Brain Functions Laboratory Inc.

Midori ku, Japan

Brain Functions Laboratory Inc.

Midori ku, Japan
SEARCH FILTERS
Time filter
Source Type

Aoki Y.,Osaka University | Ishii R.,Osaka University | Pascuai-Marqui R.D.,University of Zürich | Pascuai-Marqui R.D.,Kansai Medical University | And 9 more authors.
Frontiers in Human Neuroscience | Year: 2015

Recent functional magnetic resonance imaging (fMRI) studies have shown that functional networks can be extracted even from resting state data, the so called "Resting State independent Networks” (RS-independent-Ns) by applying independent component analysis (ICA). However, compared to fMRI, electroencephalography (EEG) and magnetoencephalography (MEG) have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RS-independent-Ns and their interactions in all frequency bands. We applied exact low resolution brain electromagnetic tomography-ICA (eLORETA-ICA) to resting- state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RS-independent-Ns in alpha, beta and gamma frequency bands. Next, taking into account previous neuroimaging findings, five RS-independent-Ns were identified: (1) the visual network in alpha frequency band, (2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) in alpha and beta frequency bands and left posterior dorsal visual pathway (DVP) in alpha frequency band, (3) self-referential processing network, characterized by a negative correlation between the medial prefrontal cortex (mPFC) in beta frequency band and right temporoparietal junction (TPJ) in alpha frequency band, (4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus in alpha frequency band; and (5) sensorimotor network in beta and gamma frequency bands. We selected eLORETA-ICA which has many advantages over the other network visualization methods and overall findings indicate that eLORETA- ICA with EEG data can identify five RS-independent-Ns in their intrinsic frequency bands, and correct correlations within RS-independent-Ns. © 2015, Frontiers in Human Neuroscience. All rights received.


Dauwels J.,Nanyang Technological University | Srinivasan K.,Nanyang Technological University | Srinivasan K.,Indian Institute of Technology Madras | Ramasubba Reddy M.,Indian Institute of Technology Madras | And 5 more authors.
International Journal of Alzheimer's Disease | Year: 2011

Medical studies have shown that EEG of Alzheimer's disease (AD) patients is "slower" (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83 (MCI) and 98 (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone. Copyright © 2011 Justin Dauwels et al.


PubMed | Brain Functions Laboratory Inc., Chiba University and Tokyo Institute of Technology
Type: | Journal: Computational intelligence and neuroscience | Year: 2016

Eye motion-based human-machine interfaces are used to provide a means of communication for those who can move nothing but their eyes because of injury or disease. To detect eye motions, electrooculography (EOG) is used. For efficient communication, the input speed is critical. However, it is difficult for conventional EOG recognition methods to accurately recognize fast, sequentially input eye motions because adjacent eye motions influence each other. In this paper, we propose a context-dependent hidden Markov model- (HMM-) based EOG modeling approach that uses separate models for identical eye motions with different contexts. Because the influence of adjacent eye motions is explicitly modeled, higher recognition accuracy is achieved. Additionally, we propose a method of user adaptation based on a user-independent EOG model to investigate the trade-off between recognition accuracy and the amount of user-dependent data required for HMM training. Experimental results show that when the proposed context-dependent HMMs are used, the character error rate (CER) is significantly reduced compared with the conventional baseline under user-dependent conditions, from 36.0 to 1.3%. Although the CER increases again to 17.3% when the context-dependent but user-independent HMMs are used, it can be reduced to 7.3% by applying the proposed user adaptation method.


Vialatte F.-B.,ESPCI ParisTech | Vialatte F.-B.,RIKEN | Dauwels J.,Nanyang Technological University | Maurice M.,RIKEN | And 2 more authors.
International Journal of Alzheimer's Disease | Year: 2011

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ(3.5-7.5Hz), α1 (7.5-9.5Hz), α2 (9.5-12.5Hz), and β(12.5-25Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD. Copyright © 2011 Franois-B. Vialatte et al.


Musha T.,Brain Functions Laboratory Inc. | Matsuzaki H.,Brain Functions Laboratory Inc. | Kobayashi Y.,Brain Functions Laboratory Inc. | Okamoto Y.,Chiba Institute of Technology | And 2 more authors.
IEEE Transactions on Biomedical Engineering | Year: 2013

A pair of markers, sNAT and vNAT, is derived from the electroencephalogram (EEG) power spectra (PS) recorded for 5 min with 21 electrodes (4-20 Hz) arranged according to the 10-20 standard. These markers form a new diagnosis tool 'NAT' aiming at characterizing various brain disorders. Each signal sequence is divided into segments of 0.64 s and its discrete PS consists of eleven frequency components from 4.68 (3 × 1.56) Hz through 20.34 (13 × 1.56) Hz. PS is normalized to its mean and the bias of PS components on each frequency component across the 21 signal channels is reset to zero. The marker sNAT consists of ten frequency components on 21 channels, characterizing neuronal hyperactivity or hypoactivity as compared with NLc (normal controls). The marker vNAT consists of ten ratios between adjacent PS components denoting the over- or undersynchrony of collective neuronal activities as compared with NLc. The likelihood of a test subject to a specified brain disease is defined in terms of the normalized distance to the template NAT state of the disease in the NAT space. Separation of MCI-AD patients (developing AD in 12-18 months) from NLc is made with a false alarm rate of 15%. Locations with neuronal hypoactivity and undersynchrony of AD patients agree with locations of rCBF reduction measured by SPECT. The 2-D diagram composed of the binary likelihoods between ADc and NLc in the two representations of sNAT and vNAT enables tracing the NAT state of a test subject approaching the AD area, and the follow-up of the treatment effects. © 1964-2012 IEEE.


Ueda T.,Tokyo Institute of Technology | Musha T.,Brain Functions Laboratory Inc. | Yagi T.,Tokyo Institute of Technology
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS | Year: 2013

Mild cognitive impairment (MCI) patients and healthy people were classified by using a 'power variance function (PVF)', namely, an index of electroencephalography (EEG) proposed in a previous report. PVF is defined by calculating variance of the power variability of an EEG signal at each frequency of the signal using wavelet transform. After confirming that the distribution of PVFs of the subjects was a normal distribution at each frequency, the distributions of PVFs of 25 MCI patients and those of 57 healthy people were compared in terms of Z-score. The comparison results indicate that for the MCI patients, the PVFs in the θ band are significantly higher in left parieto-occipital area and that those in the β band are lower in the bitemporal area. Multidimensional discriminant analysis using the PVF in the θ-β band recorded only on four electrodes on the left parieto-occipital area could be used to classify MCI patients from healthy people with leave-one-out accuracy of 87.5%. This indicates the possibility of diagnosing MCI by using EEG signals recorded only on a few electrodes. © 2013 IEEE.


Musha T.,Brain Functions Laboratory Inc.
2015 International Conference on Noise and Fluctuations, ICNF 2015 | Year: 2015

Spontaneous electric potentials on the scalp (EEG: Electroencephalogram) are generated by cerebral neuronal activities. The EEG signals are therefore rich with information about cerebral activities. As long as the living body is alive, the biological rhythm is always subject to fluctuations. Furthermore, Neuronal activities carry biological information to maintain the living state [1]. We are now testing two novel technologies, which are based on EEG fluctuations for detecting the early stage of dementias, especially Alzheimer's disease (AD). Otherwise, there would be no other ways for its prevention. © 2015 IEEE.


Patent
Brain Functions Laboratory Inc. | Date: 2016-01-13

Discrete Fourier transform is performed on an output of each of brain potential sensors, which measure a brain potential of a subject, for each of segments in order to obtain a discrete Fourier coefficient that has a frequency component, which is an integral multiple of a fundamental frequency that is an inverse number of a predetermined time width, within a predetermined frequency band. A mean value of squares of absolute values of Fourier coefficients is obtained. The Fourier coefficients are normalized using the mean value of the squares of the absolute values thereof in order to obtain a normalized power spectrum (NPS;j,m) that is a first parameter. A product of mean values of squares of absolute values of Fourier coefficients of adjoining frequency components in all the segments is normalized using a square value of the sum of the mean values of the adjoining frequency components in order to obtain a normalized power ratio (NPV;j,m) that is a second parameter. Two markers sNAT;j,m and vNAT;j,m are derived from the first and second parameters respectively in order to evaluate a brain function activity level.


Patent
Brain Functions Laboratory Inc. | Date: 2012-10-12

A magnitude of a current component in x direction, y direction or z direction, or a composite current of the current components in x direction, y direction and z direction, estimated from scalp potentials outputted from sensors mounted on a head of a subject, predetermined coordinates of lattice points preset in a standard brain, and predetermined coordinates of the sensors is determined. A normalized power variance (NPV) and its mean value are determined with Fourier coefficients determined from the magnitude of the current component or the composite current. Z-score (or Y-score) of the subject from a mean value of NPV predetermined in the same manner as the mean value and a standard deviation of the NPV for a predetermined normal person group is determined and mapped with contour lines corresponding to the lattice points on a horizontal plane designated.


Patent
Brain Functions Laboratory Inc. | Date: 2014-11-18

Discrete Fourier transform is performed on an output of each brain potential sensor, which measure a subjects brain potential, for each segment in order to obtain a discrete Fourier coefficient that has a frequency component. A mean value of squares of absolute values of Fourier coefficients is obtained. The Fourier coefficients are normalized using the mean value for obtaining a normalized power spectrum NPS;j,m. Mean values of squares of absolute values of Fourier coefficients of adjoining frequency components in all the segments is normalized using a square value of the mean values of the adjoining frequency components for obtaining a normalized power ratio NPV;j,m. Two markers sNAT;j,m and vNAT;j,m are derived from the power spectrum and power ratio for evaluating a brain function activity level.

Loading Brain Functions Laboratory Inc. collaborators
Loading Brain Functions Laboratory Inc. collaborators