The authors would like to acknowledge Christopher Green, Bradley Robinson, and Maria Manuela Valladares, for helping with data collection. Funding for EEG equipment was provided by Utah State University&#39;s Office of Research and Graduate Studies Equipment Grant to Kerry Jordan&#39;s Multisensory Cognition Lab. Benjamin Call is supported by a Presidential Doctoral Research Fellowship attained from Utah State University&#39;s School of Graduate Studies for his work with Dr. Wade Goodridge.

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The authors declare that they have no conflicting or competing financial interests.

The protocol discusses the application of electroencephalography to measure brain activity for participants working problems from two typical spatial ability instruments and highly spatial engineering Statics problems. The methods detailed here may ultimately be able to help understand the neural efficiency of high and low performers engaged in working these problems. It is vital to understand any differences in neural efficiencies of engineering students working on the MCT and PSVT:R, as these tests are often used to assess spatial ability. Comparing them to each other allows us to better assess their applicability to success in engineering and their position in foundational engineering curricula.
The protocol establishes procedures for research on neural efficiencies associated with spatial cognition tasks. It is important that reliable and valid instruments are used to assess spatial abilities connected to engineering content. It is also important that engineering problems target the representative engineering content for a specific course. EEG measurements offer a distinct non-intrusive capability to triangulate cognitive component data from students engaged in spatial aspects of engineering problem solving. Proper time stamping should be used for such data collection, ensuring triangulation with video-archived events. IRB protocols should be stringently followed, ensuring the anonymity of participant data and analysis.
Most troubleshooting concerns occur while collecting EEG data as detailed below, and the majority of those are handled before data are recorded. Corrections for poor impedance and noise are best handled during setup. Following the EEG headset manufacturer&#39;s instructions is critical, and in our experience the indications by the manufacturer&#39;s software can direct users to check specific electrodes. Typically the connection between the felt pad and the participant&#39;s head needs to be dampened more, or the connection between each electrode and the headset may need to be checked. If some connectivity is visible, but the quality is poor, using the syringe to re-dampen the felt is often sufficient, and at times the headset needs to be adjusted physically to ensure solid contact with the scalp. In a couple of cases, we had to ask participants to rinse their hair in a sink before we were able to obtain a good connection. When the electrode appeared to not be transmitting data, it was often remedied by removing the electrode and then reinserting it. At times, the plastic case for the electrode may crack, in which case it will need to be replaced.
Other troubleshooting may occur during data analysis, and is discussed in the protocol. Data preprocessing involves the filtering and removal of artifacts. Often the data analysis software supports manual rejection as well as scripts that can be run during preprocessing and processing of the data.
Modifications were made to a script within the analysis software. Those changes are documented in the supplemental code file. Modifications to the protocol may also be made. A concurrent protocol has been used in which verbal answers are required during the study. This will introduce more artifacts into the EEG data, but will provide more insight into the participant&#39;s functional knowledge during the tests. An alternative has also been used in which the participant participates in a video-recorded interview with the researcher after the session.
Other recommended potential modifications include utilizing different spatial ability tests14, different engineering questions17, or other educational assessments. Different brain activity metrics, possible via EEG and other instrumentation, could also shed light on the difficulty, or other characteristics, of skill assessments.
We recognize that there are limitations with the technique defined in this document. The constructs of spatial ability (rotation and cutting planed surface) measured by the PSVT:R and MCT are only two of many potential constructs measurable with other spatial metrics. In addition, different spatially intensive tasks (i.e., different types of problems or different courses and coursework) may also be assessed. Research into neural efficiency should of course also be conducted on a broader scope than just fundamental engineering courses such as Statics. For example, it should be investigated within the many STEM fields acknowledged in the literature to depend on spatial reasoning3. Also, neural efficiency studies should not be limited to skills directly linked only to spatial ability21-28. Even within the research involved in brainwave measurement, the practice of averaging power measurements over the duration of a task prohibits investigation into other correlations that may occur within the patterns of brain activity. EEG measurements, due to their temporal responsivity, are not limited to neural efficiency studies. And EEG instrumentation is itself limited by the depth of brainwave activity it can detect, particularly when compared to the higher spatial resolution of functional near-infrared spectroscopy or functional magnetic resonance imaging, although its temporal responsivity remains among the best36.
Ultimately, the potential of using physiological measurements to provide insight to educational theory and practice appears immense37,38. The technical approach and goals of this protocol are different than the biofeedback approach using EEG in educational/training studies39, but all are worth consideration as insight is gained in phenomena such as spatial ability development and engineering skill development. This approach of using EEG to examine neural efficiency between spatial tasks inherent within specific spatial ability instruments defines another method of segregating spatial ability tests. This exemplifies a new application of a neuroscientific approach for investigating spatial ability tests, as well as opening a neuroscientific approach towards the investigation of existing educational theory. Finding methods for verification and validation is part of engineering culture. Within this new application, physiological brainwave testing can open a new realm of understanding and refining educational theory. Indeed, if viewed as a potential avenue of validation, a novel and new generation of engineering educational research may arise.

Spatial ability is vital to Science, Technology, Engineering, and Math (STEM) fields and education and correlates with success in these areas1,2,3. Therefore, it is important to understand the development of how spatial ability impacts problem solving4. Spatial ability has been linked to interest5, performance6, success in engineering academics7 and success in engineering professionals8. However, there is not a lot of work indicating specific neural processes in solving problems typical to many spatial ability instruments, nor specific engineering content that is highly spatial.
This paper provides an introduction to methods used for data collection and analysis of spatial ability instrument scores combined with neural measurements. The intent of publishing with JoVE is to make these methods more accessible to a broader audience. General public hardware and software were utilized in this study. As a methods paper, full results/data sets are not reported, nor are multiple samples provided. All images were captured specifically for this publication. The methods detailed below were utilized in preparing a preliminary conference report9 based on data from eight college sophomore-aged participants, three of whom were female.
Many existing instruments are used to indicate levels of spatial ability inherent to or learned by individuals. Two valid and reliable10,11 instruments that are commonly used are the Mental Cutting Test (MCT)12 and the Purdue Spatial Visualization test of Rotations (PSVT:R)13. While originally occupationally designed14 these instruments test different stages of spatial visualization development described by Piagetian theory10,15. The use of these instruments creates a need to understand the underlying physiological cognitive phenomena existing when individuals work through these problems. For this reason, this study aims to showcase methods utilizing empirical physiological data that may ultimately improve the analysis and understanding of spatial thought, verify existing metrics testing capabilities, and increase the applicability of spatial assessments to more complex problems typical to engineering education. Many of these problems can be encountered in engineering Statics.
Statics is a foundational mechanics course delivered to most engineering students (e.g., Biological, Mechanical, Civil, Environmental, Aerospace Engineering)16,17. It is one of the first extensive problem solving experiences that students are given in core engineering content18. Statics involves the study of the interaction of forces on a rigid body that is at rest or moving at a constant velocity. Unfortunately Statics has high dropout, withdrawal, and failure rates (14% as seen in the investigated University) and this may be related to traditional lecture and curriculum delivery models that omit key avenues of support such as spatially enhanced approaches to education. For example, spatially enhanced approaches in Statics can target the visualization of how forces interact outside of typical analytical analysis and reinforce students&#39; procedural knowledge with grounded conceptualization. The effectiveness of such interventions needs to be investigated from a cognitive neuroscientific perspective.
Electroencephalography (EEG) presents a unique and mobile method of measuring students&#39; brainwave activity. Individuals performing tasks who elicit beta activation are generally very engaged with the task specifics and are attentive to what they are doing19,20. As task demands increase, the amplitude of the beta wave increases, as does the size of the cortical area the bandwidth frequencies occupy. The more neurons that fire within the beta frequency range (alpha: 8 - 12 Hz, beta: 12 - 24Hz) can be defined as greater beta power. Relatedly, as one becomes more experienced in a task, the amplitude of beta waves decreases, generating less beta power. This is part of the neural efficiency hypothesis21-28, in which greater task experience when performing a task is related to a decrease in frequency power. Although EEG has previously been used in the study of spatial abilities (often for mental rotation and spatial navigation tasks) &#8212; and applicable data have been identified in the alpha, beta, and theta bands27-33 &#8212; alpha and beta bands were observed for this study, and beta was selected for further representative analysis in this paper and in the preliminary conference report9. The procedures defined below thus focus on beta band analysis, but an investigation into all three bands, depending on the logged data, is recommended in the future.
The neural efficiency hypothesis has been tested on various tasks, including chess, visuospatial memory, balancing, and resting. All have indicated task experience as a factor in decreased frequency power when performing familiar tasks. One particular study25 has presented evidence that, although the intelligence of a person (as measured by IQ) can help the individual acquire the skills to perform a task, experience with the task outweighs intelligence in its contribution to neural efficiency. In other words, the more experienced an individual is, the more neurally efficient he or she becomes.
Existing neural efficiency studies involving spatial ability have primarily focused on spatial rotation, and different problem sets have been used to compare different populations (e.g., male/female)27-28. EEG studies of spatial ability tasks have also provided insight by comparing performance to other task types (e.g., verbal tasks)27,29,30. The methods discussed in this paper focus on and compare problems from the MCT, PSVT:R, as well as static equilibrium tasks, which are related to spatial ability but are not limited to spatial rotation and navigation. Other spatial tasks may be used in place of the ones given as examples in this manuscript. In this way, additional insight may be obtained in the future regarding different populations (e.g., male/female or expert/novice) to ultimately help improve engineering educational practices.
In an effort to investigate spatial ability and engineering aptitude, we have developed a protocol utilizing EEG measurements to identify the beta wave activations of low performing to high performing participants during a limited battery of specific spatial and engineering tasks. In this case, the term high performer is related to the performance of the participant, and is not reflective of the amount of time spent in the field by the learner, as all participants were at approximately the same point in their education. Additionally, the problem set involved is quite specific and basic; thus the terms &#34;expert&#34; or &#34;high performing&#34; herein must not be viewed in the sense of an expert, professionally employed engineer, but representing only high performance in this narrow slice of engineering mechanics curriculum and spatial ability instruments. The neural measurements can also be used to identify any gross trends for which task types may recruit more cognitive resources than others, with possible interpretation regarding levels of difficulty. This information may potentially provide insight into future assessment and intervention with regard to spatial ability. Other future insight may be derived by considering more specific regions of the brain, which was not possible in this study due to the limited number of channels available in the EEG hardware used.

<table><tbody><tr><td>Emotiv EPOC Model 1.0</td><td>Emotiv</td><td>Model: Emotiv Premium</td><td>&quot;High resolution, multi-channel, portable EEG system.&quot;</td></tr><tr><td>Emotiv Control Panel (software)</td><td>Emotiv</td><td></td><td>Used for data collection.</td></tr><tr><td>Emotiv Testbench (software)</td><td>Emotiv</td><td></td><td>Used for data collection.</td></tr><tr><td>Virtual Serial Port Emulator - VSPE (software)</td><td>ETERLOGIC.COM</td><td></td><td>Used COM10 in data collection. Available as a free download, depending on the operating system.</td></tr><tr><td>E-Prime 2.0 (software)</td><td>Psychology Software Tools</td><td></td><td>Used for data collection (presentation of problems to participants and collection of markers for different phases).</td></tr><tr><td>EEGLab 13.4.4b (software)</td><td>Swartz Center for Computational Neuroscience (SCCN)</td><td></td><td>Used for data analysis. &quot;An open source environment for electrophysiological signal processing&quot;. SCCN is a Center of the Institute for Neural Computation, the University of California San Diego.</td></tr><tr><td>MATLAB R2014b</td><td>The Mathworks, Inc.</td><td></td><td>Used to run EEGLab</td></tr><tr><td>Microsoft Excel 2013</td><td>Microsoft</td><td></td><td>Used to assemble and compare tabulated results from EEGLab &amp;amp; MATLAB, to create tables</td></tr><tr><td>Camcorder with built in Mic</td><td>Canon</td><td>CNVHFR50</td><td>Used to record sessions</td></tr><tr><td>Syringe Kit (5 cc syringe &amp;amp; 16 G blunted needles x 2)</td><td>Electro-Cap Intnl. Inc.</td><td>E7</td><td>For keeping the EEG cap&#039;s felts damp.</td></tr><tr><td>Nuprep EEG Skin Prep Gel</td><td>Weaver and Company</td><td>10-30</td><td>For cleaning the mastoid process.</td></tr><tr><td>Sanitizer</td><td>Purell</td><td>S-12808</td><td>For sanitizing hands</td></tr></tbody></table>

utilizing electroencephalography measurements for comparison of task-specific neural efficiencies: spatial intelligence tasks

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables comparative calculation of individuals&#39; neural efficiency as they perform successive tasks requiring spatial ability to derive solutions. Neural efficiency here is defined as having less beta activation, and therefore expending fewer neural resources, to perform a task in comparison to other groups or other tasks. For inter-task comparisons of tasks with similar durations, these measurements may enable a comparison of task type difficulty. For intra-participant and inter-participant comparisons, these measurements provide potential insight into the participant&#39;s level of spatial ability and different engineering problem solving tasks. Performance on the selected tasks can be analyzed and correlated with beta activities. This work presents a detailed research protocol studying the neural efficiency of students engaged in the solving of typical spatial ability and Statics problems. Students completed problems specific to the Mental Cutting Test (MCT), Purdue Spatial Visualization test of Rotations (PSVT:R), and Statics. While engaged in solving these problems, participants&#39; brain waves were measured with EEG allowing data to be collected regarding alpha and beta brain wave activation and use. The work looks to correlate functional performance on pure spatial tasks with spatially intensive engineering tasks to identify the pathways to successful performance in engineering and the resulting improvements in engineering education that may follow.

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Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

This manuscript describes an approach to measure neural activity of humans while solving spatially focused engineering problems. The electroencephalogram methodology helps interpret beta brain wave measurements in terms of neural efficiency, with the aim of ultimately enabling comparisons of task performance both between problem types and between participants.

Spatial intelligence is often linked to success in engineering education and engineering professions. The use of electroencephalography enables ...

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11.3K Views.  Utah State University. This manuscript describes an approach to measure neural activity of humans while solving spatially focused engineering problems. The electroencephalogram methodology helps interpret beta brain wave measurements in terms of neural efficiency, with the aim of ultimately enabling comparisons of task performance both between problem types and between participants.

Video: Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks

Performing Behavioral Tasks in Subjects with Intracranial Electrodes

Patients having stereo-electroencephalography (SEEG) electrode, subdural grid or depth electrode implants have a multitude of electrodes implanted in different areas of their brain for the localization of their seizure focus and eloquent areas. After implantation, the patient must remain in the hospital until the pathological area of brain is found and possibly resected. During this time, these patients offer a unique opportunity to the research community because any number of behavioral paradigms can be performed to uncover the neural correlates that guide behavior. Here we present a method for recording brain activity from intracranial implants as subjects perform a behavioral task designed to assess decision-making and reward encoding. All electrophysiological data from the intracranial electrodes are recorded during the behavioral task, allowing for the examination of the many brain areas involved in a single function at time scales relevant to behavior. Moreover, and unlike animal studies, human patients can learn a wide variety of behavioral tasks quickly, allowing for the ability to perform more than one task in the same subject or for performing controls. Despite the many advantages of this technique for understanding human brain function, there are also methodological limitations that we discuss, including environmental factors, analgesic effects, time constraints and recordings from diseased tissue. This method may be easily implemented by any institution that performs intracranial assessments; providing the opportunity to directly examine human brain function during behavior.

Patients implanted with intracranial electrodes provide a unique opportunity to record neurological data from multiple areas of the brain while the patient performs behavioral tasks. Here, we present a method of recording from implanted patients that can be reproducible at other institutions with access to this patient population.

Patients having stereo-electroencephalography (SEEG) electrode, subdural grid or depth electrode implants have a multitude of electrodes implanted in ...

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar tasks. Participants' behavior during task performance in an fMRI scanner can then be correlated to the brain activity using the blood-oxygen-level-dependent signal. We measure behavior to be able to sort correct trials, where the subject performed the task correctly and then be able to examine the brain signals related to correct performance. Conversely, if subjects do not perform the task correctly, and these trials are included in the same analysis with the correct trials we would introduce trials that were not only for correct performance. Thus, in many cases these errors can be used themselves to then correlate brain activity to them. We describe two complementary tasks that are used in our lab to examine the brain during suppression of an automatic responses: the stroop1 and anti-saccade tasks. The emotional stroop paradigm instructs participants to either report the superimposed emotional 'word' across the affective faces or the facial 'expressions' of the face stimuli1,2. When the word and the facial expression refer to different emotions, a conflict between what must be said and what is automatically read occurs. The participant has to resolve the conflict between two simultaneously competing processes of word reading and facial expression. Our urge to read out a word leads to strong 'stimulus-response (SR)' associations; hence inhibiting these strong SR's is difficult and participants are prone to making errors. Overcoming this conflict and directing attention away from the face or the word requires the subject to inhibit bottom up processes which typically directs attention to the more salient stimulus. Similarly, in the anti-saccade task3,4,5,6, where an instruction cue is used to direct only attention to a peripheral stimulus location but then the eye movement is made to the mirror opposite position. Yet again we measure behavior by recording the eye movements of participants which allows for the sorting of the behavioral responses into correct and error trials7 which then can be correlated to brain activity. Neuroimaging now allows researchers to measure different behaviors of correct and error trials that are indicative of different cognitive processes and pinpoint the different neural networks involved.

The goal of our research is to correlate behavior to brain activity. Accurate behavioral measures and imaging techniques allow us to elucidate brain-behavior relationships.

The aim of this methods paper is to describe how to implement a neuroimaging technique to examine complementary brain processes engaged by two similar ...

Neuroscience

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the cortical networks involved in dynamical perceptual and cognitive tasks, such as attending to different sounds in a cocktail party. Many past studies have employed data recorded at the sensor level only, i.e., the magnetic fields or the electric potentials recorded outside and on the scalp, and have usually focused on activity that is time-locked to the stimulus presentation. This type of event-related field / potential analysis is particularly useful when there are only a small number of distinct dipolar patterns that can be isolated and identified in space and time. Alternatively, by utilizing anatomical information, these distinct field patterns can be localized as current sources on the cortex. However, for a more sustained response that may not be time-locked to a specific stimulus (e.g., in preparation for listening to one of the two simultaneously presented spoken digits based on the cued auditory feature) or may be distributed across multiple spatial locations unknown a priori, the recruitment of a distributed cortical network may not be adequately captured by using a limited number of focal sources.
Here, we describe a procedure that employs individual anatomical MRI data to establish a relationship between the sensor information and the dipole activation on the cortex through the use of minimum-norm estimates (MNE). This inverse imaging approach provides us a tool for distributed source analysis. For illustrative purposes, we will describe all procedures using FreeSurfer and MNE software, both freely available. We will summarize the MRI sequences and analysis steps required to produce a forward model that enables us to relate the expected field pattern caused by the dipoles distributed on the cortex onto the M/EEG sensors. Next, we will step through the necessary processes that facilitate us in denoising the sensor data from environmental and physiological contaminants. We will then outline the procedure for combining and mapping MEG/EEG sensor data onto the cortical space, thereby producing a family of time-series of cortical dipole activation on the brain surface (or "brain movies") related to each experimental condition. Finally, we will highlight a few statistical techniques that enable us to make scientific inference across a subject population (i.e., perform group-level analysis) based on a common cortical coordinate space.

We use magneto- and electroencephalography (MEG/EEG), combined with anatomical information captured by magnetic resonance imaging (MRI), to map the dynamics of the cortical network associated with auditory attention.

Magneto- and electroencephalography (MEG/EEG) are neuroimaging techniques that provide a high temporal resolution particularly suitable to investigate the ...

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain signal. Typically, researchers represent the brain's response as the mean across repeated experimental trials and disregard signal fluctuations over time as "noise". However, it is becoming clear that brain signal variability conveys meaningful functional information about neural network dynamics. This article describes the novel method of multiscale entropy (MSE) for quantifying brain signal variability. MSE may be particularly informative of neural network dynamics because it shows timescale dependence and sensitivity to linear and nonlinear dynamics in the data.

Neuroimaging researchers typically consider the brain's response as the mean activity across repeated experimental trials and disregard signal variability over time as "noise". However, it is becoming clear that there is signal in that noise. This article describes the novel method of multiscale entropy for quantifying brain signal variability in the time domain.

When considering human neuroimaging data, an appreciation of signal variability represents a fundamental innovation in the way we think about brain ...

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&#8217; 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&#8217;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&#8217;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 &#8211; BTS coupling for simultaneous monitoring in real-time, and EEG data analysis based on automatic events detection.

The protocol presented here provides information on the simultaneous electroencephalography (EEG) and behavioral assessment in real-time. We have discussed all steps involved in this protocol as an attractive solution for researchers in many fields of neuroscience, particularly in learning and memory areas.

Brainwaves amplitude obtained from electroencephalography (EEG) has been well-recognized as a basis for cognitive capacity, memory, and learning on ...

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

Conducting Concurrent Electroencephalography and Functional Near-Infrared Spectroscopy Recordings with a Flanker Task

Concurrent EEG and fNIRS recordings offer an excellent opportunity to gain a full understanding of the neural mechanism of cognitive processing by inspecting the relationship between the neural and hemodynamic signals. EEG is an electrophysiological technology that can measure the rapid neuronal activity of the cortex, whereas fNIRS relies on the hemodynamic responses to infer brain activation. The combination of EEG and fNIRS neuroimaging techniques can identify more features and reveal more information associated with the functioning of the brain. In this protocol, fused EEG-fNIRS measurements were performed for concurrent recordings of evoked-electrical potentials and hemodynamic responses during a Flanker task. In addition, the critical steps for setting up the hardware and software system as well as the procedures for data acquisition and analysis were provided and discussed in detail. It is expected that the present protocol can pave a new avenue for improving the understanding of the neural mechanisms underlying various cognitive processes by using the EEG and fNIRS signals.

The present protocol describes how to perform concurrent EEG and fNIRS recordings and how to inspect the relationship between the EEG and fNIRS data.&#160;

Concurrent EEG and fNIRS recordings offer an excellent opportunity to gain a full understanding of the neural mechanism of cognitive processing by ...

A Visuospatial Planning Task Coupled with Eye-Tracker and Electroencephalogram Systems

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since planning is compromised in several neuropsychiatric disorders, the implementation of proper clinical and experimental tests to examine planning is critical. Due to the nature of the deployment of planning, in which several cognitive domains participate, the assessment of planning and the design of behavioral paradigms coupled with neuroimaging methods are current challenges in cognitive neuroscience. A planning task was evaluated in combination with an electroencephalogram (EEG) system and eye movement recordings in 27 healthy adult participants. Planning can be separated into two stages: a mental planning stage in which a sequence of steps is internally represented and an execution stage in which motor action is used to achieve a previously planned goal. Our protocol included a planning task and a control task. The planning task involved solving 36 maze trials, each representing a zoo map. The task had four periods: i) planning, where the subjects were instructed to plan a path to visit the locations of four animals according to a set of rules; ii) maintenance, where the subjects had to retain the planned path in their working memory; iii) execution, where the subjects used eye movements to trace the previously planned path as indicated by the eye-tracker system; and iv) response, where the subjects reported the order of the visited animals. The control task had a similar structure, but the cognitive planning component was removed by modifying the task goal. The spatial and temporal patterns of the EEG revealed that planning induces a gradual and lasting rise in frontal-midline theta activity (FM&#952;) over time. The source of this activity was identified within the prefrontal cortex by source analyses. Our results suggested that the experimental paradigm combining EEG and eye-tracker systems was optimal for evaluating cognitive planning.

The study of cognitive planning combining EEG and eye-tracking systems provides a multimodal approach to investigate the neural mechanisms that mediate cognitive control and goal-directed behavior in humans. Here, we describe a protocol for investigating the role of brain oscillations and eye movements in planning performance.

The planning process, characterized by the capability to formulate an organized plan to reach a goal, is essential for human goal-directed behavior. Since ...

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Alteration of electroencephalography (EEG) signals during task-specific movement of the impaired limb has been reported as a potential biomarker for the severity of motor impairment and for the prediction of motor recovery in individuals with stroke. When implementing EEG experiments, detailed paradigms and well-organized experiment protocols are required to obtain robust and interpretable results. In this protocol, we illustrate a task-specific paradigm with upper limb movement and methods and techniques needed for the acquisition and analysis of EEG data. The paradigm consists of 1 min of rest followed by 10 trials comprising alternating 5 s and 3 s of resting and task (hand extension)-states, respectively, over 4 sessions. EEG signals were acquired using 32 Ag/AgCl scalp electrodes at a sampling rate of 1,000 Hz. Event-related spectral perturbation analysis associated with limb movement and functional network analyses at the global level in the low-beta (12-20 Hz) frequency band were performed. Representative results showed an alteration of the functional network of low-beta EEG frequency bands during movement of the impaired upper limb, and the altered functional network was associated with the degree of motor impairment in chronic stroke patients. The results demonstrate the feasibility of the experimental paradigm in EEG measurements during upper limb movement in individuals with stroke. Further research using this paradigm is needed to determine the potential value of EEG signals as biomarkers of motor impairment and recovery.

The experimental protocol demonstrates the paradigm for acquiring and analyzing electroencephalography (EEG) signals during upper limb movement in individuals with stroke. The alteration of the functional network of low-beta EEG frequency bands was observed during the movement of the impaired upper limb and was associated with the degree of motor impairment.

Alteration of electroencephalography (EEG) signals during task-specific movement of the impaired limb has been reported as a potential biomarker for the ...

Integrating MEG and HD-EEG in Pediatric Epilepsy Localization

Electromagnetic Source Imaging in Presurgical Evaluation of Children with Drug-Resistant Epilepsy

For children with drug-resistant epilepsy (DRE), seizure freedom relies on the delineation and resection (or ablation/disconnection) of the epileptogenic zone (EZ) while preserving the eloquent brain areas. The development of a reliable and noninvasive localization method that provides clinically useful information for the localization of the EZ is, therefore, crucial to achieving successful surgical outcomes. Electric and magnetic source imaging (ESI and MSI) have been increasingly utilized in the presurgical evaluation of these patients showing promising findings in the delineation of epileptogenic as well as eloquent brain areas. Moreover, the combination of ESI and MSI into a single solution, namely electromagnetic source imaging (EMSI), performed on simultaneous high-density electroencephalography (HD-EEG) and magnetoencephalography (MEG) recordings has shown higher source localization accuracy than either modality alone. Despite these encouraging findings, such techniques are performed in only a few tertiary epilepsy centers, are rarely recorded simultaneously, and are underutilized in pediatric cohorts. This study illustrates the experimental setup for recording simultaneous MEG and HD-EEG data as well as the methodological framework for analyzing these data aiming to localize the irritative zone, the seizure onset zone, and eloquent brain areas in children with DRE. More specifically, the experimental setups are presented for (i) recording and localizing interictal and ictal epileptiform activity during sleep and (ii) recording visual-, motor-, auditory-, and somatosensory-evoked responses and mapping relevant eloquent brain areas (i.e., visual, motor, auditory, and somatosensory) during visuomotor task, as well as auditory and somatosensory stimulations. Detailed steps of the data analysis pipeline are further presented for performing EMSI as well as individual ESI and MSI using equivalent current dipole (ECD) and dynamic statistical parametric mapping (dSPM).

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Magnetoencephalography (MEG) and high-density electroencephalography (HD-EEG) are rarely recorded simultaneously, although they yield confirmatory and complementary information. Here, we illustrate the experimental setup for recording simultaneous MEG and HD-EEG and the methodology for analyzing these data aiming to localize epileptogenic and eloquent brain areas in children with drug-resistant epilepsy.

For children with drug-resistant epilepsy (DRE), seizure freedom relies on the delineation and resection (or ablation/disconnection) of the epileptogenic ...