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

Summary

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.

Abstract

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

Introduction

Spatial ability is vital to Science, Technology, Engineering, and Math (STEM) fields and education and correlates with success in these areas^1,2,3. 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 reliable^10,11 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 theory^10,15. 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' 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' brainwave activity. Individuals performing tasks who elicit beta activation are generally very engaged with the task specifics and are attentive to what they are doing^19,20. 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 hypothesis^21-28, 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) — and applicable data have been identified in the alpha, beta, and theta bands^27-33 — 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 "expert" or "high performing" 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.

Protocol

Ethical Statement Regarding Use of Human Participants Procedures involved in this work have been approved by the Institutional Review Board (IRB) at Utah State University for the study of human subjects. It is recommended that any similar work should also be approved by the relevant IRB. Participants are allowed to stop or withdraw from the study at any time during the experiment. 1. Selection of Participants Select participants on a voluntary basis from students currently enrol…

Representative Results

In this section, the preceding steps are illustrated with sample figures as described below. Full data summaries with statistical tests are not provided, as the objective of this paper is to focus on methods. Examples of potential PSVT:R, MCT, and Spatial problems are given in Figure 1, Figure 2, and Figure 3, respectively. The EEG cap will collect brain activation via electrical potentials for each given channel, which can be viewed in parallel as shown in Figure 7. …

Discussion

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 as…

Materials

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