to be the conserved hub, besides disruption of four hubs and regeneration of five other hubs. Lower clustering coefficient and local efficiency were indicative of reduced local integration and information processing, respectively. Hence, we suggest that global clustering coefficient and local efficiency could have a pivotal role in evaluating network topology. Thereby, our findings confirmed impairment of normal structural brain network topology reflecting disconnectivity mechanisms in PD. Clinical Relevance - Analyzing structural brain connectivity in Parkinson's disease might provide the researchers and clinicians with a signature pattern of the disease to discriminate patients from normal controls.

This paper applies Correlated component analysis (CorrCA) to extract the reliable and maximally correlated P300 component. Trials collected over time during an oddball electroencephalogram (EEG) experiment are first segregated into the target and non-target trial cohorts. CorrCA was then applied to both cohorts separately. Event related potential (ERP) image plots of CorrCA components from target trials showed a coherent structure for P300 latency around 400 ms when compared to plots of single electrode (Cz) trials. A difference in peak amplitude of 0.37 micro volts was observed between averaged first CorrCA component and averaged target trials at Cz electrode for able bodied subjects. Inter trial Correlation (ITC) obtained using CorrCA algorithm is applied to detect subjects who performed poorly during the oddball experiment. We also used a forward model considering sixteen channels to identify the spatial patterns of neural activity for both able and disabled subjects. It was observed that disabled subjects illustrated lower neural activation during target trials in most brain areas when compared to able-bodied subjects.

We present a novel android application named CameraEEG that enables synchronized acquisition of Electroencephalogram(EEG) and camera data using a smartphone. Audio-visual events of interest experienced by the subject were also recorded using a button press on the CameraEEG app. Unlike lab-restricted experiments, which usually constrain the subject’s mobility, this wearable solution enables monitoring of the human brain during everyday life activities. The app was built using Android SDK version 28 and Smarting mobi SDK from mbraintrain. It works on all android devices having a minimum Android OS - Lollipop.We successfully recorded thirty minutes of synchronized Video and EEG during eyes closed and walking tasks using the app. Event markers enabled by the subject using the app during walking tasks were also recorded. Timing tests showed that temporal synchronization of video and EEG data was good. We analysed the recorded data and were able to identify the task performed by the subject from the event markers. The power spectrum density of the two tasks showed different power spectrums with a peak in the alpha band for eyes closed task. We also provide android studio codes for download and detailed help documentation for the community to test the developed application.

Objective: We propose and develop a novel biclustering (N-BiC) approach for performing N-way biclustering of neuroimaging data. Our approach is applicable to an arbitrary number of features from both imaging and behavioral data (e.g., symptoms). We applied it to structural MRI data from patients with schizophrenia. Methods: It uses a source-based morphometry approach [i.e., independent component analysis of gray matter segmentation maps] to decompose the data into a set of spatial maps, each of which includes regions that covary among individuals. Then, the loading parameters for components of interest are entered to an exhaustive search, which incorporates a modified depth-first search technique to carry out the biclustering, with the goal of obtaining submatrices where the selected rows (individuals) show homogeneity in their expressions of selected columns (components) and vice versa. Results: Findings demonstrate that multiple biclusters have an evident association with distinct brain networks for the different types of symptoms in schizophrenia. The study identifies two components: inferior temporal gyrus (16) and brainstem (7), which are related to positive (distortion/excess of normal function) and negative (diminution/loss of normal function) symptoms in schizophrenia, respectively. Conclusion: N-BiC is a data-driven method of biclustering MRI data that can exhaustively explore relationships/substructures from a dataset without any prior information with a higher degree of robustness than earlier biclustering applications. Significance: The use of such approaches is important to investigate the underlying biological substrates of mental illness by grouping patients into homogeneous subjects, as the schizophrenia diagnosis is known to be relatively nonspecific and heterogeneous.

Brain state classification using electroencephalography (EEG) finds applications in both clinical and non-clinical contexts, such as detecting sleep states or perceiving illusory effects during multisensory McGurk paradigm, respectively. Existing literature mostly considers recordings of EEG electrodes that cover the entire head. However, for real world applications, wearable devices that encompass just one (or a few) channels are desirable, which make the classification of EEG states even more challenging. With this as background, we applied variants of data driven Empirical Mode Decomposition (EMD) on McGurk EEG, which is an illusory perception of speech when the movement of lips does not match with the audio signal, for classifying whether the perception is affected by the visual cue or not. After applying a common pre-processing pipeline, we explored four EMD based frameworks to extract EEG features, which were classified using Random Forest. Among the four alternatives, the most effective framework decomposes the ensemble average of two classes of EEG into their respective intrinsic mode functions forming the basis on which the trials were projected to obtain features, which on classification resulted in accuracies of 63.66% using single electrode and 75.85% using three electrodes. The frequency band which plays vital role during audio-visual integration was also studied using traditional band pass filters. Of all, Gamma band was found to be the most prominent followed by alpha and beta bands which contemplates findings from previous studies.

Healthcare field is highly benefited by incorporating BCI for detection and diagnosis of some health related detriment as well as rehabilitation and restoration of certain disabilities. An EEG dataset acquired from 15 high-functioning ASD patients, while they were undergoing a P300 experiment in a virtual reality platform, was analysed in this paper using three algorithms. Performance of Bayes Linear Discriminant Analysis (BLDA) was predominant over Convolutional Neural Network (CNN) and Random Undersampling (RUS) Boosting. BLDA rendered 73% overall accuracy in predicting target and the best accuracy for each subject using CNN or BLDA yielded an overall accuracy of 76%.

In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future.

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 (h(2) = 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. © 2015 Wiley Periodicals, Inc.

We propose a novel framework to reduce background electroencephalogram (EEG) artifacts from multitrial visual-evoked potentials (VEPs) signals for use in brain-computer interface (BCI) design. An algorithm based on cyclostationary (CS) analysis is introduced to locate the suitable frequency ranges that contain the stimulus-related VEP components. CS technique does not require VEP recordings to be phase locked and exploits the intertrial similarities of the VEP components in the frequency domain. The obtained cyclic frequency spectrum enables detection of VEP frequency band. Next, bandpass or lowpass filtering is performed to reduce the EEG artifacts using these identified frequency ranges. This is followed by overlapping band EEG artifact reduction using genetic algorithm and independent component analysis (G-ICA) which uses mutual information (MI) criterion to separate EEG artifacts from VEP. The CS and GA methods need to be applied only to the training data; for the test data, the knowledge of the cyclic frequency bands and unmixing matrix would be sufficient for enhanced VEP detection. Hence, the framework could be used for online VEP detection. This framework was tested with various datasets and it showed satisfactory results with very few trials. Since the framework is general, it could be applied to the enhancement of evoked potential signals for any application.

Despite its reliable diagnosis, schizophrenia lacks an objective diagnostic test or a validated biomarker, which prevents a better understanding of this disorder. Structural magnetic resonance imaging (sMRI) has been vastly explored to find consistent abnormality patterns of gray matter concentration (GMC) in schizophrenia, yet we are far from having reached conclusive evidence. This paper presents a machine learning approach based on resampling techniques to find brain regions with consistent patterns of GMC differences between healthy controls and schizophrenia patients, these regions being detected by means of source-based morphometry. This work uses multi-site data from the Mind Clinical Imaging Consortium, which is composed of sMRI data from 124 controls and 110 patients. Our method achieves a better classification rate than other algorithms and detects regions with GMC differences between both groups that are consistent with several findings on the literature. In addition, the results obtained on data from multiple sites suggest that it may be possible to replicate these results on other datasets.

© The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved.Analyses of gray matter concentration (GMC) deficits in patients with schizophrenia (Sz) have identified robust changes throughout the cortex. We assessed the relationships between diagnosis, overall symptom severity, and patterns of gray matter in the largest aggregated structural imaging dataset to date. We performed both sourcebased morphometry (SBM) and voxel-based morphometry (VBM) analyses on GMC images from 784 Sz and 936 controls (Ct) across 23 scanning sites in Europe and the United States. After correcting for age, gender, site, and diagnosis by site interactions, SBM analyses showed 9 patterns of diagnostic differences. They comprised separate cortical, subcortical, and cerebellar regions. Seven patterns showed greater GMC in Ct than Sz, while 2 (brainstem and cerebellum) showed greater GMC for Sz. The greatest GMC deficit was in a single pattern comprising regions in the superior temporal gyrus, inferior frontal gyrus, and medial frontal cortex, which replicated over analyses of data subsets. VBM analyses identified overall cortical GMC loss and one small cluster of increased GMC in Sz, which overlapped with the SBM brainstem component. We found no significant association between the component loadings and symptom severity in either analysis. This mega-analysis confirms that the commonly found GMC loss in Sz in the anterior temporal lobe, insula, and medial frontal lobe form a single, consistent spatial pattern even in such a diverse dataset. The separation of GMC loss into robust, repeatable spatial patterns across multiple datasets paves the way for the application of these methods to identify subtle genetic and clinical cohort effects.

For the 24th Machine Learning for Signal Processing competition, participants were asked to automatically diagnose schizophrenia using multimodal features derived from MRI scans. The objective of the classification task was to achieve the best possible schizophrenia diagnosis prediction based only on the multimodal features derived from brain MRI scans. A total of 2087 entries from 291 participants with active Kaggle.com accounts were made. Each participant developed a classifier, with optional feature selection, that combined functional and structural magnetic resonance imaging features. Here we review details about the competition setup, the winning strategies, and provide basic analyses of the submitted entries. We conclude with a discussion of the advances made to the neuroimaging and machine learning fields. Index Terms— Competition, MRI, Schizophrenia, FNC, SBM 1. INTRODUCTION Schizophrenia is a severe and disabling mental illness which has no well-established, non-invasive diagnosis biomarker. Currently, due to its symptom overlap with other mental illnesses (like bipolar disorder) it is diagnosed subjectively only, by process of elimination. In order to broadly explore the current limits of automatic diagnosis of schizophrenia, this year’s machine learning competition was entitled The MLSP 2014 Schizophrenia Classification Challenge, which occurred from June 5 through July 20, 2014. For the second consecutive year, the competition was hosted on Kaggle.com. The response from neuroscience and machine learning communities was fantastic, including 291 participants in 245 teams, and an impressive 2087 valid entries. The competition was sponsored by Kaggle.com, who provided their hosting services, and Springer, who provided monetary rewards to the winning participants. In total, 611 entries from 151 participants outperformed the provided competition benchmark in all performance measures considered here. The three winning strategies used, in ranking order, Gaussian process (GP) [1], Gaussian kernel support vector machine (SVM) [2], and distance weighted discrimination (DWD) [3] classifiers, respectively.

Item does not contain fulltext BACKGROUND: Pooling of multi-site MRI data is often necessary when a large cohort is desired. However, different scanning platforms can introduce systematic differences which confound true effects of interest. One may reduce multi-site bias by calibrating pivotal scanning parameters, or include them as covariates to improve the data integrity. NEW METHOD: In the present study we use a source-based morphometry (SBM) model to explore scanning effects in multi-site sMRI studies and develop a data-driven correction. Specifically, independent components are extracted from the data and investigated for associations with scanning parameters to assess the influence. The identified scanning-related components can be eliminated from the original data for correction. RESULTS: A small set of SBM components captured most of the variance associated with the scanning differences. In a dataset of 1460 healthy subjects, pronounced and independent scanning effects were observed in brainstem and thalamus, associated with magnetic field strength-inversion time and RF-receiving coil. A second study with 110 schizophrenia patients and 124 healthy controls demonstrated that scanning effects can be effectively corrected with the SBM approach. COMPARISON WITH EXISTING METHOD(S): Both SBM and GLM correction appeared to effectively eliminate the scanning effects. Meanwhile, the SBM-corrected data yielded a more significant patient versus control group difference and less questionable findings. CONCLUSIONS: It is important to calibrate scanning settings and completely examine individual parameters for the control of confounding effects in multi-site sMRI studies. Both GLM and SBM correction can reduce scanning effects, though SBM's data-driven nature provides additional flexibility and is better able to handle collinear effects.

Evidence suggests that microRNA-137 (miR-137) is involved in the genetic basis of schizophrenia. Risk variants within the miR-137 host gene (MIR137HG) influence structural and functional brain-imaging measures, and miR-137 itself is predicted to regulate hundreds of genes. We evaluated the influence of a MIR137HG risk variant (rs1625579) in combination with variants in miR-137-regulated genes TCF4, PTGS2, MAPK1 and MAPK3 on gray matter concentration (GMC). These genes were selected based on our previous work assessing schizophrenia risk within possible miR-137-regulated gene sets using the same cohort of subjects. A genetic risk score (GRS) was determined based on genotypes of these four schizophrenia risk-associated genes in 221 Caucasian subjects (89 schizophrenia patients and 132 controls). The effects of the rs1625579 genotype with the GRS of miR-137-regulated genes in a three-way interaction with diagnosis on GMC patterns were assessed using a multivariate analysis. We found that schizophrenia subjects homozygous for the MIR137HG risk allele show significant decreases in occipital, parietal and temporal lobe GMC with increasing miR-137-regulated GRS, whereas those carrying the protective minor allele show significant increases in GMC with GRS. No correlations of GMC and GRS were found in control subjects. Variants within or upstream of genes regulated by miR-137 in combination with the MIR137HG risk variant may influence GMC in schizophrenia-related regions in patients. Given that the genes evaluated here are involved in protein kinase A signaling, dysregulation of this pathway through alterations in miR-137 biogenesis may underlie the gray matter loss seen in the disease.

Recognizing humans based upon one’s intrinsic physical or behavioral traits has been gaining acceptance and is termed as biometrics. It involves either confirmation or denial of the identity that the user is claiming. It is especially important in ensuring security for access to highly restricted areas (for example: accessing classified documents, control gates and defence related applications). This chapter will discuss the use of brain signals at an application level exploiting the evoked potential approach for biometrics.

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.

We present a novel two module scheme for efficient analysis of noisy Electrocardiogram (ECG) signals. The first module consists of a segmentation algorithm which uses cyclostationary analysis for the detection of a single heart beat or cycle (P wave-QRS complex-T wave). The time domain cyclostationary (CS) algorithm exploits the statistical properties of the recorded periodic ECG signal and does not use any prior knowledge about signal morphology. Using the obtained cycle length the next module uses repeated applications of Principal Component Analysis (PCA) to reduce multiple additive noises from the multi trial and multi channel recorded ECG signals. PCA has been used for noise reduction in ECG but the method of repeated applications of PCA is novel. In this study, PCA was applied in 2 stages. In the first stage, PCA was applied to multi-channel ECG signals from one trial. The output ECG signals from the first stage were used in the second stage, where PCA was applied to multi-trial ECG signals from a single channel. The proposed scheme was tested with the 12-lead ECG signals from PTB Diagnostic database (National Metrology Institute of Germany) provided on physionet website which showed significant improvement in Signal to Noise ratio. We suggest that this simple scheme can be used for automatic analysis of noisy ECG signals where the extraction and denoising of single heart beat provide enhanced physiological features which enables better clinical interpretation of cardiovascular functionalities.

A segmentation algorithm, which detects a single cardiac cycle (S 1-systole-S2-diastole) of phonocardiogram (PCG) signals using homomorphic filtering and K-means clustering and a three way classification of heart sounds into normal (N), systolic murmur (S) and diastolic murmur (D) using grow and learn (GAL) neural network, are presented. Homomorphic filtering converts a non-linear combination of signals (multiplied in time domain) into a linear combination by applying logarithmic transformation. It involves the retrieval of the envelope, a(n) of the PCG signal by attenuating the contribution of fast varying component, f(n) using an appropriate low pass filter. K-means clustering is a non-hierarchical partitioning method, which helps to indicate single cardiac cycle in the PCG signal. Segmentation performance of 90.45% was achieved using the proposed algorithm. Feature vectors were formed after segmentation by using Daubechies-2 wavelet detail coefficients at the second decomposition level. Grow and learn network was used for classification of the segmented PCG signals and a classification accuracy of 97.02% was achieved. It is concluded that homomorphic filtering and GAL network could be used for segmentation and classification of PCG signals without using a reference signal

Cortical responses to complex natural stimuli can be isolated by examining the relationship between neural measures obtained while multiple individuals view the same stimuli. These inter-subject correlation's (ISC's) emerge from similarities in individual's cortical response to the shared audiovisual inputs, which may be related to their emergent cognitive and perceptual experience. Within the present study, our goal is to examine the utility of using ISC's for predicting which audiovisual clips individuals viewed, and to examine the relationship between neural responses to natural stimuli and subjective reports. The ability to predict which clips individuals viewed depends on the relationship of the EEG response across subjects and the nature in which this information is aggregated. We conceived of three approaches for aggregating responses, i.e. three assignment algorithms, which we evaluated in Experiment 1A. The aggregate correlations algorithm generated the highest assignment accuracy (70.83% chance = 33.33%) and was selected as the assignment algorithm for the larger sample of individuals and clips within Experiment 1B. The overall assignment accuracy was 33.46% within Experiment 1B (chance = 06.25%), with accuracies ranging from 52.9% (Silver Linings Playbook) to 11.75% (Seinfeld) within individual clips. ISC's were significantly greater than zero for 15 out of 16 clips, and fluctuations within the delta frequency band (i.e. 0-4 Hz) primarily contributed to response similarities across subjects. Interestingly, there was insufficient evidence to indicate that individuals with greater similarities in clip preference demonstrate greater similarities in cortical responses, suggesting a lack of association between ISC and clip preference. Overall these results demonstrate the utility of using ISC's for prediction, and further characterize the relationship between ISC magnitudes and subjective reports.

An algorithm for segmentation of heart sounds (HSs) into a single cardiac cycle (Sl-Systole-S2-Diastole) using homomorphic filtering and k-means clustering and a three way classification of heart sounds into normal (N), systolic murmur (S), and diastolic murmur (D), based on neural networks is developed. This algorithm does not require additional reference signal such as ECG signal. Feature vectors are formed after segmentation by using Daubechies-2 wavelet detail coefficients at the second decomposition level. Redundant features are removed using principal component analysis (PCA). Multilayer perceptron-backpropagation neural network (MLP-BP) is used for classification of three different HSs. A classification accuracy of 94.5% and a segmentation accuracy (or performance) of 90.45% was achieved; thus, demonstrating that segmentation and classification of heart sounds without the aid of reference signal is achievable

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.

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.

We present various techniques to detect a target in an oddball paradigm. These techniques are presented with reference to two P300 paradigms being studied to build a biometric system using electroencephalogram (EEG) signals. The novel inblock paradigm presented in this paper proposes a possible variant to the known oddball paradigm and analyses the effect of spatial location of the target block with respect to the non-target block. It brings out a new approach in the oddball paradigm, wherein location of the target might help evoke a higher P300 potential. A comparison of the various analysis techniques studied for both the paradigms is also presented. Initial results from four subjects show that energy analysis gave improved results than the traditional amplitude analysis techniques for target detection in the studied oddball paradigms. The results were comparable for all subjects except for one subject where energy analysis provided better target detection, suggesting that energy based methods could be further explored. This proposed novel paradigm is a step towards the online brain biometric system which is being built for authentication in high security scenarios.