EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease

Cichocki, Andrzej; Shishkin, Sergei L.; Musha, Toshimitsu; Leonowicz, Zbigniew; Asada, Takashi; Kurachi, Takayoshi

doi:10.1016/j.clinph.2004.09.017

Cited by 109 publications

(171 citation statements)

References 34 publications

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“…The EEG data used here have been analyzed in previous studies concerning early diagnosis of AD (Chapman et al, 2007;Cichocki et al, 2005;Hogan et al, 2003;Musha et al, 2002;Vialatte et al, 2005).…”

Section: Eeg Datamentioning

confidence: 99%

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

371

384

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It is well known that EEG signals of Alzheimer's disease (AD) patients are generally less synchronous than in age-matched control subjects. However, this effect is not always easily detectable. This is especially the case for patients in the pre-symptomatic phase, commonly referred to as Mild Cognitive Impairment (MCI), during which neuronal degeneration is occurring prior to the clinical symptoms appearance. In this paper, various synchrony measures are studied in the context of AD diagnosis, including the correlation coefficient, mean-square and phase coherence, Granger causality, phase synchrony indices, information-theoretic divergence measures, state space based measures, and the recently proposed stochastic event synchrony measures. Experiments with EEG data show that many of those measures are strongly correlated (or anti-correlated) with the correlation coefficient, and hence, provide little complementary information about EEG synchrony. Measures that are only weakly correlated with the correlation coefficient include the phase synchrony indices, Granger causality measures, and stochastic-event synchrony measures. In addition, those three families of synchrony measures are mutually uncorrelated, and therefore, they each seem to capture a specific kind of interdependence. For the data set at hand, only two synchrony measures are able to convincingly distinguish AD patients from age-matched control patients, i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony. Those two measures are used as features to distinguish MCI patients from age-matched control subjects, yielding a leave-one-out classification rate of 83%. The classification performance may be further improved by adding complementary features from EEG; this approach may eventually lead to a reliable EEG-based diagnostic tool for MCI and AD.

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Section: Eeg Datamentioning

confidence: 99%

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Dauwels

Vialatte

Musha³

et al. 2010

NeuroImage

371

384

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“…Additionally, nonlinear analysis methods can provide useful information about the brain dynamics in this dementia [4,5,[8][9][10]. Nevertheless, it is desirable to develop novel strategies to help in AD detection from the analysis of the electromagnetic brain activity [9,11,12]. Techniques based on spatial filtering can help to achieve this goal, as these algorithms offer additional perspectives to examine EEG and MEG signals [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Considering these research studies, it can be hypothesised that the application of BBS, together with features extracted from electromagnetic brain activity recordings, may enhance features associated with diseases like AD. This is due to the fact that some BSS components of the EEG and MEG signals may be more sensitive to AD than others [12,14,22]. Hence, the most relevant components may be selected and the electromagnetic brain signals may be partially reconstructed using only these components to achieve a better discrimination between AD patients and healthy subjects [14].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, it is desirable to develop novel strategies to help in AD detection from the analysis of the electromagnetic brain activity [9,11,12]. Techniques based on spatial filtering can help to achieve this goal, as these algorithms offer additional perspectives to examine EEG and MEG signals [11][12][13][14]. For instance, common spatial patterns (CSP) have been recently applied to enhance characteristics of EEG recordings in mild cognitive impairment (MCI) patients who eventually developed AD [11].…”

Section: Introductionmentioning

confidence: 99%

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Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease

Escudero¹,

Hornero²,

Abásolo³

et al. 2009

Medical Engineering & Physics

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This work studied whether a blind source separation (BSS) and component selection procedure could increase the differences between Alzheimer's disease (AD) patients and control subjects' spectral and non-linear features of magnetoencephalogram (MEG) recordings. MEGs were acquired with a 148-channel whole-head magnetometer from 62 subjects (36 AD patients and 26 controls), who were divided randomly into training and test sets. MEGs were decomposed using the algorithm for multiple unknown signals extraction (AMUSE). The extracted AMUSE components were characterised with two spectral -median frequency and spectral entropy (SpecEn) -and two non-linear features: Lempel-Ziv complexity (LZC) and sample entropy (SampEn). One-way analyses of variance with age as a covariate were applied to the training set to decide which components had the most significant differences between groups. Then, partial reconstructions of the MEGs were computed with these significant components. In the test set, the accuracy and area under the ROC curve (AUC) associated with each partial reconstruction of the MEGs were compared with the case where no BSS-preprocessing was applied. This preprocessing increased the AUCs between 0.013 and 0.227, while the accuracy for SpecEn, LZC and SampEn rose between 6.4% and 22.6%, improving the separation between AD patients and control subjects.

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“…The disadvantage is that they are more time-consuming and less objective, as components typically need to be identified visually. Preprocessing with blind source separation has been shown to improve EEG-based classification between Mild Cognitive Impairment patients subsequently converting to Alzheimer's Disease and healthy controls (Cichocki et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Signal-to-noise ratio of the MEG signal after preprocessing

Gonzalez-Moreno

Aurtenetxe

Lopez-Garcia

et al. 2014

Journal of Neuroscience Methods

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Signal-to-noise ratio of the MEG signal after preprocessing HIGHLIGHTSThe signal-to-noise ratio of event-related fields is used to evaluate the effectiveness of various preprocessing algorithms for magnetoencephalography data. Signal Space Separation algorithms provide approximately a 100% increase in signal to noise ratio. Epoch-based artifact rejection and decomposition methods such as independent component analysis yielded a signal to noise ratio increase of 5-10% and 35% respectively. The use of decomposition methods seems advisable. The evaluation of the signal-to-noise ratio increase can help to guide the choice of preprocessing methods. ABSTRACTBackground: Magnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise. New method: The effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials. Results: Recordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5-10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI. Comparison with existing methods:No previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed. Conclusions: The application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable.

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EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease

Cited by 109 publications

References 34 publications

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG

Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease

Signal-to-noise ratio of the MEG signal after preprocessing

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