Electroencephalography Acquisition Research Articles

A 22.3-Bit Third-Order Delta-Sigma Modulator for EEG Signal Acquisition Systems

This paper presents a high resolution delta-sigma modulator for continuous acquisition of electroencephalography (EEG) signals. The third-order single-loop architecture with a 1-bit quantizer is adopted to achieve 22.3-bit resolution. The effects of thermal noise on the performance of the delta-sigma modulator are analyzed to reasonably allocate the switched-capacitor sizes for optimal signal to noise ratio (SNR) and minimum chip area. The coefficients in feedback path and input path are optimized to avoid the signal distortion under the full-scale input voltage range with almost no increase in total capacitance sizes. Fabricated in 0.5 µm CMOS technology and powered by a 5 V voltage supply, the proposed delta-sigma modulator can achieve 136 dB peak SNR with 16 Hz input and 137 dB dynamic range in 100 Hz signal bandwidth with an oversampling ratio of 512. The modulator dissipates 700 µA. The core chip area is 1.96 mm2. The modulator occupies 1.41 mm2 and the decimator occupies 0.55 mm2.

NeuXus open-source tool for real-time artifact reduction in simultaneous EEG-fMRI

The simultaneous acquisition of electroencephalography and functional magnetic resonance imaging (EEG-fMRI) allows the complementary study of the brain's electrophysiology and hemodynamics with high temporal and spatial resolution. One application with great potential is neurofeedback training of targeted brain activity, based on the real-time analysis of the EEG and/or fMRI signals. This depends on the ability to reduce in real time the severe artifacts affecting the EEG signal acquired with fMRI, mainly the gradient and pulse artifacts. A few methods have been proposed for this purpose, but they are either slow, hardware-dependent, publicly unavailable, or proprietary software. Here, we present a fully open-source and publicly available tool for real-time EEG artifact reduction in simultaneous EEG-fMRI recordings that is fast and applicable to any hardware. Our tool is integrated in the Python toolbox NeuXus for real-time EEG processing and adapts to a real-time scenario well-established artifact average subtraction methods combined with a long short-term memory network for R peak detection. We benchmarked NeuXus on three different datasets, in terms of artifact power reduction and background signal preservation in resting state, alpha-band power reactivity to eyes closure, and event-related desynchronization during motor imagery. We showed that NeuXus performed at least as well as the only available real-time tool for conventional hardware setups (BrainVision's RecView) and a well-established offline tool (EEGLAB's FMRIB plugin). We also demonstrated NeuXus’ real-time ability by reporting execution times under 250 ms. In conclusion, we present and validate the first fully open-source and hardware-independent solution for real-time artifact reduction in simultaneous EEG-fMRI studies.

Tuberculose pulmonaire sur maladie de Takayasu compliquée de dissection de l’artère carotide commune

Previous studies have observed lower visual cortex activation for visual processing in cochlear implant (CI) users compared to normal hearing controls, while others reported enhanced visual speechreading abilities in CI users. The present work investigated whether lower visual cortical activation for visual processing can be explained by a more efficient visual sensory encoding in CI users. Specifically, we investigated whether CI users show enhanced stimulus-specific adaptation for visual stimuli compared to controls. Auditory sensory adaptation was also investigated to explore the sensory specificity of the predicted effect. Twenty post-lingually deafened adult CI users and twenty age-matched controls were presented with repeated visual and auditory stimuli during simultaneous acquisition of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). By integrating EEG and fNIRS signals we found significantly enhanced visual adaptation and lower visual cortex activation in CI users compared to controls. That is, responses to repeated visual stimuli decreased more prominently in CI users than in controls. The results suggest that CI users process visual stimuli more efficiently than controls.

Electroencephalographic analysis of brain activity after interventions with transcranial direct current stimulation over the motor cortex: a systematic review

This study aims to determine processes and procedures for the analysis of the behavior of brain signals in clinical trials before and after different intervention protocols involving transcranial direct current stimulation (tDCS). In this study, PubMed and Cochrane databases were searched for original articles describing clinical trials that used electroencephalography to analyze the behavior of brain signals after submitted to transcranial direct current stimulation. Searches were conducted with MeSH terms for clinical trials to determine the behavior of brain activity over time through evaluations performed before and after a period of intervention involving tDCS. The search was limited to articles published in English in the last ten years. It was found 257 articles during the initial search, where 14 of which were submitted to an appraisal of methodological quality, and 4 were considered to have adequate quality. Based on the results, there is inconsistency in the processes and procedures used for the acquisition of brain signals, even when limiting the search to clinical trials offering an analysis of evaluations performed before and after clinical interventions. Moreover, researchers seek to offer guidance regarding the acquisition of electroencephalography (EEG) signals to favor the quality of the data but fail to standardize regions for these acquisitions in the analysis of the results of tDCS.

A Wireless Multifunctional SSVEP-Based Brain–Computer Interface Assistive System

Several kinds of brain–computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their nonportability, impracticality, and inconvenience because of the adoption of wired or invasive electroencephalography acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this paper, we propose a wireless, noninvasive and multifunctional assistive system which integrates steady state visually evoked potential-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: 1) video entertainment; 2) video calling; and 3) active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate achieved is 90.91% and 24.94 bit per min, respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.

Low-Power High-Input-Impedance EEG Signal Acquisition SoC With Fully Integrated IA and Signal-Specific ADC for Wearable Applications.

This paper presents a low-power high-input-impedance analog-front-end (AFE) design including an instrumentational-amplifier (IA) and a neural-signal-specific ADC (NSS-ADC) for continuous acquisition of electroencephalography (EEG) signals. In the proposed AFE, low-voltage low-power design techniques are used to reduce the power consumption of the whole system. Furthermore, by utilizing the proposed NSS-ADC, high-amplitude EEG spikes, which convey more important information, are converted with higher resolutions, while the background-noise (B-Noise) of the EEG signal is converted with the lowest resolution. Hence, when the NSS-ADC enters the inactive region, the resolution- and DAC- controlling-units (RCU and DCU) set the analog and digital components of the NSS-ADC into off mode, which leads to power reduction. Based on measurement results, the AFE consumes a power of 3.7 μW under the sampling rate of 20 KS/s. In the proposed AFE, to avoid signal attenuation, active-electrodes (AEs) are utilized to enhance the input impedance of the AFE up to 102 GΩ and 5.2 GΩ at 1 Hz and 20 Hz, respectively. In addition, by using circuit-design techniques the input-referred-noise is reduced as low as 1.5 μVrms over 0.5-1.2 kHz. Finally, by using a transconductance-driven-right-leg (TDRL) and a common-mode-feedback (CMFB) blocks, a common-mode-rejection-ratio (CMRR) of 108 dB is achieved.

Design of a portable SSVEP signal acquisition system

Objective To design a portable electroencephalography(EEG) acquisition system to acquire and analysis steady-state visual potentials (SSVEP). Methods The microprocessor MSP432P401 series MCU was used to control the high-performance integrated analog front end ADS1299 to realize the acquisition, amplification and analog-to-digital (AD) conversion of EEG signals. The digital EEG signal is sent to the host computer for processing by WIFI. Spontaneous EEG signals and steady-state visually evoked EEG signals from 3 healthy subjects were collected to verify system performance. Results The collected signal had a clear α-wave rhythm of closed-eye spontaneous EEG signals. The power spectrum density shows that the steady-state visually induced EEG signal frequency and harmonic frequency peak at the corresponding stimulation frequency, indicating that the system works normally and the performance is good. Conclusions The designed portable EEG acquisition system can accurately collect the spontaneous and induced EEG signals of the human body, which provides technical support for the clinical application of SSVEP technology. Key words: Electroencephalography acquisition; Steady-state visual potentials; ADS1299; WIFI

How Expertise Changes Cortical Sources of EEG Rhythms and Functional Connectivity in Divers Under Simulated Deep-Sea Conditions.

Although the recent years have witnessed a growing interest in functional connectivity (FC) through brain sources, the FC in extreme situations has not been completely elucidated. This paper is aimed at investigating whether the expertise acquired during the deep-sea diving is reflected in FC in a group of professional divers (PDs) compared to a group of new divers (NDs), and how it could affect the concentration and stress levels. The sources of brain frequency rhythms, derived by the electroencephalography acquisition in a hyperbaric chamber, were extracted in different frequency bands and the corresponding FC was estimated in order to compare the two groups. The results highlighted a significant decrease of the alpha source in PDs during air breathing and a significant increase of the upper beta source over central areas at the beginning of post-oxygen air, as well as an increase of beta FC between fronto-temporal regions in the last minutes of oxygen breathing and in the early minutes of post-oxygen air. This provides evidence in support of the hypothesis that experience and expertise differences would modulate brain networks. These experiments provided the unique opportunity of investigating the impact of the neurophysiological activity in simulated critical scenarios in view of the investigation in real sea-water experiments.

Domain Transfer Multiple Kernel Boosting for Classification of EEG Motor Imagery Signals

The application of wireless sensors in the brain-computer interface (BCI) system provides great convenience for the acquisition of electroencephalography (EEG) signals. However, a large amount of training data is needed to build the classification architectures used in motor imagery (MI) brain-computer interface (BCI), which is time-consuming to generate. To address this issue, transfer learning has gained significant attention in a small sample setting BCI system. The transfer learning methods have shown promising results by leveraging labeled patterns from the source domain to learn robust classifiers for the target domain, which has only a limited number of labeled samples. However, the successful application of such approaches in a motor imagery BCI remains limited. In this paper, we present a novel framework called domain transfer multiple kernel boosting (DTMKB), which extends the DTMKL algorithms by applying boosting techniques for learning kernel-based classifiers with the transfer of multiple kernels. Based on the proposed framework, we examined their empirical performance in comparison to several state-of-the-art algorithms on two MI task datasets. DTMKB yields the best performance for all datasets and achieves the best average classification accuracy 87.60%, 76.00%, 74.66%, and 74.13%, respectively. In particular, the proposed framework can be applied successfully in a small sample of EEG motor imagery signals.

Motor imagery-assisted brain-computer interface for gait retraining in neurorehabilitation in chronic stroke

Stroke is the most common cause for physical disability and impairments to lower limb function remain one of its most debilitating symptom. Motor imagery (MI), as a safe, self-paced technique, has been shown to effectively facilitating the effects of motor practice. When combined with brain-computer interface (MI-BCI), it also demonstrates an improvement in stroke motor recovery. A feasibility trial was carried out to investigate the effect of MI-BCI neurofeedback in chronic hemiplegic lower limb rehabilitation. The neurophysiological correlates to clinical outcomes was also studied by using, transcranial magnetic stimulation (TMS). Subjects ( n = 13) with more than 9 months post-stroke and Functional Ambulation Category 3–4 underwent 12 sessions of MI-BCI gait training, at a frequency of thrice a week. Subjects were instructed to perform a MI task whereby they imagined themselves walking properly with both legs. If the MI task is performed correctly as detected via electroencephalography acquisition, a pair of cartoon footprints in the monitor will be activated to walk forward. Each MI-BCI session includes 160 MI trials with resting interval every 40 trials. Timed up-to-go test and 10 meter walk test, as well as the resting motor threshold measured by TMS were performed before, after and 6 weeks after MI-BCI gait training. It was shown that MI-BCI was safe and well tolerated by stroke subjects. Both walking speed and balance improved after MI-BCI gait training ( Fig. 1 ). This was in line with an increase in the corticospinal activity in the contralesional M1 motor cortex ( Fig. 2 ). MI-BCI could improve mobility in chronic stroke patients with residual mobility impairment. The study also suggested that the contralesional motor cortex is involved in the recovery of mobility.

A real-time wireless wearable electroencephalography system based on Support Vector Machine for encephalopathy daily monitoring

Wearable electroencephalography systems of out-of-hospital can both provide complementary recordings and offer several benefits over long-term monitoring. However, several limitations were present in these new-born systems, for example, uncomfortable for wearing, inconvenient for retrieving the recordings by patients themselves, unable to timely provide accurate classification, and early warning information. Therefore, we proposed a wireless wearable electroencephalography system for encephalopathy daily monitoring, named as Brain-Health, which focused on the following three points: (a) the monitoring device integrated with electroencephalography acquisition sensors, signal processing chip, and Bluetooth, attached to a sport hat or elastic headband; (b) the mobile terminal with dedicated application, which is not only for continuous recording and displaying electroencephalography signal but also for early warning in real time; and (c) the encephalopathy’s classification algorithm based on intelligent Support Vector Machine, which is used in a new application of wearable electroencephalography for encephalopathy daily monitoring. The results showed a high mean accuracy of 91.79% and 93.89% in two types of classification for encephalopathy. In conclusion, good performance of our Brain-Health system indicated the feasibility and effectiveness for encephalopathy daily monitoring and patients’ health self-management.

EEG-Informed fMRI: A Review of Data Analysis Methods.

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

Event-related potentials during sustained attention and memory tasks: Utility as biomarkers for mild cognitive impairment

IntroductionThe objective of the study is to validate attention and memory tasks that elicit event-related potentials (ERPs) for utility as sensitive biomarkers for early dementia. MethodsA 3-choice vigilance task designed to evaluate sustained attention and standard image recognition memory task designed to evaluate attention, encoding, and image recognition memory were administered with concurrent electroencephalography acquisition to elicit ERPs in mild cognitive impairment (MCI) and healthy cohorts. ERPs were averaged, and mean or maximum amplitude of components was measured and compared between and within cohorts. ResultsThere was significant suppression of the amplitude of the late positive potential in the MCI cohort compared with the healthy controls during 3-choice vigilance task, predominantly over occipital and right temporal-parietal region, and standard image recognition memory task over all regions. During standard image recognition memory task, diminished performance showed strong correlation with electroencephalography measurements. The old/new effects observed in the healthy controls cohort correlated with performance and were lost in MCI. DiscussionERPs obtained during cognitive tasks may provide a powerful tool for assessing MCI and have strong potential as sensitive and robust biomarkers for tracking disease progression and evaluating response to investigative therapeutics.

Brain computer interface with the P300 speller: Usability for disabled people with amyotrophic lateral sclerosis

ObjectivesAmyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease, restricts patients’ communication capacity a few years after onset. A proof-of-concept of brain–computer interface (BCI) has shown promise in ALS and “locked-in” patients, mostly in pre-clinical studies or with only a few patients, but performance was estimated not high enough to support adoption by people with physical limitation of speech. Here, we evaluated a visual BCI device in a clinical study to determine whether disabled people with multiple deficiencies related to ALS would be able to use BCI to communicate in a daily environment. MethodsAfter clinical evaluation of physical, cognitive and language capacities, 20 patients with ALS were included. The P300 speller BCI system consisted of electroencephalography acquisition connected to real-time processing software and separate keyboard-display control software. It was equipped with original features such as optimal stopping of flashes and word prediction. The study consisted of two 3-block sessions (copy spelling, free spelling and free use) with the system in several modes of operation to evaluate its usability in terms of effectiveness, efficiency and satisfaction. ResultsThe system was effective in that all participants successfully achieved all spelling tasks and was efficient in that 65% of participants selected more than 95% of the correct symbols. The mean number of correct symbols selected per minute ranged from 3.6 (without word prediction) to 5.04 (with word prediction). Participants expressed satisfaction: the mean score was 8.7 on a 10-point visual analog scale assessing comfort, ease of use and utility. Patients quickly learned how to operate the system, which did not require much learning effort. ConclusionWith its word prediction and optimal stopping of flashes, which improves information transfer rate, the BCI system may be competitive with alternative communication systems such as eye-trackers. Remaining requirements to improve the device for suitable ergonomic use are in progress.

Enhanced visual adaptation in cochlear implant users revealed by concurrent EEG-fNIRS

Previous studies have observed lower visual cortex activation for visual processing in cochlear implant (CI) users compared to normal hearing controls, while others reported enhanced visual speechreading abilities in CI users. The present work investigated whether lower visual cortical activation for visual processing can be explained by a more efficient visual sensory encoding in CI users. Specifically, we investigated whether CI users show enhanced stimulus-specific adaptation for visual stimuli compared to controls. Auditory sensory adaptation was also investigated to explore the sensory specificity of the predicted effect. Twenty post-lingually deafened adult CI users and twenty age-matched controls were presented with repeated visual and auditory stimuli during simultaneous acquisition of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). By integrating EEG and fNIRS signals we found significantly enhanced visual adaptation and lower visual cortex activation in CI users compared to controls. That is, responses to repeated visual stimuli decreased more prominently in CI users than in controls. The results suggest that CI users process visual stimuli more efficiently than controls.

Towards motion insensitive EEG-fMRI: Correcting motion-induced voltages and gradient artefact instability in EEG using an fMRI prospective motion correction (PMC) system

The simultaneous acquisition of electroencephalography and functional magnetic resonance imaging (EEG-fMRI) is a multimodal technique extensively applied for mapping the human brain. However, the quality of EEG data obtained within the MRI environment is strongly affected by subject motion due to the induction of voltages in addition to artefacts caused by the scanning gradients and the heartbeat. This has limited its application in populations such as paediatric patients or to study epileptic seizure onset. Recent work has used a Moiré-phase grating and a MR-compatible camera to prospectively update image acquisition and improve fMRI quality (prospective motion correction: PMC). In this study, we use this technology to retrospectively reduce the spurious voltages induced by motion in the EEG data acquired inside the MRI scanner, with and without fMRI acquisitions. This was achieved by modelling induced voltages from the tracking system motion parameters; position and angles, their first derivative (velocities) and the velocity squared. This model was used to remove the voltages related to the detected motion via a linear regression. Since EEG quality during fMRI relies on a temporally stable gradient artefact (GA) template (calculated from averaging EEG epochs matched to scan volume or slice acquisition), this was evaluated in sessions both with and without motion contamination, and with and without PMC. We demonstrate that our approach is capable of significantly reducing motion-related artefact with a magnitude of up to 10mm of translation, 6° of rotation and velocities of 50mm/s, while preserving physiological information. We also demonstrate that the EEG-GA variance is not increased by the gradient direction changes associated with PMC. Provided a scan slice-based GA template is used (rather than a scan volume GA template) we demonstrate that EEG variance during motion can be supressed towards levels found when subjects are still. In summary, we show that PMC can be used to dramatically improve EEG quality during large amplitude movements, while benefiting from previously reported improvements in fMRI quality, and does not affect EEG data quality in the absence of large amplitude movements.

Comparative Study of Different Pulse Artefact Correction Techniques during Concurrent EEG-FMRI Using FMRIB

Simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) aims to combine the strength of both methods by investigating brain activity with the high temporal resolution of the former and the good spatial precision of the later. However, simultaneous EEG-fMRI measurements are technically challenging because both methods interfere with the other modality. The main confounding factors are the pulse artefact (PA) caused by pulsatile motion linked to the cardiac cycle and the gradient artefact (GA) produced by the temporally varying magnetic fields required for MR imaging. Both of these artefacts are orders of magnitude larger than the neuronal activity of interest, but their inherent periodicity facilitates reasonable artefact correction by post-processing techniques such as average artefact subtraction (AAS), Optimal Basis Set (OBS). While the different post processing methods for PA artefact are being used to-date, but their comparison were not studied yet. In this work, different methods - OBS, simple mean (AAS) and Gaussian-weighted mean (GWM) - implemented in open source FMRIB tool-box, were used for PA correction and to compare their performance in artefact correction while retaining the neuronal information. It has been found that, of the three different PA removal methods, OBS is better in preserving bio-signal while removing PA successfully.

Beyond the N400: Complementary access to early neural correlates of novel metaphor comprehension using combined electrophysiological and haemodynamic measurements

The simultaneous application of different neuroimaging methods combining high temporal and spatial resolution can uniquely contribute to current issues and open questions in the field of pragmatic language perception. In the present study, comprehension of novel metaphors was investigated using near-infrared spectroscopy (NIRS) combined with the simultaneous acquisition of electroencephalography (EEG)/event-related potentials (ERPs). For the first time, we investigated the effects of figurative language on early electrophysiological markers (P200, N400) and their functional relationship to cortical haemodynamic responses within the language network (Broca's area, Wernicke's area). To this end, 20 healthy subjects judged 120 sentences with respect to their meaningfulness, whereby phrases were either literal, metaphoric, or meaningless. Our results indicated a metaphor-specific P200 reduction and a linear increase of N400 amplitudes from literal over metaphoric to meaningless sentences. Moreover, there were metaphor related effects on haemodynamic responses accessed with NIRS, especially within the left lateral frontal cortex (Broca's area). Significant correlations between electrophysiological and haemodynamic responses indicated that P200 reductions during metaphor comprehension were associated with an increased recruitment of neural activity within left Wernicke's area, indicating a link between variations in neural activity and haemodynamic changes within Wernicke's area. This link may reflect processes related to interindividual differences regarding the ability to classify novel metaphors. The present study underlines the usefulness of simultaneous NIRS measurements in language paradigms – especially for investigating the functional significance of neurophysiological markers that have so far been rarely examined – as these measurements are easily and efficiently realizable and allow for a complementary examination of neural activity and associated metabolic changes in cortical areas.

A single current conveyor-based low voltage low power bootstrap circuit for ElectroCardioGraphy and ElectroEncephaloGraphy acquisition systems

In this letter we propose a novel low voltage and low power single-CCII bootstrap circuit specifically designed to be implemented as input stage in ElectroCardioGraphy (ECG) or ElectroEncephaloGraphy (EEG) acquisition systems. The proposed circuit implements only a second generation current conveyor that has been designed to obtain, at X and Z nodes, reduced parasitic impedances, so improving CCII performance. Moreover, simulation results are also presented for a two electrodes ECG system. The circuit, designed in a standard 0.35 μm CMOS technology, shows low voltage (1.5 V) low power (28 μW) characteristics, so it is particularly suitable for portable applications.

Application of Partial Directed Coherence to the Analysis of Resting-State EEG-fMRI Data

The simultaneous acquisition of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data potentially allows measurement of brain signals with both high spatial and temporal resolution. Partial directed coherence (PDC) is a Granger causality measure in the frequency domain, which is often used to infer the intensity of information flow over the brain from EEG data. In the current study, we propose a new approach to investigate functional connectivity in resting-state (RS) EEG-fMRI data by combining time-varying PDC with the analysis of blood oxygenation level-dependent (BOLD) signal fluctuations. Basically, we aim to identify brain circuits that are more active when the information flow is increased between distinct remote neuronal modules. The usefulness of the proposed method is illustrated by application to simultaneously recorded EEG-fMRI data from healthy subjects at rest. Using this approach, we decomposed the nodes of RS networks in fMRI data according to the frequency band and directed flow of information provided from EEG. This approach therefore has the potential to inform our understanding of the regional characteristics of oscillatory processes in the human brain.