EEG Acquisition Research Articles

Studying critical frequency bands and channels for EEG-based automobile sound recognition with machine learning

As for subjective methods (such as scale scores) for evaluation of automobile sound quality, electroencephalogram (EEG) can objectively and real-time reflect human mental states. Thus, EEG was introduced here to study the method of automobile sound recognition in view of frequency bands and channels. An EEG acquisition instrument was utilized to simultaneously acquire the EEGs of subjects stimulated by engine, exhaust and in-car sounds, and the power spectra of EEG were extracted by means of Welch Algorithm to construct a feature matrix; Then, the accuracies of automobile sound recognition based on five machine learning models were compared to determine the optimal model and the best frequency band range; What’ more, considering the differences between subjects, the m-ReliefF algorithm was applied here to investigate the universality channel with the optimal weight. Our results indicated that the differences between the three kinds of automobile sounds can be effectively identified by means of the KNN model, whose mean of accuracy peaks up to 87.93%; In addition, the recognition accuracies of beta and gamma bands were far higher than that of alpha and theta bands. Also, the accuracies of top universality 9 and 12 channels were up to 89.9% and 92.8% respectively, which were even better than that of the original 63 channels. This study revealed that recognition accuracy and computational efficiency can be significantly improved through the precise localization of the frequency bands and channels related with the automobile sounds, and demonstrated that EEG-based evaluation of the automobile sound quality shall be feasible.

Does Real-Time Feedback Affect Sensorimotor EEG Patterns in Routine Motor Imagery Practice?

Background. Motor imagery engages much of the same neural circuits as an overt movement. Therefore, the mental rehearsal of movements is often used to supplement physical training and might aid motor neurorehabilitation after stroke. One attempt to capture the brain’s involvement in imagery involves the use, as a marker, of the depression or event-related desynchronization (ERD) of thalamocortical sensorimotor rhythms found in a human electroencephalogram (EEG). Using fast real-time processing, it is possible to make the subject aware of their own brain reactions or—even better—to turn them into actions through a technology called the brain–computer interface (BCI). However, it remains unclear whether BCI-enabled imagery facilitates a stronger or qualitatively different brain response compared to the open-loop training. Methods. Seven healthy volunteers who were experienced in both closed and open-loop motor imagery took part in six experimental sessions over a period of 4.5 months, in which they performed kinesthetic imagery of a previously known set of finger and arm movements with simultaneous 30-channel EEG acquisition. The first and the last session mostly consisted of feedback trials in which the subjects were presented with the classification results of the EEG patterns in real time; during the other sessions, no feedback was provided. Spatiotemporal and amplitude features of the ERD patterns concomitant with imagery were compared across experimental days and between feedback conditions using linear mixed-effects modeling. Results. The main spatial sources of ERD appeared to be highly stable across the six experimental days, remaining nearly identical in five of seven subjects (Pearson’s ρ > 0.94). Only in one subject did the spatial pattern of activation statistically significantly differ (p = 0.009) between the feedback and no-feedback conditions. Real-time visual feedback delivered through the BCI did not significantly increase the ERD strength. Conclusion. The results imply that the potential benefits of MI could be yielded by well-habituated subjects with a simplified open-loop setup, e.g., through at-home self-practice.

Development of single channel EEG Acquisition system for BCI applications

Brain-Computer Interface (BCI) plays a major role in current technologies such as rehabilitation, control of devices, and various medical applications. BCI or brain-machine interface provides direct communication between a brain signal and an external device. In this paperwork, a detailed survey was carried out with the design of single-channel EEG system for various applications. Also, this paper mainly focused on the development of single-channel electroencephalography (EEG) signal acquisition system which includes a preamplifier, bandpass filter, post-amplifier and level shifter circuits. The design of the preamplifier and post-amplifier circuit was carried out by integrated circuits (IC) such as instrumentation amplifier IN128P and bandpass filter with the help of low power operational amplifier LM324. The developed single-channel acquisition board was tested by acquiring an electrooculogram (EOG) signal with closed and opened eye conditions. The acquired signal is displayed and stored in the computer with the help of the HBM-DAQ unit.

FMRI-Informed EEG for brain mapping of imagined lower limb movement: Feasibility of a brain computer interface

BackgroundEEG and fMRI have contributed greatly to our understanding of brain activity and its link to behaviors by helping to identify both when and where the activity occurs. This is particularly important in the development of brain-computer interfaces (BCIs), where feed forward systems gather data from imagined brain activity and then send that information to an effector. The purpose of this study was to develop and evaluate a computational approach that enables an accurate mapping of spatial brain activity (fMRI) in relation to the temporal receptors (EEG electrodes) associated with imagined lower limb movement. New methodEEG and fMRI data from 16 healthy, male participants while imagining lower limb movement were used for this purpose. A combined analysis of fMRI data and EEG electrode locations was developed to identify EEG electrodes with a high likelihood of capturing imagined lower limb movement originating from various clusters of brain activity. This novel feature selection tool was used to develop an artificial neural network model to classify right and left lower limb movement. ResultsResults showed that left versus right lower limb imagined movement could be classified with 66.5% accuracy using this approach.Comparison with existing methods: Adopting a purely data-driven approach for feature selection to use in the right/left classification task resulted in the same accuracy (66.6%) but with reduced interpretability. ConclusionsThe developed fMRI-informed EEG approach could pave the way towards improved brain computer interfaces for lower limb movement while also being applicable to other systems where fMRI could be helpful to inform EEG acquisition and processing.

A large-scale brain network mechanism for increased seizure propensity in Alzheimer's disease.

People with Alzheimer's disease (AD) are 6-10 times more likely to develop seizures than the healthy aging population. Leading hypotheses largely consider hyperexcitability of local cortical tissue as primarily responsible for increased seizure prevalence in AD. However, in the general population of people with epilepsy, large-scale brain network organization additionally plays a role in determining seizure likelihood and phenotype. Here, we propose that alterations to large-scale brain network organization seen in AD may contribute to increased seizure likelihood. To test this hypothesis, we combine computational modelling with electrophysiological data using an approach that has proved informative in clinical epilepsy cohorts without AD. EEG was recorded from 21 people with probable AD and 26 healthy controls. At the time of EEG acquisition, all participants were free from seizures. Whole brain functional connectivity derived from source-reconstructed EEG recordings was used to build subject-specific brain network models of seizure transitions. As cortical tissue excitability was increased in the simulations, AD simulations were more likely to transition into seizures than simulations from healthy controls, suggesting an increased group-level probability of developing seizures at a future time for AD participants. We subsequently used the model to assess seizure propensity of different regions across the cortex. We found the most important regions for seizure generation were those typically burdened by amyloid-beta at the early stages of AD, as previously reported by in-vivo and post-mortem staging of amyloid plaques. Analysis of these spatial distributions also give potential insight into mechanisms of increased susceptibility to generalized (as opposed to focal) seizures in AD vs controls. This research suggests avenues for future studies testing patients with seizures, e.g. co-morbid AD/epilepsy patients, and comparisons with PET and MRI scans to relate regional seizure propensity with AD pathologies.

Automatic epilepsy detection based on generalized convolutional prototype learning

In clinical applications, different EEG acquisition equipment and experience of neurologists make the quality and pattern of EEG signals different. This hinders the promotion of some automatic epilepsy detection methods in clinical practice. In order to solve this problem, we proposed an epilepsy detection method based on generalized convolutional prototype learning (GCPL). GCPL maps EEG samples to the regions close to the prototype in the feature space, which is more suitable for the classification of clinical EEG signals with diverse quality and pattern. Moreover, it can overcome the impact of the change of discriminative features caused by different datasets on the classification performance, which shows more robustness. In the experiments, the proposed method could reach a sensitivity of 98.75%, a specificity 100% of and an accuracy 99.38%, respectively on the clinical dataset. GCPL can complete a test within 2 s, which meets the actual clinical needs.

Design of Portable EEG and Blood Oxygen Synchronous Acquisition System

In order to obtain comprehensive brain activity information conveniently in real time, this study designs a portable EEG and blood oxygen synchronous acquisition system for real-time monitoring of brain functional activities. The EEG electrodes filter and amplify the detected EEG signals, and send them to the microprocessor via Bluetooth to analyze the EEG data; the photoelectric probe converts the optical signals into electrical signals, which are amplified and separated, filtered, and AD converted, calculates the brain's oxygenation and blood-red protein (ΔHbO2) and blood-red protein (ΔHb) by amplification of Lambert-Beer law. Finally, experiments with commercial instruments NuAmps and Oximeter show that the system can calculate ΔHbO2 and ΔHb while collecting EEG signals.

A Portable Waterproof EEG Acquisition Device for Dolphins.

The acquisition and analysis of EEG signals of dolphins, a highly intelligent creature, has always been a focus of the research of bioelectric signals. Prevailing cable-connected devices cannot be adapted to data acquisition very well when dolphins are in motion. Therefore, this study designs a novel, light-weighted, and portable EEG acquisition device aimed at relatively unrestricted EEG acquisition. An embedded main control board and an acquisition board were designed, and all modules are encapsulated in a 162 × 94 × 60 mm3 waterproof device box, which can be tied to the dolphin’s body by a silicon belt. The acquisition device uses customized suction cups with embedded electrodes and adopts a Bluetooth module for wireless communication with the ground station. The sampled signals are written to the memory card on board when the Bluetooth communication is blocked. A limited experiment was designed to verify the effectiveness of the device functionality onshore and underwater. However, more rigorous long-term tests on dolphins in various states with our device are expected in future to further prove its capability and study the movement-related artifacts.

A review of EEG acquisition, processing and application

As the study about the human body goes forward, some breakthrough in human biological signals is made, especially in EEG signal. At the same time, people are getting to know more about the benefits of EEG. Therefore, EEG research is enjoying a stronger demand around the world. Nowadays, EEG research is carried in several fields and different stages, such as the acquisition of EEG, the processing of EEG, energy recovery of EEG, etc. Scholars are mainly searching for behavioral and electrophysiology, neural network, multi-lead electroencephalogram, non-invasive brain imaging technology, etc. And the current research frontier and the main trend is the comprehensive study of brain function and disease at the molecular, cellular, and global levels and the understanding of the brain structure principle from the brain development process. In this review, three aspects are chosen to study: the processing of EEG, the acquisition of EEG and EEG application. Four EEG acquisition methods, five ways of EEG processing system and three patterns of application are introduced. EEG acquisition system comprises scalp electrodes, a signal isolation amplifier, an analog-to-digital converter, and a wireless transmission module. Low power consumption, small size and portability are the main directions. The research status and difficulties of suitable electrodes, high integration acquisition chip, and analysis algorithm are analyzed and predicted. Due to the rapid development of EEG signal processing, the application of EEG has developed from medical rehabilitation to driving safety, education assistance, and entertainment consumption.

Thalamocortical excitability modulation guides human perception under uncertainty

Knowledge about the relevance of environmental features can guide stimulus processing. However, it remains unclear how processing is adjusted when feature relevance is uncertain. We hypothesized that (a) heightened uncertainty would shift cortical networks from a rhythmic, selective processing-oriented state toward an asynchronous (“excited”) state that boosts sensitivity to all stimulus features, and that (b) the thalamus provides a subcortical nexus for such uncertainty-related shifts. Here, we had young adults attend to varying numbers of task-relevant features during EEG and fMRI acquisition to test these hypotheses. Behavioral modeling and electrophysiological signatures revealed that greater uncertainty lowered the rate of evidence accumulation for individual stimulus features, shifted the cortex from a rhythmic to an asynchronous/excited regime, and heightened neuromodulatory arousal. Crucially, this unified constellation of within-person effects was dominantly reflected in the uncertainty-driven upregulation of thalamic activity. We argue that neuromodulatory processes involving the thalamus play a central role in how the brain modulates neural excitability in the face of momentary uncertainty.

Mitigating Psychological Trauma on Adult Burn Patients Based on Virtual Reality Technology of Smart Medical Treatment.

Virtual reality (VR) is one of the hot spots in the computer network world in recent years, which has attracted more and more people's attention. This study mainly explores the effect of mitigating the psychological trauma of adult burn patients based on the VR technology of smart medical treatment. First, the EEG data are sent to the data processing module through a wireless protocol; then, the data processing module denoises the EEG data and performs feature extraction and feedback parameter calculation; after that, these parameters will be sent to the VR interaction engine; based on this, these parameters change the VR scene to capture and reflect the physiological activities of the patient's brain in real time; finally, the patient uses the VR scene content presented by the real-time feedback of the captured EEG signal as a guide to making self-adjustment in time, and the electrical signal of captured brain at this time is again transmitted to the next work cycle and continues to feed back and present new VR interactive scenes to guide and intervene in the patient's self-regulation behavior. The VR feedback training module is responsible for receiving the characteristic data calculated from the EEG acquisition and processing module and converts it into parameter variables that control the VR intervention system. The system user adjusts the state according to the feedback information displayed in the VR scene and generates new EEG signals to promote the realization of self-adjustment. The biofeedback training based on EEG feeds back the intuitive EEG state to the patient, prompting them to learn how to realize self-regulation and achieve the purpose of adjusting the level of mental health. The degree of itching and pain in the VR treatment group was alleviated, and compared with the normal training treatment results, it was statistically significant (P > 0.05). This study has a positive effect on the psychological intervention of patients with facial injuries.

Impairment of novelty-related theta oscillations and P3a in never medicated first-episode psychosis patients

We explored the neurophysiological activity underlying auditory novelty detection in antipsychotic-naive patients with a first episode of psychosis (FEP). Fifteen patients with a non-affective FEP and 13 healthy controls underwent an active involuntary attention task along with an EEG acquisition. Time-frequency representations of power, phase locking, and fronto-parietal connectivity were calculated. The P3a event-related potential was extracted as well. Compared to controls, the FEP group showed reduced theta phase-locking and fronto-parietal connectivity evoked by deviant stimuli. Also, the P3a amplitude was significantly reduced. Moreover, reduced theta connectivity was associated with more severe negative symptoms within the FEP group. Reduced activity (phase-locking and connectivity) of novelty-related theta oscillations, along with P3a reduction, may represent a failure to synchronize large-scale neural populations closely related to fronto-parietal attentional networks, and might be explored as a potential biomarker of disease severity in patients with emerging psychosis, given its association with negative symptoms.

Discrimination Improvement Through Undesirable Feedback in Coupling Object Manipulation Tasks.

Subjective effort can significantly affect the ability of humans to act optimally in dynamic manipulation tasks. In a previous study, we designed a complex object coupling manipulation task that required tight performance and induced high cognitive workload. We hypothesize that strong-effort-related physiological reactivity during the dynamic manipulation task improves the user performance in an undesired task feedback situation. To test this hypothesis, using the motor intentions' discrimination from electroencephalogram (EEG) measurements, we evaluate the effort expended by 20 participants in a controlling task with constraints involving complex coupling objects. Specifically, the finer motor decisions are obtained from the controlling information in EEG by using two fingers from the same hand rather than two hands. The motor intention is decoded from a task-dependent EEG through a regularized discriminant analysis, and the area under the curve is [Formula: see text]. Furthermore, we compare the undesired and desired task feedback conditions along with the individual's effort dynamic adjustment, and investigate whether the undesired task feedback improved the discrimination of the motor activities. A stronger effort to attain the desired feedback state corresponds to improved motor activity discrimination from the EEG in the undesired task feedback scenario. The differences in the brain activities under the undesired and desired task feedback conditions are analyzed using brain-network-based topographical scalp maps. Our experiment provides preliminary evidence that inducing strong effort can improve discrimination performance during highly demanding tasks. This finding can advance our understanding of human attention, potentially improve the accuracy of intention recognition, and may inspire better EEG acquisition contexts.

P‐2.6: Research on Desensitization Medical Therapy Based on Virtual Reality

As the pace of society gradually speeding up, more and more people came the problem of psychological fear. The traditional drug therapy trauma on the human body is very serious. The communication with the psychologist does not fully understand the patient's own situation, and it is easy to cause trauma to the psychologist. Virtual reality exposure therapy has important implications for the management of posttraumatic stress disorder and severe phobias that are difficult to treat with conventional psychotherapy and drugs. Based on the above situation, this paper proposes a psychological phobia detection scheme in a virtual environment based on EEG signals. The program began with a scene of virtual reality stimulation, EEG acquisition, combined with neural network algorithm, patient diagnosis and treatment. Experiments show that virtual reality‐based desensitization therapy has a better diagnosis and treatment effect on patients' fear.

Development of multithread acquisition system for high quality EEG signal measurement

This work was concerned on development of the EEG acquisition and EEG signal processing by adding active electrodes and implementing multithread techniques. By using active electrodes, mounting them on the scalp surface would be easier to capture low signals of less than 1µV. The active electrodes were used to reduce noise when transfer signals from the electrode to the acquisition systems which equipped 20 gain. The verification was performed by comparing the active and passive electrodes using NETECH MiniSIM EEG Simulator 330. The advantage of this research was to reduce time delay for EEG signal computation on 32 channels. The acquisition system was based on Raspberry Pi and ADS1299 with multithread signal treatment. Signal filtering was performed into different threads and put all the EEG features into the database. A PC was used to process signal calculation such as processing FFT, signal feature extractions, and signal analysis. These calculations were divided into several functionally independent computations. The signals of each channel were calculated into different threads. The results of this work showed the effectiveness of the multithreaded method for processing large amounts of data (32 channels of 24 bits EEG signal) with low noise levels on the active electrodes.

Similarities and Differences between Performers and Observers in Processing Auditory Action Consequences: Evidence from Simultaneous EEG Acquisition.

In our social environment, we easily distinguish stimuli caused by our own actions (e.g., water splashing when I fill my glass) from stimuli that have an external source (e.g., water splashing in a fountain). Accumulating evidence suggests that processing the auditory consequences of self-performed actions elicits N1 and P2 ERPs of reduced amplitude compared to physically identical but externally generated sounds, with such reductions being ascribed to neural predictive mechanisms. It is unexplored, however, whether the sensory processing of action outcomes is similarly modulated by action observation (e.g., water splashing when I observe you filling my glass). We tested 40 healthy participants by applying a methodological approach for the simultaneous EEG recording of two persons: An observer observed button presses executed by a performer in real time. For the performers, we replicated previous findings of a reduced N1 amplitude for self- versus externally generated sounds. This pattern differed significantly from the one in observers, whose N1 for sounds generated by observed button presses was not attenuated. In turn, the P2 amplitude was reduced for processing action- versus externally generated sounds for both performers and observers. These findings show that both action performance and observation affect the processing of action-generated sounds. There are, however, important differences between the two in the timing of the effects, probably related to differences in the predictability of the actions and thus also the associated stimuli. We discuss how these differences might contribute to recognizing the stimulus as caused by self versus others.

Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG.

Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.

Hyper-acute EEG alterations predict functional and morphological outcomes in thrombolysis-treated ischemic stroke: a wireless EEG study

Owing to the large inter-subject variability, early post-stroke prognosis is challenging, and objective biomarkers that can provide further prognostic information are still needed. The relation between quantitative EEG parameters in pre-thrombolysis hyper-acute phase and outcomes has still to be investigated. Hence, possible correlations between early EEG biomarkers, measured on bedside wireless EEG, and short-term/long-term functional and morphological outcomes were investigated in thrombolysis-treated strokes. EEG with a wireless device was performed in 20 patients with hyper-acute (< 4.5 h from onset) anterior ischemic stroke before reperfusion treatment. The correlations between outcome parameters (i.e., 7-day/12-month National Institutes of Health Stroke Scale NIHSS, 12-month modified Rankin Scale mRS, final infarct volume) and the pre-treatment EEG parameters were studied. Relative delta power and alpha power, delta/alpha (DAR), and (delta+theta)/(alpha+beta) (DTABR) ratios significantly correlated with NIHSS 7-day (rho = 0.80, − 0.81, 0.76, 0.75, respectively) and NIHSS 12-month (0.73, − 0.78, 0.74, 0.73, respectively), as well as with final infarct volume (0.75, − 0.70, 0.78, 0.62, respectively). A good outcome in terms of mRS ≤ 2 at 12 months was associated with DAR parameter (p = 0.008). The neurophysiological biomarkers obtained by non-invasive and portable technique as wireless EEG in the early pre-treatment phase may contribute as objective parameters to the short/long-term outcome prediction pivotal to better establish the treatment strategies.Graphical abstractBlock diagram of study protocol and main findings. Assessment at admission including wireless EEG acquisition in emergency setting (< 4.5 from stroke onset), extracted EEG features before reperfusion thrombolytic treatment. The main findings in our study sample are summarized in two different exemplificative stroke patients with different pre-thrombolysis alterations of EEG parameters resulting in different final infarct volume extensions and short/long-term clinical outcomes (NIHSS, mRS).

The MEMOWAVE project: A phase I/II clinical trial targeting treatment of mild cognitive impairment through sound stimulation during slow‐wave sleep

AbstractBackgroundMild Cognitive Impairment (MCI) is a highly prevalent syndrome especially among the elderly, but its treatment remains challenging. The MEMOWAVE project is a phase I/II randomized, double blind and cross‐over clinical trial. The first objective is to validate the safety and tolerance of a device consisting of a small microphone, also equipped with electroencephalographic electrodes, inserted in the external ear canals. The device produces pink noises during slow‐wave sleep based on real‐time EEG acquisition. The second objective is to investigate the effectiveness of this device in amplifying cerebral slow waves during sleep through sound emission. The third objective is to study the effect of this stimulation on memory consolidation, attention, executive functions, insight, creativity and mood in patients with MCI. Indeed, there is converging evidence that deep slow‐wave sleep plays a major role in these aspects. This process has already shown improvements in memory performance in healthy subjects and during normal aging.MethodTwenty‐four patients between 40 and 85 years of age will spend three nights in a Center for sleep disorders. Their cognitive functions, insight, creativity abilities and mood will be tested before and after a night of slow wave stimulation or placebo (device in place without stimulation), in a randomized and crossover fashion.ResultWe expect that this device will represent a simple therapeutic approach, easily tolerable by the patient, and able, if successful, of being offered quickly to a larger population of patients with MCI.ConclusionWe are proposing a new innovative methodology for the treatment of MCI which we are going to test for safety and efficacy.

Night-to-night variability of sleep electroencephalography-based brain age measurements

ObjectiveBrain Age Index (BAI), calculated from sleep electroencephalography (EEG), has been proposed as a biomarker of brain health. This study quantifies night-to-night variability of BAI and establishes probability thresholds for inferring underlying brain pathology based on a patient’s BAI. Methods86 patients with multiple nights of consecutive EEG recordings were selected from Epilepsy Monitoring Unit patients whose EEGs reported as within normal limits. While EEGs with epileptiform activity were excluded, the majority of patients included in the study had a diagnosis of chronic epilepsy. BAI was calculated for each 12-hour segment of patient data using a previously established algorithm, and the night-to-night variability in BAI was measured. ResultsThe within-patient night-to-night standard deviation in BAI was 7.5 years. Estimates of BAI derived by averaging over 2, 3, and 4 nights had standard deviations of 4.7, 3.7, and 3.0 years, respectively. ConclusionsAveraging BAI over n nights reduces night-to-night variability of BAI by a factor of n, rendering BAI a more suitable biomarker of brain health at the individual level. A brain age risk lookup table of results provides thresholds above which a patient has a high probability of excess BAI. SignificanceWith increasing ease of EEG acquisition, including wearable technology, BAI has the potential to track brain health and detect deviations from normal physiologic function. The measure of night-to-night variability and how this is reduced by averaging across multiple nights provides a basis for using BAI in patients’ homes to identify patients who should undergo further investigation or monitoring.