Anahita Goljahani

A novel method for the determination of the EEG individual alpha frequency.

The individual alpha frequency (IAF) is one of the most common tools used to study the variability of EEG rhythms among subjects. Several approaches have been proposed in the literature for IAF determination, including the popular peak frequency (PF) method, the extended band (EB) method, and the transition frequency (TF) method. However, literature techniques for IAF determination are over-reliant on the presence of peaks in the EEG spectrum and are based on qualitative criteria that require visual inspection of every individual EEG spectrum, a task that can be time consuming and difficult to reproduce. In this paper a novel channel reactivity based (CRB) method is proposed for IAF computation. The CRB method is based on quantitative indexes and criteria and relies on task-specific alpha reactivity patterns rather than on the presence of peaks in the EEG spectrum. Application of the technique to EEG signals recorded from 19 subjects during a cognitive task demonstrates the effectiveness of the CRB method and its capability to overcome the limits of PF, EB, and TF approaches.

Superimposed Sequence Versus Pilot Aided Channel Estimations for Next Generation DVB-T Systems

In this paper we analyze the performance of a low complexity superimposed channel estimation technique for orthogonal frequency division multiplexing (OFDM). In particular, an analytical model of interference due to channel estimation errors and imperfect superimposed sequence cancellation at receiver is proposed, whose effectiveness is validated by simulations. Indeed, the significative length of OFDM symbols used in new wide area broadcasting applications makes the superimposed technique a viable alternative to the classical pilot aided technique. For the same computational complexity, the comparison between the two techniques is based on the achievable system throughput both for the current terrestrial digital video broadcasting (DVB-T) standard and for the proposed next generation DVB-T (DVB-T2). Our results show that superimposed technique provides higher bit-rates than the pilot aided technique, with a gain in the range of 4% to 10%.

Hypoglycemia-Related Electroencephalogram Changes Assessed by Multiscale Entropy

BACKGROUND Several clinical studies have shown that low blood glucose (BG) levels affect electroencephalogram (EEG) rhythms through the quantification of traditional indicators based on linear spectral analysis. Nonlinear measures used in the last decades to characterize the EEG in several physiopathological conditions have never been assessed in hypoglycemia. The present study investigates if properties of the EEG signal measured by nonlinear entropy-based algorithms are altered in a significant manner when a state of hypoglycemia is entered. SUBJECTS AND METHODS EEG was acquired from 19 patients with type 1 diabetes during a hyperinsulinemic-euglycemic-hypoglycemic clamp experiment. In parallel, BG was frequently monitored by the standard YSI glucose and lactate analyzer and used to identify two 1-h intervals corresponding to euglycemia and hypoglycemia, respectively. In each subject, the P3-C3 EEG derivation in the two glycemic intervals was assessed using the multiscale entropy (MSE) approach, obtaining measures of sample entropy (SampEn) at various temporal scales. The comparison of how signal irregularity measured by SampEn varies as the temporal scale increases in the two glycemic states provides information on how EEG complexity is affected by hypoglycemia. RESULTS For both glycemic states, the MSE analysis showed that SampEn increases at small time scales and then monotonically decreases as the time scale becomes larger. Comparing the two conditions, SampEn was higher in hypoglycemia only at medium time scales. CONCLUSIONS A decrease in the complexity of EEG occurs when a state of hypoglycemia is entered, because of a degradation of the EEG long-range temporal correlations. Thanks to its ability to assess nonlinear dynamics of the EEG signal, the MSE approach seems to be a useful tool to complement information brought by standard linear indicators and provide new insights on how hypoglycemia affects brain functioning.

Insight into the relationship between brain/behavioral speed and variability in patients with minimal hepatic encephalopathy

OBJECTIVE Intra-individual variability (IIV) of response reaction times (RTs) and psychomotor slowing were proposed as markers of brain dysfunction in patients with minimal hepatic encephalopathy (MHE), a subclinical disorder of the central nervous system frequently detectable in patients with liver cirrhosis. However, behavioral measures alone do not enable investigations into the neural correlates of these phenomena. The aim of this study was to investigate the electrophysiological correlates of psychomotor slowing and increased IIV of RTs in patients with MHE. METHODS Event-related potentials (ERPs), evoked by a stimulus-response (S-R) conflict task, were recorded from a sample of patients with liver cirrhosis, with and without MHE, and a group of healthy controls. A recently presented Bayesian approach was used to estimate single-trial P300 parameters. RESULTS Patients with MHE, with both psychomotor slowing and higher IIV of RTs, showed higher P300 latency jittering and lower single-trial P300 amplitude compared to healthy controls. In healthy controls, distribution analysis revealed that single-trial P300 latency increased and amplitude decreased as RTs became longer; however, in patients with MHE the linkage between P300 and RTs was weaker or even absent. CONCLUSIONS These findings suggest that in patients with MHE, the loss of the relationship between P300 parameters and RTs is related to both higher IIV of RTs and psychomotor slowing. SIGNIFICANCE This study highlights the utility of investigating the relationship between single-trial ERPs parameters along with RT distributions to explore brain functioning in normal or pathological conditions.

An EEGLAB plugin to analyze individual EEG alpha rhythms using the channel reactivity-based method

A recent paper [1] proposed a new technique, termed the channel reactivity-based method (CRB), for characterizing EEG alpha rhythms using individual (IAFs) and channel (CAFs) alpha frequencies. These frequencies were obtained by identifying the frequencies at which the power of the alpha rhythms decreases. In the present study, we present a graphical interactive toolbox that can be plugged into the popular open source environment EEGLAB, making it easy to use CRB. In particular, we illustrate the major functionalities of the software and discuss the advantages of this toolbox for common EEG investigations. The CRB analysis plugin, along with extended documentation and the sample dataset utilized in this study, is freely available on the web at http://bio.dei.unipd.it/crb/.

Superimposed technique for OFDM/OQAM based digital terrestrial television broadcasting

Orthogonal frequency division multiplexing offset quadrature amplitude modulation (OFDM/OQAM) is a multi carrier modulation using staggered transmission on the I and Q axes. It uses as well an optimized non-rectangular pulse shaping. There are several advantages with respect to the conventional OFDM modulation, while the main drawback is the intrinsic intersymbol interference, hindering e.g. a proper channel estimation. In this paper to overcome the effect of the intrinsic interference we propose to estimate the channel through the superimposed correlation-based method. We propose then to exploit the superimposed sequence to achieve time synchronization as well. In particular, in this paper we show that, under the realistic conditions (e.g regarding the spectrum masks), the proposed techniques work very well and allow to achieve a significant improvement in spectral efficiency.

Preprocessing by a Bayesian Single-Trial Event-Related Potential Estimation Technique Allows Feasibility of an Assistive Single-Channel P300-Based Brain-Computer Interface

A major clinical goal of brain-computer interfaces (BCIs) is to allow severely paralyzed patients to communicate their needs and thoughts during their everyday lives. Among others, P300-based BCIs, which resort to EEG measurements, have been successfully operated by people with severe neuromuscular disabilities. Besides reducing the number of stimuli repetitions needed to detect the P300, a current challenge in P300-based BCI research is the simplification of systems setup and maintenance by lowering the number N of recording channels. By using offline data collected in 30 subjects (21 amyotrophic lateral sclerosis patients and 9 controls) through a clinical BCI with N = 5 channels, in the present paper we show that a preprocessing approach based on a Bayesian single-trial ERP estimation technique allows reducing N to 1 without affecting the systems accuracy. The potentially great benefit for the practical usability of BCI devices (including patient acceptance) that would be given by the reduction of the number N of channels encourages further development of the present study, for example, in an online setting.

Hypoglycaemia-Related EEG Changes Assessed by Approximate Entropy

Several studies performed in human beings demonstrated that glucose concentration in blood can affect EEG rhythms, typically evaluated by standard spectral analysis techniques. In the present work, we investigate if EEG complexity assessed by a nonlinear algorithm, Approximate Entropy (ApEn), reflects changes of glucose concentration levels during an induced hypoglycaemia experiment. In particular, in 10 type-1 diabetic volunteers, ApEn was computed from the P3-C3 EEG channel at different temporal scales and then correlated to the three classes of glycaemic states, i.e. hyper/eu/hypo-glycaemia. Results show that, for all considered temporal scales, EEG complexity in hypoglycaemia is lower, with statistical significance, than in eu- and in hyper-glycaemia. No statistically significant difference can be evidenced between ApEn values in hyper- and in eu-glycaemic states. In conclusion, in addition to power indexes in the four traditional EEG bands, other indicators, and ApEn in particular, can be used to quantitatively investigate glucose-related EEG changes.

Variability of EEG Theta Power Modulation in Type 1 Diabetics Increases during Hypo-glycaemia

EEG spectral content has been widely investigated to illuminate cognitive processes and assess clinical conditions of patients. In particular, increase of powers in EEG low frequency bands were proved to reflect low levels of glucose concentration in the blood, i.e., hypo-glycaemia states. In the present work we investigate if and how levels of glucose concentrations affect the time course of EEG power modulations in the conventional theta, alpha and beta bands. To this aim, the reactivity index ρ, recently introduced for characterizing individual modulations of alpha rhythms, was utilized to quantify, for each band, EEG power modulations at the P3-C3 channel during induced hypo-glycaemia experiments performed with 10 type-1 diabetic volunteers. Results show that, in any glycemic state, i.e., hyper/eu/hypo-glycaemia, ρ continuously vary in any band, alternating increases and decreases of powers with respect to preceding intervals. In particular, in the theta band, the variability of EEG power modulations during hypo-glycaemia (measured by the ρ sample standard deviation) is significantly higher than in hyper- and eu- gly- caemia. This suggests that the variability of ρ in the theta band can be a useful indicator to quantitatively investigate glucose-related EEG changes.