EEG data during 'peaceful' auditory processing at different tempi

This dataset accompanies the publication by Nicolaou et al. (2017), "Directed motor-auditory EEG connectivity is modulated by music tempo", Front Hum. Neurosci., doi: 10.3389/fnhum.2017.00502. The purpose of the research activity in which the data were collected was to investigate how tempo affects the EEG connectivity between different electrodes. For this purpose the participants listened to 'peaceful' music clips at 4 different tempi (50, 100, 150 and 200 beats per minute). To isolate changes related to tempo from changes related to acoustic stimulation, the participants also listened to noise clips generated from the original music clips. Differences in connectivity from a resting state were also studied to isolate the effect of acoustic stimulation. The dataset contains the EEG data while the participants listened to the music and noise clips, and the EEG data from resting state

Consciousness Detection in a Complete Locked-in Syndrome Patient through Multiscale Approach Analysis

Completely locked-in state (CLIS) patients are unable to speak and have lost all muscle movement. From the external view, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to still be conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is important to find alternative ways to re-establish communication with these patients during periods of awareness, and one such alternative is through a brain–computer interface (BCI). In this study, multiscale-based methods (multiscale sample entropy, multiscale permutation entropy and multiscale Poincaré plots) were applied to analyze electrocorticogram signals from a CLIS patient to detect the underlying consciousness level. Results from these different methods converge to a specific period of awareness of the CLIS patient in question, coinciding with the period during which the CLIS patient is recorded to have communicated with an experimenter. The aim of the investigation is to propose a methodology that could be used to create reliable communication with CLIS patients

EEG-Based Automatic Classification of ‘Awake’ versus ‘Anesthetized’ State in General Anesthesia Using Granger Causality

BACKGROUND: General anesthesia is a reversible state of unconsciousness and depression of reflexes to afferent stimuli induced by administration of a "cocktail" of chemical agents. The multi-component nature of general anesthesia complicates the identification of the precise mechanisms by which anesthetics disrupt consciousness. Devices that monitor the depth of anesthesia are an important aide for the anesthetist. This paper investigates the use of effective connectivity measures from human electrical brain activity as a means of discriminating between 'awake' and 'anesthetized' state during induction and recovery of consciousness under general anesthesia. METHODOLOGY/PRINCIPAL FINDINGS: Granger Causality (GC), a linear measure of effective connectivity, is utilized in automated classification of 'awake' versus 'anesthetized' state using Linear Discriminant Analysis and Support Vector Machines (with linear and non-linear kernel). Based on our investigations, the most characteristic change of GC observed between the two states is the sharp increase of GC from frontal to posterior regions when the subject was anesthetized, and reversal at recovery of consciousness. Features derived from the GC estimates resulted in classification of 'awake' and 'anesthetized' states in 21 patients with maximum average accuracies of 0.98 and 0.95, during loss and recovery of consciousness respectively. The differences in linear and non-linear classification are not statistically significant, implying that GC features are linearly separable, eliminating the need for a complex and computationally expensive non-linear classifier. In addition, the observed GC patterns are particularly interesting in terms of a physiological interpretation of the disruption of consciousness by anesthetics. Bidirectional interaction or strong unidirectional interaction in the presence of a common input as captured by GC are most likely related to mechanisms of information flow in cortical circuits. CONCLUSIONS/SIGNIFICANCE: GC-based features could be utilized effectively in a device for monitoring depth of anesthesia during surgery

The use of analytical techniques for the rapid detection of microbial spoilage and adulteration in milk

Milk is an important nutritious component of our diet consumed by most humans on a daily basis. Microbiological spoilage affects its safe use and consumption, its organoleptic properties and is a major part of its quality control process. European Union legislation and the Hazard Analysis and the Critical Control Point (HACCP) system in the dairy industry are therefore in place to maintain both the safety and the quality of milk production in the dairy industry. A main limitation of currently used methods of milk spoilage detection in the dairy industry is the time-consuming and sometimes laborious turnover of results. Attenuated total reflectance (ATR) and high throughput (HT) Fourier transform infrared (FTIR) spectroscopy metabolic fingerprinting techniques were investigated for their speed and accuracy in the enumeration of viable bacteria in fresh pasteurized cows' milk. Data analysis was performed using principal component-discriminant function analysis (PC-DFA) and partial least squares (PLS) multivariate statistical techniques. Accurate viable microbial loads were rapidly obtained after minimal sample preparation, especially when FTIR was combined with PLS, making it a promising technique for routine use by the dairy industry. FTIR and Raman spectroscopies in combination with multivariate techniques were also explored as rapid detection and enumeration techniques of S. aureus, a common milk pathogen, and Lactococcus lactis subsp cremoris, a common lactic acid bacterium (LAB) and potential antagonist of S. aureus, in ultra-heat treatment milk. In addition, the potential growth interaction between the two organisms was investigated. FTIR spectroscopy in combination with PLS and kernel PLS (KPLS) appeared to have the greatest potential with good discrimination and enumeration attributes for the two bacterial species even when in co-culture without previous separation. Furthermore, it was shown that the metabolic effect of L. cremoris predominates when in co-culture with S. aureus in milk but with minimal converse growth interaction between the two microorganisms and therefore potential implications in the manufacture of dairy products using LAB. The widespread and high consumption of milk make it a target for potential financial gain through adulteration with cheaper products reducing quality, breaking labeling and patent laws and potentially leading to dire health consequences. The time consuming and laborious nature of currently used analytical techniques in milk authentication enabled the study of FTIR spectroscopy and matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF-MS) as rapid analytical techniques in quantification of milk adulteration, using binary and tertiary fresh whole cows', goats' and sheep's milk mixture samples. Chemometric data analysis was performed using PLS and KPLS multivariate analyses. Overall, results indicated that both techniques have excellent enumeration and detection attributes for use in milk adulteration with good prospects for potential use in the dairy industry.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Directed motor-auditory EEG connectivity is modulated by music tempo

Beat perception is fundamental to how we experience music, and yet the mechanism behind this spontaneous building of the internal beat representation is largely unknown. Existing findings support links between the tempo (speed) of the beat and enhancement of electroencephalogram (EEG) activity at tempo-related frequencies, but there are no studies looking at how tempo may affect the underlying long-range interactions between EEG activity at different electrodes. The present study investigates these long-range interactions using EEG activity recorded from 21 volunteers listening to music stimuli played at 4 different tempi (50, 100, 150 and 200 beats per minute). The music stimuli consisted of piano excerpts designed to convey the emotion of “peacefulness”. Noise stimuli with an identical acoustic content to the music excerpts were also presented for comparison purposes. The brain activity interactions were characterized with the imaginary part of coherence (iCOH) in the frequency range 1.5–18 Hz (δ, θ, α and lower β) between all pairs of EEG electrodes for the four tempi and the music/noise conditions, as well as a baseline resting state (RS) condition obtained at the start of the experimental task. Our findings can be summarized as follows: (a) there was an ongoing long-range interaction in the RS engaging fronto-posterior areas; (b) this interaction was maintained in both music and noise, but its strength and directionality were modulated as a result of acoustic stimulation; (c) the topological patterns of iCOH were similar for music, noise and RS, however statistically significant differences in strength and direction of iCOH were identified; and (d) tempo had an effect on the direction and strength of motor-auditory interactions. Our findings are in line with existing literature and illustrate a part of the mechanism by which musical stimuli with different tempi can entrain changes in cortical activity

Autoregressive Model Order Estimation Criteria for Monitoring Awareness during Anaesthesia

Part 3: Medical Informatics and Biomedical EngineeringInternational audienceThis paper investigates the use of autoregressive (AR) model order estimation criteria for monitoring awareness during anaesthesia. The Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC) were applied to electroencephalogram (EEG) data from 29 patients, obtained during surgery, to estimate the optimum multivariate AR model order. Maintenance of anaesthesia was achieved with propofol, desflurane or sevoflurane. The optimum orders estimated from the BIC reliably decreased during anaesthetic-induced unconsciousness, as opposed to AIC estimates, and, thus, successfully tracked the loss of awareness. This likely reflects the decrease in the complexity of the brain activity during anaesthesia. In addition, AR order estimates sharply increased for diathermy-contaminated EEG segments. Thus, the BIC could provide a simple and reliable means of identifying awareness during surgery, as well as automatic exclusion of diathermy-contaminated EEG segments