Significance of Time Scales in Nonlinear Dynamical Analysis of Electroencephalogram Signals

We propose to show in this paper, that the time series obtained from biological systems such as human brain are invariably nonstationary because of different time scales involved in the dynamical process. This makes the invariant parameters time dependent. We made a global analysis of the EEG data obtained from the eight locations on the skull space and studied simultaneously the dynamical characteristics from various parts of the brain. We have proved that the dynamical parameters are sensitive to the time scales and hence in the study of brain one must identify all relevant time scales involved in the process to get an insight in the working of brain.

Epileptic seizures as condensed sleep: an analysis of network dynamics from electroencephalogram signals

Both deepening sleep and evolving epileptic seizures are associated with increasing slow-wave activity. Larger-scale functional networks derived from electroencephalogram indicate that in both transitions dramatic changes of communication between brain areas occur. During seizures these changes seem to be 'condensed', because they evolve more rapidly than during deepening sleep. Here we set out to assess quantitatively functional network dynamics derived from electroencephalogram signals during seizures and normal sleep. Functional networks were derived from electroencephalogram signals from wakefulness, light and deep sleep of 12 volunteers, and from pre-seizure, seizure and post-seizure time periods of 10 patients suffering from focal onset pharmaco-resistant epilepsy. Nodes of the functional network represented electrical signals recorded by single electrodes and were linked if there was non-random cross-correlation between the two corresponding electroencephalogram signals. Network dynamics were then characterized by the evolution of global efficiency, which measures ease of information transmission. Global efficiency was compared with relative delta power. Global efficiency significantly decreased both between light and deep sleep, and between pre-seizure, seizure and post-seizure time periods. The decrease of global efficiency was due to a loss of functional links. While global efficiency decreased significantly, relative delta power increased except between the time periods wakefulness and light sleep, and pre-seizure and seizure. Our results demonstrate that both epileptic seizures and deepening sleep are characterized by dramatic fragmentation of larger-scale functional networks, and further support the similarities between sleep and seizures.

Electroencephalogram cepstral distances in alzheimer’s disease diagnosis

Alzheimer's disease (AD) represents one ofthe greatest public health challenges worldwide nowadays, because it affects millions of people ali o ver the world and it is expected that the disease will increase considerably in the near future. This study is the first application attempt of cepstral analysis on Electroencephalogram (EEG) signals to find new parameters in arder to achieve a better differentiation belween EEGs of AD patients and Control subjects. The results show that the methodology that uses a combined Wavelet (WT) Biorthogonal (Bior) 3.5 and cepstrum analysis was able to describe the EEG dynamics with a higher discriminative power than the other WTs/spectmm methodologies m previous studies. The most important significance figures were found in cepstral distances between cepstrums oftheta and alpha bands (p=0. 00006<0. 05).

Complexity quantification of dense array EEG using sample entropy analysis

n this paper, a time series complexity analysis of dense array electroencephalogram signals is carried out using the recently introduced Sample Entropy (SampEn) measure. This statistic quantifies the regularity in signals recorded from systems that can vary from the purely deterministic to purely stochastic realm. The present analysis is conducted with an objective of gaining insight into complexity variations related to changing brain dynamics for EEG recorded from the three cases of passive, eyes closed condition, a mental arithmetic task and the same mental task carried out after a physical exertion task. It is observed that the statistic is a robust quantifier of complexity suited for short physiological signals such as the EEG and it points to the specific brain regions that exhibit lowered complexity during the mental task state as compared to a passive, relaxed state. In the case of mental tasks carried out before and after the performance of a physical exercise, the statistic can detect the variations brought in by the intermediate fatigue inducing exercise period. This enhances its utility in detecting subtle changes in the brain state that can find wider scope for applications in EEG based brain studies.

Development of an active orthosis prototype for lower limbs

ARAUJO, Márcio V. ; ALSINA, Pablo J. ; MEDEIROS, Adelardo A. D. ; PEREIRA, Jonathan P.P. ; DOMINGOS, Elber C. ; ARAÚJO, Fábio M.U. ; SILVA, Jáder S. . Development of an Active Orthosis Prototype for Lower Limbs. In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING, 20., 2009, Gramado, RS. Proceedings… Gramado, RS: [s. n.], 2009

Development of an active orthosis prototype for lower limbs

ARAUJO, Márcio V. ; ALSINA, Pablo J. ; MEDEIROS, Adelardo A. D. ; PEREIRA, Jonathan P.P. ; DOMINGOS, Elber C. ; ARAÚJO, Fábio M.U. ; SILVA, Jáder S. . Development of an Active Orthosis Prototype for Lower Limbs. In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING, 20., 2009, Gramado, RS. Proceedings… Gramado, RS: [s. n.], 2009

Development of an active orthosis prototype for lower limbs

ARAUJO, Márcio V. ; ALSINA, Pablo J. ; MEDEIROS, Adelardo A. D. ; PEREIRA, Jonathan P.P. ; DOMINGOS, Elber C. ; ARAÚJO, Fábio M.U. ; SILVA, Jáder S. . Development of an Active Orthosis Prototype for Lower Limbs. In: INTERNATIONAL CONGRESS OF MECHANICAL ENGINEERING, 20., 2009, Gramado, RS. Proceedings… Gramado, RS: [s. n.], 2009

Evaluation of two minimally invasive techniques for electroencephalogram recording in wild or freely behaving animals.

Insight into the function of sleep may be gained by studying animals in the ecological context in which sleep evolved. Until recently, technological constraints prevented electroencephalogram (EEG) studies of animals sleeping in the wild. However, the recent development of a small recorder (Neurologger 2) that animals can carry on their head permitted the first recordings of sleep in nature. To facilitate sleep studies in the field and to improve the welfare of experimental animals, herein, we test the feasibility of using minimally invasive surface and subcutaneous electrodes to record the EEG in barn owls. The EEG and behaviour of four adult owls in captivity and of four chicks in a nest box in the field were recorded. We scored a 24-h period for each adult bird for wakefulness, slow-wave sleep (SWS), and rapid-eye movement (REM) sleep using 4 s epochs. Although the quality and stability of the EEG signals recorded via subcutaneous electrodes were higher when compared to surface electrodes, the owls' state was readily identifiable using either electrode type. On average, the four adult owls spent 13.28 h awake, 9.64 h in SWS, and 1.05 h in REM sleep. We demonstrate that minimally invasive methods can be used to measure EEG-defined wakefulness, SWS, and REM sleep in owls and probably other animals.

Electroencephalogram paroxysmal theta characterizes cataplexy in mice and children.

Astute control of brain activity states is critical for adaptive behaviours and survival. In mammals and birds, electroencephalographic recordings reveal alternating states of wakefulness, slow wave sleep and paradoxical sleep (or rapid eye movement sleep). This control is profoundly impaired in narcolepsy with cataplexy, a disease resulting from the loss of orexin/hypocretin neurotransmitter signalling in the brain. Narcolepsy with cataplexy is characterized by irresistible bouts of sleep during the day, sleep fragmentation during the night and episodes of cataplexy, a sudden loss of muscle tone while awake and experiencing emotions. The neural mechanisms underlying cataplexy are unknown, but commonly thought to involve those of rapid eye movement-sleep atonia, and cataplexy typically is considered as a rapid eye movement sleep disorder. Here we reassess cataplexy in hypocretin (Hcrt, also known as orexin) gene knockout mice. Using a novel video/electroencephalogram double-blind scoring method, we show that cataplexy is not a state per se, as believed previously, but a dynamic, multi-phased process involving a reproducible progression of states. A knockout-specific state and a stereotypical paroxysmal event were introduced to account for signals and electroencephalogram spectral characteristics not seen in wild-type littermates. Cataplexy almost invariably started with a brief phase of wake-like electroencephalogram, followed by a phase featuring high-amplitude irregular theta oscillations, defining an activity profile distinct from paradoxical sleep, referred to as cataplexy-associated state and in the course of which 1.5-2 s high-amplitude, highly regular, hypersynchronous paroxysmal theta bursts (∼7 Hz) occurred. In contrast to cataplexy onset, exit from cataplexy did not show a predictable sequence of activities. Altogether, these data contradict the hypothesis that cataplexy is a state similar to paradoxical sleep, even if long cataplexies may evolve into paradoxical sleep. Although not exclusive to overt cataplexy, cataplexy-associated state and hypersynchronous paroxysmal theta activities are highly enriched during cataplexy in hypocretin/orexin knockout mice. Their occurrence in an independent narcolepsy mouse model, the orexin/ataxin 3 transgenic mouse, undergoing loss of orexin neurons, was confirmed. Importantly, we document for the first time similar paroxysmal theta hypersynchronies (∼4 Hz) during cataplexy in narcoleptic children. Lastly, we show by deep recordings in mice that the cataplexy-associated state and hypersynchronous paroxysmal theta activities are independent of hippocampal theta and involve the frontal cortex. Cataplexy hypersynchronous paroxysmal theta bursts may represent medial prefrontal activity, associated in humans and rodents with reward-driven motor impulse, planning and conflict monitoring.

Investigation of ICA algorithms for feature extraction of EEG signals in discrimination of Alzheimer disease

In this paper we present a quantitative comparisons of different independent component analysis (ICA) algorithms in order to investigate their potential use in preprocessing (such as noise reduction and feature extraction) the electroencephalogram (EEG) data for early detection of Alzhemier disease (AD) or discrimination between AD (or mild cognitive impairment, MCI) and age-match control subjects.

Evaluation of a piezoelectric system as an alternative to electroencephalogram/ electromyogram recordings in mouse sleep studies.

STUDY OBJECTIVES: Traditionally, sleep studies in mammals are performed using electroencephalogram/electromyogram (EEG/EMG) recordings to determine sleep-wake state. In laboratory animals, this requires surgery and recovery time and causes discomfort to the animal. In this study, we evaluated the performance of an alternative, noninvasive approach utilizing piezoelectric films to determine sleep and wakefulness in mice by simultaneous EEG/EMG recordings. The piezoelectric films detect the animal's movements with high sensitivity and the regularity of the piezo output signal, related to the regular breathing movements characteristic of sleep, serves to automatically determine sleep. Although the system is commercially available (Signal Solutions LLC, Lexington, KY), this is the first statistical validation of various aspects of sleep. DESIGN: EEG/EMG and piezo signals were recorded simultaneously during 48 h. SETTING: Mouse sleep laboratory. PARTICIPANTS: Nine male and nine female CFW outbred mice. INTERVENTIONS: EEG/EMG surgery. MEASUREMENTS AND RESULTS: The results showed a high correspondence between EEG/EMG-determined and piezo-determined total sleep time and the distribution of sleep over a 48-h baseline recording with 18 mice. Moreover, the piezo system was capable of assessing sleep quality (i.e., sleep consolidation) and interesting observations at transitions to and from rapid eye movement sleep were made that could be exploited in the future to also distinguish the two sleep states. CONCLUSIONS: The piezo system proved to be a reliable alternative to electroencephalogram/electromyogram recording in the mouse and will be useful for first-pass, large-scale sleep screens for genetic or pharmacological studies. CITATION: Mang GM, Nicod J, Emmenegger Y, Donohue KD, O'Hara BF, Franken P. Evaluation of a piezoelectric system as an alternative to electroencephalogram/electromyogram recordings in mouse sleep studies.

Signatures of Higher Functions of Brain Dynamics in Electroencephalogram: A Nonlinear Analysis

The brain with its highly complex structure made up of simple units,imterconnected information pathways and specialized functions has always been an object of mystery and sceintific fascination for physiologists,neuroscientists and lately to mathematicians and physicists. The stream of biophysicists are engaged in building the bridge between the biological and physical sciences guided by a conviction that natural scenarios that appear extraordinarily complex may be tackled by application of principles from the realm of physical sciences. In a similar vein, this report aims to describe how nerve cells execute transmission of signals ,how these are put together and how out of this integration higher functions emerge and get reflected in the electrical signals that are produced in the brain.Viewing the E E G Signal through the looking glass of nonlinear theory, the dynamics of the underlying complex system-the brain ,is inferred and significant implications of the findings are explored.

Rejoinder to 'Performance of different synchronisation measures in real data: A case study on electroencephalograhic signals

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga et al. [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga et al. in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga et al. in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.

Comment on "Performance of different synchronization measures in real data: A case study on electroencephalographic signals"

We agree with Duckrow and Albano [Phys. Rev. E 67, 063901 (2003)] and Quian Quiroga [Phys. Rev. E 67, 063902 (2003)] that mutual information (MI) is a useful measure of dependence for electroencephalogram (EEG) data, but we show that the improvement seen in the performance of MI on extracting dependence trends from EEG is more dependent on the type of MI estimator rather than any embedding technique used. In an independent study we conducted in search for an optimal MI estimator, and in particular for EEG applications, we examined the performance of a number of MI estimators on the data set used by Quian Quiroga in their original study, where the performance of different dependence measures on real data was investigated [Phys. Rev. E 65, 041903 (2002)]. We show that for EEG applications the best performance among the investigated estimators is achieved by k-nearest neighbors, which supports the conjecture by Quian Quiroga in Phys. Rev. E 67, 063902 (2003) that the nearest neighbor estimator is the most precise method for estimating MI.

Automated artifact removal from the electroencephalogram; a comparative study

Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.