Linear Discrimination Analysis of Monkey Behavior in an Alternative Free Choice Task

2007 ◽  
Vol 19 (4) ◽  
pp. 416-422
Author(s):  
Kazuhito Takenaka ◽  
◽  
Yasuo Nagasaka ◽  
Sayaka Hihara ◽  
Hiroyuki Nakahara ◽  
...  
Keyword(s):  
Social Context ◽  
Discriminant Analysis ◽  
Free Choice ◽  
Discriminant Score ◽  
Linear Discriminant ◽  
Linear Discrimination ◽  
Choice Tasks ◽  
The Brain ◽  
Alternative Decision ◽  
A Current

When we observe people, we can often comprehend their intention from their behaviors. The intentions expressed by individuals can be considered as existing in interpersonal space and from a current social context. In our daily activity, choosing socially correct behavior through the observation of such social context is essential. However, it is not known how we can decode intention from another’s behavior. Here, we show how we can retrieve the intention of monkeys through external observation of their behavior patterns while performing alternative free choice tasks. We found that linear discriminant analysis on a monkey’s motion parameters could provide a discriminant score that appears to reflect the internal decision making process. The score showed a clear flexion point that we defined as a moment of outward expression of intention (OEI). This suggests that an alternative decision is made just before an OEI and that intention is expressed in the environment after this OEI in behavior, which in turn suggests that discriminant analysis may be useful in indicating how the brain implements nonverbal social communication. If we could embed the function in a human-machine interfaces, it could enable intuitive, smooth communication between machines and humans.

Discriminant and Bayes Analysis in the Differential Diagnosis of Crohns Disease and Proctocolitis

1973 ◽  
Vol 12 (02) ◽  
pp. 118-122 ◽  
Author(s):  
C. C. Spicer ◽  
J. Jones ◽  
J. E. Jones
Keyword(s):  
Differential Diagnosis ◽  
Crohn’S Disease ◽  
Discriminant Analysis ◽  
Crohn's Disease ◽  
Linear Discriminant Analysis ◽  
Bayesian Method ◽  
Crohns Disease ◽  
Linear Discriminant ◽  
Linear Discrimination ◽  
Bayes Analysis

Proctocolitis and colonic Crohn’s disease have not yet been precisely differentiated and the existence of the latter is still disputed. The study reported here is concerned with identification of the clinical features characterising patients diagnosed clinically as suffering from one or other of these diseases.A stepwise linear discriminant analysis of 91 possible indicants showed that seven were sufficient to discriminate between the two clinical groups. A further analysis of the patterns of these seven by a Bayesian method which takes account of the interdependence of the indicants showed that only five indicants were necessary for discrimination. The Crohn’s disease patients fell into a much greater variety of patterns than those with proctocolitis most of which fell into only one pattern.It is suggested that this use of linear discrimination to reduce the number of indicants combined with a subsequent analysis of the patterns of these indicants might be useful in other diseases.

scholarly journals Shared and specific signatures of locomotor ataxia in mutant mice

2020 ◽  
Author(s):  
Ana S. Machado ◽  
Hugo G. Marques ◽  
Diogo F. Duarte ◽  
Dana M. Darmohray ◽  
Megan R. Carey
Keyword(s):  
Discriminant Analysis ◽  
Motor Coordination ◽  
Locomotor Behavior ◽  
Whole Body ◽  
Gait Ataxia ◽  
Cell Degeneration ◽  
Linear Discriminant ◽  
Mouse Mutants ◽  
Compare And Contrast ◽  
The Brain

AbstractSeveral spontaneous mouse mutants with deficits in motor coordination and associated cerebellar neuropathology have been described. Intriguingly, both visible gait alterations and neuroanatomical abnormalities throughout the brain differ across mutants. We previously used the LocoMouse system to quantify specific deficits in locomotor coordination in mildly ataxic Purkinje cell degeneration mice (pcd; Machado et al., 2015). Here, we analyze the locomotor behavior of severely ataxic reeler mutants and compare and contrast it with that of pcd. Despite clearly visible gait differences, direct comparison of locomotor kinematics and linear discriminant analysis reveal a surprisingly similar pattern of impairments in multijoint, interlimb, and whole-body coordination in the two mutants. These findings capture both shared and specific signatures of gait ataxia and provide a quantitative foundation for mapping specific locomotor impairments onto distinct neuropathologies in mice.

MULTIPLE CLASSIFICATION TECHNIQUES TOWARD A ROBUST AND RELIABLE P300 BCI SYSTEM

2020 ◽  
Vol 32 (02) ◽  
pp. 2050010
Author(s):  
Fatma EL-Zahraa M. Labib ◽  
Islam A. Fouad ◽  
Mai S. Mabrouk ◽  
Amr A. Sharawy
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Linear Discriminant ◽  
The Brain ◽  
Bci System

A brain–computer interface (BCI) can be used for people with severe physical disabilities such as ALS or amyotrophic lateral sclerosis. BCI can allow these individuals to communicate again by creating a new communication channel directly from the brain to an output device. BCI technology can allow paralyzed people to share their intent with others, and thereby demonstrate that direct communication from the brain to the external world is possible and that it might serve useful functions. BCI systems include machine learning algorithms (MLAs). Their performance depends on the feature extraction and classification techniques employed. In this paper, we propose a system to exploit the P300 signal in the brain, a positive deflection in event-related potentials. The P300 signal can be incorporated into a spelling device. There are two benefits behind this kind of research. First of all, this work presents the research status and the advantages of communication via a BCI system, especially the P300 BCI system for disordered people, and the related literature review is presented. Secondly, the paper discusses the performance of different machine learning algorithms. Two different datasets are presented: the first dataset 2004 and the second dataset 2019. A preprocessing step is introduced to the subjects in both datasets first to extract the important features before applying the proposed machine learning methods: linear discriminant analysis (LDA I and LDA II), support vector machine (SVM I, SVM II, SVM III, and SVM IV), linear regression (LREG), Bayesian linear discriminant analysis (BLDA), and twin support vector machine (TSVM). By comparing the performance of the different machine learning systems, in the first dataset it is found that BLDA and SVMIV classifiers yield the highest performance for both subjects “A” and “B”. BLDA yields 98% and 66% for 15th and 5th sequences, respectively, whereas SVMIV yields 98% and 54.4% for 15th and 5th sequences, respectively. While in the second dataset, it is obvious that BLDA classifier yields the highest performance for both subjects “1” and “2”, it achieves 90.115%. The paper summarizes the P300 BCI system for the two introduced datasets. It discusses the proposed system, compares the classification methods performances, and considers some aspects for the future work to be handled. The results show high accuracy and less computational time which makes the system more applicable for online applications.

scholarly journals Mining the Mind: Linear Discriminant Analysis of MEG Source Reconstruction Time Series Supports Dynamic Changes in Deep Brain Regions During Meditation Sessions

2021 ◽  
Author(s):  
Daniela Calvetti ◽  
Brian Johnson ◽  
Annalisa Pascarella ◽  
Francesca Pitolli ◽  
Erkki Somersalo ◽  
...  
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Longitudinal Studies ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Atlas ◽  
Meditation Practice ◽  
Linear Discriminant ◽  
The Brain

AbstractMeditation practices have been claimed to have a positive effect on the regulation of mood and emotions for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the influence of meditation on the human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as an evidence its effectiveness beyond the subjective self reporting. Using magnetoencephalography (MEG) or electroencephalography to quantify the changes in brain activity during meditation practice represents a challenge, as no clear hypothesis about the spatial or temporal pattern of such changes is available to date. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data are first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas. Next, by bootstrapping and spectral analysis, the data are mapped to matrices representing random samples of power spectral densities in $$\alpha$$ α , $$\beta$$ β , $$\gamma$$ γ , and $$\theta$$ θ frequency bands. We use linear discriminant analysis to demonstrate that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that the cingulate cortex, insular cortex and some of the internal structures, most notably the accumbens, the caudate and the putamen nuclei, the thalamus and the amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies.

scholarly journals Characterizing Functional Connectivity Network Based on Multi-Domain Analysis for Epilepsy Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 2379-2386
Keyword(s):  
Discriminant Analysis ◽  
Detection System ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Bayesian Optimization ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Linear Discriminant ◽  
The Brain

Epileptic is a neural disease exemplified through untypical concurrent signal discharge from the neurons present in the brain region. This abnormal brain functionality could be captured through electroencephalography (EEG) system. Generally the observed EEG signals are examined by the experienced neurologist, which may be time consuming when observing hours of EEG signal. Therefore, this proposed work provides a fully automatic epileptic seizure detection system by means of the multi-domain features along with various machine learning algorithms. Initially, the obtained EEG signals are processed to clear noise and artefacts. Subsequently, the pre-processed signals are segregated as 5 seconds epochs and for each epoch various features are extracted from frequency domain, time domain. Additionally entropy, correlation and graph theory approaches has been used for analysis the connectivity of the brain network. Subsequently, distinguishable features are chosen carefully in this regard from the immense feature set by virtue of multi-objective evolutionary method and convincingly, classification has been performed using support vector machine(SVM).A Bayesian optimization (BaO) algorithm was utilized to optimize the SVM's hyper-plane parameters. In addition, Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA),Random Forest Ensemble (RFE) and k-Nearest Neighbor Ensemble (k- NNE) was also used for comparing the proposed results. These obtained results validates by considering the performance of this work is competing along with state-of the-arts approaches. The proposed work is implemented on a CHB-MIT database .The obtained performance measure of the classifiers are 99.09%, 81.49%,80.90%,76.85% and 84.14 % in SVM , LDA, QDA, k- NNE and RFE respectively. Finally SVM with Bayesian Optimization (BaO) algorithm outperforms than other classifiers with accuracy, AUC, sensitivity and specificity, as 99.09%, 99.67%, 98.06% and 98.12%, respectively.

scholarly journals Spermatozoal Head Shape in Two Inbred Strains of Mice and Their Fl and F2 Progenies

1969 ◽  
Vol 22 (4) ◽  
pp. 947 ◽  
Author(s):  
Liia Illisson
Keyword(s):  
Discriminant Analysis ◽  
Inbred Strains ◽  
Head Shape ◽  
Discriminant Score ◽  
Linear Discriminant ◽  
Effective Factors ◽  
Inbred Strains Of Mice ◽  
Inbred Parent ◽  
Low Levels ◽  
Minimal Estimate

Seven available inbred strains of mice-A, C57, SWR, C3H, 101, CBA, and DBA-were examined for differences in the shape of their spermatozoal heads. The two most extreme strains with respect to spermatozoal head shape were found to be SWR and C57. The Fl and F2 progenies derived from crossing C57 and SWR strains were found to be roughly intermediate between the parent inbred strains. Spermatozoal head shape for these preliminary investigations was calculated as outlined by Penrose (1953). Discriminant analysis was then carried out on F2 data and a linear discriminant function was obtained whereby 13 characteristics of the spermatozoal head were combined into one "super-character" or discriminant score. The numerical value of the discriminant score was taken as an estimate of spermatozoal head shape for each spermatozoon measured. 4nalyses of variance carried out on the discriminant scores for each generation revealed that intrastrain variation was not significant in the SWR strain and reached only low levels of significance in the C57 strain. The Fl males were found to be more variable than the inbred males. A large portion of the variability between the Fl males was shown to arise from "maternal effects". The F2 males were found to be much more variable than the Fl males and an estimate of heritability was approximately 0 -9. A minimal estimate of the number of "effective factors" operating to distinguish the two inbred parent strains was found to be two. The within-male variance was found not to differ significantly from generation to generation. The implications of these results are discussed.

scholarly journals Shared and specific signatures of locomotor ataxia in mutant mice

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Ana S Machado ◽  
Hugo G Marques ◽  
Diogo F Duarte ◽  
Dana M Darmohray ◽  
Megan R Carey
Keyword(s):  
Discriminant Analysis ◽  
Motor Coordination ◽  
Locomotor Behavior ◽  
Whole Body ◽  
Gait Ataxia ◽  
Cell Degeneration ◽  
Linear Discriminant ◽  
Mouse Mutants ◽  
Compare And Contrast ◽  
The Brain

Several spontaneous mouse mutants with deficits in motor coordination and associated cerebellar neuropathology have been described. Intriguingly, both visible gait alterations and neuroanatomical abnormalities throughout the brain differ across mutants. We previously used the LocoMouse system to quantify specific deficits in locomotor coordination in mildly ataxic Purkinje cell degeneration mice (pcd; Machado et al., 2015). Here, we analyze the locomotor behavior of severely ataxic reeler mutants and compare and contrast it with that of pcd. Despite clearly visible gait differences, direct comparison of locomotor kinematics and linear discriminant analysis reveal a surprisingly similar pattern of impairments in multijoint, interlimb, and whole-body coordination in the two mutants. These findings capture both shared and specific signatures of gait ataxia and provide a quantitative foundation for mapping specific locomotor impairments onto distinct neuropathologies in mice.

scholarly journals Predicting Aphasia from Strokes

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Joseph Jia ◽  
Joanna Gilberti
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Brain Lesion ◽  
Brain Cells ◽  
Lesion Volume ◽  
Classification Methods ◽  
Linear Discriminant ◽  
Lesion Location ◽  
Predictive Rate ◽  
The Brain

Strokes can occur when someone’s blood vessels get blocked and the nutrients and oxygen being transported will not reach the brain. When a stroke happens, the brain cells don’t get the nutrients they need and start to die [3]. This could cause different side effects after stroke. In this study, we try to predict the possibility of one type of after-stroke side effect, aphasia, using Machine Learning (ML) techniques. Using the data of a study about brain lesion damage after a stroke and what effects the patients were experiencing afterward, we trained a model to predict whether a person may have aphasia based on where their lesion was, how big the lesion was, how long ago their stroke was, and some other factors. We evaluated several classification methods and found that using linear discriminant analysis was the most accurately predicting when we used age, sex, lesion location, lesion volume, and many more. By linear discriminant analysis, we were able to have a 91% overall predictive rate of patients having aphasia or not after experiencing a stroke.

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