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 system’s 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 system’s 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

OFDM/OQAM based terrestrial digital broadcasting

In this thesis an OFDM/ OQAM based system for the next generation terrestrial digital broadcasting standard is proposed and its performance, in terms of spectral efficiency, is compared to OFDM-CD3 performance. As far as coding is concerned, LDPC and BCH codes have been utilized, synchronization and channel estimation have been performed by a superimposed technique and a one tap MMSE equalization scheme has been adopted. As shown by simulation results, an improvement of more than 13% is achieved by OFDM/OQAM system, confirming, in a practical system, the expectation of a significant spectral efficiency improvement.In questa tesi viene proposto un sistema di trasmissione basato su modulazione OFDM/OQAM per il broadcasting video digitale terrestre di nuova generazione. Le prestazioni di tale sistema, in termini di efficienza spettrale, sono confrontate con le prestazioni di un sistema OFDM-CD3. Per quanto concerne la codifica, vengono impiegati codici LDPC e BCH concatenati, la sincronizzazione e la stima di canale vengono ottenuti attraverso una tecnica a sequenza sovrapposta e l'equalizzazione MMSE è a un tappo. Come dimostrato da risultati simulativi, il sistema OFDM/OQAM permettere di ottenere prestazioni maggiori del più del 13%, a conferma dell'aspettativa di un significativo miglioramento dell'efficienza spettrale in un sistema reale

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

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 Sequences 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%

Superimposed sequence channel estimation and pilot aided channel estimation: a throughput comparison

Many wireless standards adopted orthogonal frequency division multiplexing (OFDM) modulation technique with training values allocated in empty slots or multiplexed with data (pilots) to estimate the channel, e.g. IEEE 802.11, HIPERLAN/2, IEEE 802.16, HIPERMAN, terrestrial digital video broadcasting (DVB-T). The pilot insertion clearly causes a waste of bandwidth efficiency. In this paper we consider an alternative estimation scheme with a lower computational complexity at a comparable efficiency loss. We address the simple and effective correlation method as time domain superimposed training based channel estimation technique. As a mean of comparison, the classical OFDM estimation technique derived by a windowed least squared (LS) approach is also studied. For both techniques, we derive an analytical expression of the mean square estimation error as a function of the information signal, noise and superimposed sequence powers. Then, we perform a capacity comparison in a realistic OFDM environment yielded by DVB-T standard specifications. We will show that the superimposed scheme allows the same performance as the windowed LS for 8k operational mode up to a signal to noise ratio (SNR) of 15 dB with a much lower complexity

A multi-task learning approach for the extraction of single-trial evoked potentials

Evoked potentials (EPs) are of great interest in neuroscience, but their measurement is difficult as they are embedded in background spontaneous electroencephalographic (EEG) activity which has a much larger amplitude. The widely used averaging technique requires the delivery of a large number of identical stimuli and yields only an \u201caverage\u201d EP which does not allow the investigation of the possible variability of single-trial EPs. In the present paper, we propose the use of a multi-task learning method (MTL) for the simultaneous extraction of both the average and the single-trial EPs from recorded sweeps. The technique is developed within a Bayesian estimation framework and uses flexible stochastic models to describe the average response and the shifts between the single-trial EPs and this average. Differently from other single-trial estimation approaches proposed in the literature, MTL can provide estimates of both the average and the single-trial EPs in a single stage. In the present paper, MTL is successfully assessed on both synthetic (100 simulated recording sessions with sweeps) and real data (11 subjects with sweeps) relative to a cognitive task carried out for the investigation of the P300 component of the EP

Performance of a P300-based BCI system improved by a Bayesian single-trial ERP estimation technique

Brain computer interface (BCI) systems based on electroencephalographic (EEG) signals are appealing given their non invasiveness, high temporal resolution, portability and low set- up cost. In particular, P300-based BCI does not require any previous long training of the subject. In this work we assess the improvement of classication performance obtained in a P300-based BCI system by \preprocessing" the signal by a Bayesian ltering procedure for single trial ERP estimation. The reference system is the BCI prototype designed at the IRCSS San Camillo Hospital (Venice, Italy), which embeds a preprocessing procedure based on independent component analysis (ICA). Results from two healthy subjects and four patients aected by amyotrophic lateral sclerosis (ALS) show that classication errors relative to the Bayesian approach for single-trial ERP estimation are at least halved with respect to the reference ICA method

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

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 hyperglycaemia. 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