Record EEG Data Research Articles

Resonance and beat perception of ballroom dancers: An EEG study.

The ability to synchronize the perceptual and motor systems is important for full motor coordination and the core determinant of motor skill performance. Dance-related training has been found to effectively improve sensorimotor synchronization, however, the underlying characteristics behind these improvements still warrant further exploration. This study was conducted to investigate the behavioral and neuroactivity characteristics of ballroom dancers relative to those of non-dancers. Thirty-two dancers (19.8 ± 1.8 years old) and 31 non-dancers (22.6 ± 3.1 years old) were recruited to perform a finger-tapping task in synchrony with audiovisual beat stimuli at two intervals: 400 and 800 ms, while simultaneously recording EEG data. Behavioral and neural activity data were recorded during the task. The dancers employed a predictive strategy when synchronizing with the beat. EEG recordings revealed stronger brain resonance with external rhythmic stimuli, indicating heightened neural resonance compared to non-dancers (p < 0.05). The task was more challenging with an 800-ms beat interval, as observed through both behavioral metrics and corresponding neural signatures in the EEG data, leading to poorer synchronization performance and necessitating a greater allocation of attentional resources (ps < 0.05). When performing the finger-tapping task involving audiovisual beats, the beat interval was the primary factor influencing movement synchronization, neural activity and attentional resource allocation. Although no significant behavioral differences were observed between dancers and non-dancers, dancers have enhanced neural resonance in response to rhythmic stimuli. Further research using more ecologically valid tasks and stimuli may better capture the full extent of dancers' synchronization abilities.

HEROIC: a platform for remote collection of electroencephalographic data using consumer-grade brain wearables

The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC’s implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants’ homes. Our results demonstrate HEROIC’s capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease.

Developmental changes in individual alpha frequency: Recording EEG data during public engagement events.

Statistical power in cognitive neuroimaging experiments is often very low. Low sample size can reduce the likelihood of detecting real effects (false negatives) and increase the risk of detecting non-existing effects by chance (false positives). Here, we document our experience of leveraging a relatively unexplored method of collecting a large sample size for simple electroencephalography (EEG) studies: by recording EEG in the community during public engagement and outreach events. We collected data from 346 participants (189 females, age range 6-76 years) over 6 days, totalling 29 hours, at local science festivals. Alpha activity (6-15 Hz) was filtered from 30 seconds of signal, recorded from a single electrode placed between the occipital midline (Oz) and inion (Iz) while the participants rested with their eyes closed. A total of 289 good-quality datasets were obtained. Using this community-based approach, we were able to replicate controlled, lab-based findings: individual alpha frequency (IAF) increased during childhood, reaching a peak frequency of 10.28 Hz at 28.1 years old, and slowed again in middle and older age. Total alpha power decreased linearly, but the aperiodic-adjusted alpha power did not change over the lifespan. Aperiodic slopes and intercepts were highest in the youngest participants. There were no associations between these EEG indexes and self-reported fatigue, measured by the Multidimensional Fatigue Inventory. Finally, we present a set of important considerations for researchers who wish to collect EEG data within public engagement and outreach environments.

Noise characteristics in spaceflight multichannel EEG.

The cognitive performance of the crew has a major impact on mission safety and success in space flight. Monitoring of cognitive performance during long-duration space flight therefore is of paramount importance and can be performed using compact state-of-the-art mobile EEG. However, signal quality of EEG may be compromised due to the vicinity to various electronic devices and constant movements. We compare noise characteristics between in-flight extraterrestrial microgravity and ground-level terrestrial electroencephalography (EEG) recordings. EEG data recordings from either aboard International Space Station (ISS) or on earth's surface, utilizing three EEG amplifiers and two electrode types, were compared. In-flight recordings showed noise level of an order of magnitude lower when compared to pre- and post-flight ground-level recordings with the same EEG system. Noise levels between ground-level recordings with actively shielded cables, and in-flight recordings without shielded cables, were similar. Furthermore, noise level characteristics of shielded ground-level EEG recordings, using wet and dry electrodes, and in-flight EEG recordings were similar. Actively shielded mobile dry EEG systems will support neuroscientific research and neurocognitive monitoring during spaceflight, especially during long-duration space missions.

Evoked Acute Stress Alters Frontal Midline Neural Oscillations Affecting Behavioral Inhibition in College Students.

PurposeThe current research of the effect of acute stress on individual behavioral inhibition remains divergent. The present study aims to explore the effects of acute stress on behavioral inhibition in college students and to understand the neural oscillatory characteristics of their behavioral inhibition process.Patients and MethodsWe invited 27 college students (12 males and 15 females) to participate in the study. The experiment was conducted using the Trier Social Stress paradigm to evoke an acute stress state and an out-of-speech reading to set a neutral state. Participants completed a two-choice Oddball task in the acute stress state and the neutral state, respectively. We used a 64-channel EEG cap to record EEG data from university students during the experimental task. In combination with the ERO technique, we compared the reaction time, the number of errors, and the power of the alpha (8–13 Hz) and theta (4–8 Hz) frequency bands at the midline of the frontal lobe for subjects in both states. The correlation between the area under the stress area line and the alpha as well as theta frequency bands was also analyzed.ResultsWe found that in the two-choice Oddball task, the response inhibition time was shorter, the number of response errors decreased, and the alpha-band power values decreased in the acute stress state compared to the neutral state. For the standard stimulus, the theta-band power increase in the acute stress state.ConclusionOur results suggest that evoked acute stress promotes behavioral inhibition in college students by affecting their frontal midline neural oscillations.

EEG parameters as endpoints in epilepsy clinical trials - An expert panel opinion paper

IntroductionThe lack of ideal measurement of treatment efficacy is a well acknowledged problem in the epilepsy community, both in clinical care and clinical trials. Whilst still the current gold-standard, self-reported seizure frequency significantly underestimates the true number of seizures and does not account for any other at least equally important outcome parameters, such as neurodevelopment and cognition. With the rise of disease modifying treatments, the need for more reliable endpoints in practice and clinical trials becomes more pressing. In this paper we assembled an expert panel to discuss the nature of these needs, current limitations, and obstacles based on a survey amongst these experts who were queried about the most important issues regarding the use of electroencephalography (EEG) parameters as endpoints in clinical drug and device development. MethodsA structured survey was sent to a group of experts in the design and conduct of epilepsy trials in adults and children. This was followed by a virtual in-person meeting discussing the results of the trial and identifying a list of most important issues. ResultsSix clinical trialists and 5 individuals from pharmaceutical companies returned the survey containing 14 questions, and 8 clinical trialists and 10 pharma-representatives attended the meeting. Three main issues were identified (1) lack of accuracy of seizure diaries due to nocturnal seizures, subtle motor seizures, impairment of consciousness and lack of awareness of the seizure by the patient (2) inter-rater variability of EEG assessment (3) lack of standardization regarding definition(s) of seizures (clinical and electrographic), EEG recording methods and EEG data management. Recommended solutions included (1) validation of EEG parameters as biomarkers and use of wearables (2) development of a manual that describes EEG rating criteria, protocol for validation by > 1 central reader and use of a resolution of disagreements reporting template (3) standardization of EEG recording, data management and reporting. Discussion & conclusionCurrent developments in research and technology seem promising to advance the use of EEG parameters as potential endpoints and offer partial solutions to the current needs. However, continuous, focused and collaborative efforts of all stakeholders (academia, industry and regulatory agencies) are needed to formulate guidelines, validate emerging technologies and approve them for use in trials. It is the intent of this opinion “position paper” to stimulate those efforts.

How robust is the relationship between neural processing speed and cognitive abilities?

Individual differences in processing speed are consistently related to individual differences in cognitive abilities, but the mechanisms through which a higher processing speed facilitates reasoning remain largely unknown. To identify these mechanisms, researchers have been using latencies of the event-related potential (ERP) to study how the speed of cognitive processes associated with specific ERP components is related to cognitive abilities. Although there is some evidence that latencies of ERP components associated with higher-order cognitive processes are related to intelligence, results are overall quite inconsistent. These inconsistencies likely result from variations in analytic procedures and little consideration of the psychometric properties of ERP latencies in relatively small sample studies. Here we used a multiverse approach to evaluate how different analytical choices regarding references, low-pass filter cutoffs, and latency measures affect the psychometric properties of P2, N2, and P3 latencies and their relations with cognitive abilities in a sample of 148 participants. Latent correlations between neural processing speed and cognitive abilities ranged from -.49 to -.78. ERP latency measures contained about equal parts of measurement error variance and systematic variance, and only about half of the systematic variance was related to cognitive abilities, whereas the other half reflected nuisance factors. We recommend addressing these problematic psychometric properties by recording EEG data from multiple tasks and modeling relations between ERP latencies and covariates in latent variable models. All in all, our results indicate that there is a substantial and robust relationship between neural processing speed and cognitive abilities when those issues are addressed.

Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review.

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS–EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS–EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS–EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS–EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS–EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS–EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS–EEG data analyses in future research.

Understanding Clinical Reasoning through Visual Scanpath and Brain Activity Analysis

This paper presents an experimental study that analyzes learners’ visual behaviour and brain activity in clinical reasoning. An acquisition protocol was defined to record eye tracking and EEG data from 15 participants as they interact with a computer-based learning environment called Amnesia, a medical simulation system that assesses the analytical skills of novice medicine students while they solve patient cases. We use gaze data to assess learners’ visual focus and present our methodology to track learners’ reasoning process through scanpath pattern analysis. We also describe our methodology for examining learners’ cognitive states using mental engagement and workload neural indexes. Finally, we discuss the relationship between gaze path information and EEG and how our analyses can lead to new forms of clinical diagnostic reasoning assessment.

A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.

Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this study is to show the feasibility of decoding lower limb movements from EEG data recordings. The second aim is to investigate whether well-known neuroplastic adaptations in individuals with an amputation have an influence on decoding performance. To address this, we collected data from multiple individuals with lower limb amputation and a matched able-bodied control group. Using these data, we trained and evaluated common BCI methods that have already been proven effective for upper limb BCI. With an average test decoding accuracy of 84% for both groups, our results show that it is possible to discriminate different lower extremity movements using EEG data with good accuracy. There are no significant differences (p = 0.99) in the decoding performance of these movements between healthy subjects and subjects with lower extremity amputation. These results show the feasibility of using BCI for lower limb prosthesis control and indicate that decoding performance is not influenced by neuroplasticity-induced differences between the two groups.

Binaural sound therapy for tinnitus treatment: A psychometric and neurophysiological evaluation

IntroductionTinnitus is an annoying buzz that manifests itself in many ways. In addition, it can provoke anxiety, stress, depression, and fatigue. The acoustic therapies have become the most commonly applied treatment for tinnitus, either self-administered or clinically prescribed. Binaural Sound Therapy (BST) and Music Therapy (MT) aim to reverse the neuroplasticity phenomenon related to tinnitus by adequately stimulating the auditory path-way. The goal of this research is to evaluate the feasibility of applying BST for tinnitus treatment by comparing its effect with MT effect. Materials and methods34 patients with tinnitus from 29 to 60 years were informed about the experimental procedure and consented their participation. Patients were divided into two groups: 1) MT and 2) BST. They applied their sound-based treatment for one hour every day along eight weeks. Each treatment was adjusted to Hearing Loss (HL) and tinnitus characteristics of each participant. To record EEG data, a bio-signal amplifier with sixteen EEG channels was used. The system recorded data at a sampling frequency of 256 Hz within a bandwidth between 0.1 and 100 Hz. ResultsThe questionnaire-monitoring reported that MT increased tinnitus perception in 30% of the patients, and increased anxiety and stress in 8% of them. Regarding EEG-monitoring, major neural synchronicity over the frontal lobe was found after the treatment. In the case of BST reduced stress in 23% of patients. Additionally, BST reduced tinnitus perception similar to MT (15% of patients). With respect to EEG-monitoring, slightly major neural synchronicity over the right frontal lobe was found after the treatment. ConclusionsMT should be applied with caution since it could be worsening the tinnitus sufferer condition. On the other hand, BST is recommended for tinnitus sufferers who have side effects concerning stress but no anxiety.

Deep long short term memory based minimum variance kernel random vector functional link network for epileptic EEG signal classification

In this paper, the efficiently extracted and reduced features using deep long short-term memory (DLSTM) of the epileptic EEG signal integrated with minimum variance kernel random vector functional link net (MVKRVFLN) classifier are used to identify the seizure and non-seizure productively. Our methodology uses Children Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) multi-channel scalp EEG data, Bonn university single-channel intracranial EEG data, and two-channel Bern Barcelona intracranial EEG data recordings to assess the performance. The non-stationary, non-linear, complex, and chaotic type EEG signal is directly applied to DLSTM to obtain compressed significant features. The scatter plots of the DLSTM output features signify this compressed information are unique in nature. The excellent generalization ability, faster learning rate, simpler network-based MVKRVFLN classifier is formulated to well identify the seizure and non-seizure epochs precisely by applying the deep LSTM extracted discriminative features as input. The type of kernel function selection and choice of regularization coefficient are added information to improve the performance of the proposed approach. The suggested technique provides excellent classification accuracy, superior detection ability, faster speed, and insignificant false positive rate per hour, simpler structure, robustness to classify the seizure and non-seizure signals.

Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG.

The quality and quantity of training data are crucial to the performance of a deep-learning-based brain-computer interface (BCI) system. However, it is not practical to record EEG data over several long calibration sessions. A promising time- and cost-efficient solution is artificial data generation or data augmentation (DA). Here, we proposed a DA method for the motor imagery (MI) EEG signal called brain-area-recombination (BAR). For the BAR, each sample was first separated into two ones (named half-sample) by left/right brain channels, and the artificial samples were generated by recombining the half-samples. We then designed two schemas (intra- and adaptive-subject schema) corresponding to the single- and multi-subject scenarios. Extensive experiments using the classifier of EEGnet were conducted on two public datasets under various training set sizes. In both schemas, the BAR method can make the EEGnet have a better performance of classification (p < 0.01). To make a comparative investigation, we selected two common DA methods (noise-added and flipping), and the BAR method beat them (p < 0.05). Further, using the proposed BAR for augmentation, EEGnet achieved up to 8.3% improvement than a typical decoding algorithm CSP-SVM (p < 0.01), note that both the models were trained on the augmented dataset. This study shows that BAR usage can significantly improve the classification ability of deep learning to MI-EEG signals. To a certain extent, it may promote the development of deep learning technology in the field of BCI.

Internal causes of return trip effect based on eye movement and EEG indices

When traveling by car, the driver normally feels that the return trip is shorter than the outbound trip. The reason for this feeling, called return trip effect, is not clear. To explore the internal causes of this return trip effect, an indoor driving simulation experiment and a real car driving experiment were performed in this study. Questionnaires were used to obtain the estimated time of the outbound and return trips of the drivers. An eye tracker and an electroencephalograph equipment were used to record driver's eye movement and EEG data in the real-car driving experiment. The physiological indices and questionnaire results showed that the difference in the driver's cognitive loads of the outbound and return trips was the internal cause of the return trip effect. Drivers who were unfamiliar with the road had a different cognitive load between the two trips, which resulted in the return trip effect. However, drivers who were familiar with the road did not experience a return trip effect due to the close cognitive loads of the two trips.

P300 ERP Component on Eating Habits Profiling Using Dynamic Evolving Spiking Neural Network (deSNN)

— Unhealthy eating habits have become a big issue that often causes many chronic diseases in various countries in recent years. The current assessment to identify the status of eating habits is to use self-assessment. However, self-assessment is known to have an error or uncertainty value due to cognitive factors from respondents that affect the results of the assessment. A person's profile is potentially measured by reviewing Event-related potential (ERP) which is an ideal technique for understanding perception and attention. This study uses images of healthy and unhealthy foods as a stimulus when recording EEG data. The method used for classification is dynamic evolving spiking neural network (deSSN) based on the Neucube architecture. The results showed that the mean amplitude of the P300 component discovered in the Parietal and Occipital lobes was higher for healthy food in the healthy eating habits group. Whereas the unhealthy eating habits group was higher for unhealthy foods. The deSNN classification is proven to operate in learning ERP data but the accuracy rate is not too high due to inadequate sample training

Simultaneous Monitoring of Wireless Electrophysiology and Memory Behavioral Test as a Tool to Study Hippocampal Neurogenesis.

Brainwaves amplitude obtained from electroencephalography (EEG) has been well-recognized as a basis for cognitive capacity, memory, and learning on animals and humans. Adult neurogenesis mechanism is also linked to memory and learning improvement. Traditionally, researchers used to assess learning and memory parameters in rodent models by behavioral tasks. Therefore, the simultaneous monitoring of behavioral changes and EEG is particularly interesting in correlating data between brain activity and task-related behaviors. However, most of the equipment required to perform both studies are either complex, expensive, or uses a wired setup network that hinders the natural animals' movement. In this study, EEG was recorded with a wireless electrophysiology device along with the execution of a novel object recognition task (NORT). The animal's behavior was monitored simultaneously by a video tracking system. Both recordings were analyzed offline by their timestamps which were synched to link EEG signals with the animal's actions. Subjects consist of adult Wistar rats after medium-term environmental enrichment treatment. Six skull screw electrodes were fixed in pairs on both hemispheres over frontal, central, and parietal regions and were referenced to an electrode located posterior of the nasal bone. NORT protocol consists of exposing the animal to two identical objects for 10 min. After 2 h and 24 h, one of the objects was replaced with a novel one. Exploration time for each object was monitored by a behavioral tracking software (BTS) and EEG data recording. The analysis of the EEG synced with behavioral data consists of estimations of alpha and beta relative band power and comparisons between novel object recognition versus familiar object exploration, between three experimental stages. In this manuscript, we have discussed electrodes manufacturing process, epidural electrodes implantation surgery, environmental enrichment protocol, NORT protocol, BTS setup, EEG - BTS coupling for simultaneous monitoring in real-time, and EEG data analysis based on automatic events detection.

Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals

EEG signal processing is a fundamental method for neurophysiology research and clinical neurology practice. Historically the classification of EEG into physiological, pathological, or artifacts has been performed by expert visual review of the recordings. However, the size of EEG data recordings is rapidly increasing with a trend for higher channel counts, greater sampling frequency, and longer recording duration and complete reliance on visual data review is not sustainable. In this study, we publicly share annotated intracranial EEG data clips from two institutions: Mayo Clinic, MN, USA and St. Anne’s University Hospital Brno, Czech Republic. The dataset contains intracranial EEG that are labeled into three groups: physiological activity, pathological/epileptic activity, and artifactual signals. The dataset published here should support and facilitate training of generalized machine learning and digital signal processing methods for intracranial EEG and promote research reproducibility. Along with the data, we also propose a statistical method that is recommended for comparison of candidate classifier performance utilizing out-of-institution/out-of-patient testing.

The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data

ObjectiveTo identify autistic children, we used features extracted from two modalities (EEG and eye-tracking) as input to a machine learning approach (SVM). MethodsA total of 97 children aged from 3 to 6 were enrolled in the present study. After resting-state EEG data recording, the children performed eye-tracking tests individually on own-race and other-race stranger faces stimuli. Power spectrum analysis was used for EEG analysis and areas of interest (AOI) were selected for face gaze analysis of eye-tracking data. The minimum redundancy maximum relevance (MRMR) feature selection method combined with SVM classifiers were used for classification of autistic versus typically developing children. ResultsResults showed that classification accuracy from combining two types of data reached a maximum of 85.44%, with AUC = 0.93, when 32 features were selected. LimitationsThe sample consisted of children aged from 3 to 6, and no younger patients were included. ConclusionsOur machine learning approach, combining EEG and eye-tracking data, may be a useful tool for the identification of children with ASD, and may help for diagnostic processes.

Upper limb complex movements decoding from pre-movement EEG signals using wavelet common spatial patterns

Background and objectiveDecoding functional movements from electroencephalographic (EEG) activity for motor disability rehabilitation is essential to develop home-use brain-computer interface systems. In this paper, the classification of five complex functional upper limb movements is studied by using only the pre-movement planning and preparation recordings of EEG data. MethodsNine healthy volunteers performed five different upper limb movements. Different frequency bands of the EEG signal are extracted by the stationary wavelet transform. Common spatial patterns are used as spatial filters to enhance separation of the five movements in each frequency band. In order to increase the efficiency of the system, a mutual information-based feature selection algorithm is applied. The selected features are classified using the k-nearest neighbor, support vector machine, and linear discriminant analysis methods. ResultsK-nearest neighbor method outperformed the other classifiers and resulted in an average classification accuracy of 94.0 ± 2.7% for five classes of movements across subjects. Further analysis of each frequency band's contribution in the optimal feature set, showed that the gamma and beta frequency bands had the most contribution in the classification. To reduce the complexity of the EEG recording system setup, we selected a subset of the 10 most effective EEG channels from 64 channels, by which we could reach an accuracy of 70%. Those EEG channels were mostly distributed over the prefrontal and frontal areas. ConclusionsOverall, the results indicate that it is possible to classify complex movements before the movement onset by using spatially selected EEG data.

A step forward in the quest for a mobile EEG-designed epoch for psychophysiological studies.

The aim of this study was to compare a reconfigurable mobile electroencephalography (EEG) system (M-EMOTIV) based on the Emotiv Epoc® (which has the ability to record up to 14 electrode sites in the 10/20 International System) and a commercial, clinical-grade EEG system (Neuronic MEDICID-05®), and then validate the rationale and accuracy of recordings obtained with the prototype proposed. In this approach, an Emotiv Epoc® was modified to enable it to record in the parieto-central area. All subjects (15 healthy individuals) performed a visual oddball task while connected to both devices to obtain electrophysiological data and behavioral responses for comparative analysis. A Pearson's correlation analysis revealed a good between-devices correlation with respect to electrophysiological measures. The present study not only corroborates previous reports on the ability of the Emotiv Epoc® to suitably record EEG data but presents an alternative device that allows the study of a wide range of psychophysiological experiments with simultaneous behavioral and mobile EEG recordings.