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. 2017 Nov 10:11:527.
doi: 10.3389/fnhum.2017.00527. eCollection 2017.

Deployment of Mobile EEG Technology in an Art Museum Setting: Evaluation of Signal Quality and Usability

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Deployment of Mobile EEG Technology in an Art Museum Setting: Evaluation of Signal Quality and Usability

Jesus G Cruz-Garza et al. Front Hum Neurosci. .

Abstract

Electroencephalography (EEG) has emerged as a powerful tool for quantitatively studying the brain that enables natural and mobile experiments. Recent advances in EEG have allowed for the use of dry electrodes that do not require a conductive medium between the recording electrode and the scalp. The overall goal of this research was to gain an understanding of the overall usability and signal quality of dry EEG headsets compared to traditional gel-based systems in an unconstrained environment. EEG was used to collect Mobile Brain-body Imaging (MoBI) data from 432 people as they experienced an art exhibit in a public museum. The subjects were instrumented with either one of four dry electrode EEG systems or a conventional gel electrode EEG system. Each of the systems was evaluated based on the signal quality and usability in a real-world setting. First, we describe the various artifacts that were characteristic of each of the systems. Second, we report on each system's usability and their limitations in a mobile setting. Third, to evaluate signal quality for task discrimination and characterization, we employed a data driven clustering approach on the data from 134 of the 432 subjects (those with reliable location tracking information and usable EEG data) to evaluate the power spectral density (PSD) content of the EEG recordings. The experiment consisted of a baseline condition in which the subjects sat quietly facing a white wall for 1 min. Subsequently, the participants were encouraged to explore the exhibit for as long as they wished (piece-viewing). No constraints were placed upon the individual in relation to action, time, or navigation of the exhibit. In this freely-behaving approach, the EEG systems varied in their capacity to record characteristic modulations in the EEG data, with the gel-based system more clearly capturing stereotypical alpha and beta-band modulations.

Keywords: EEG; MoBI; aesthetics; dry-electrodes; museum; real-world recording; signal quality.

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Figures

Figure 1
Figure 1
Image of each EEG system and their respective electrode montage. The chart shows the cumulative usage (stacked timeline) of each system over the course of 22 consecutive weekly experimental sessions. The arrows with headset names indicate the session number in which each device was introduced into the experiment. The total number of recordings with each headset is shown under the diagram of the electrode montage for each devices (N = number of subjects).
Figure 2
Figure 2
(Left) Data collection and pre-processing pipeline. Visual inspection was performed on all the partitioned data to find and reject bad epochs. The findings were used to create a set of guidelines (gray dashed boxes) to automatically remove bad epochs. (Right, A–D) Examples of the four most recurring gross artifacts encountered through visual inspection. The red dashed sections show artifactual segments of EEG data and the blue dashed sections show normal EEG data.
Figure 3
Figure 3
Channel and data rejection statistics for all the headsets. (A) Channel rejection rate across subjects during baseline and piece-viewing. The orange bars indicate the channel rejection rate due to the channel surpassing the upper threshold for 20% of the session, while the blue bars show the rejection rate based on the channel not surpassing the lower threshold for 80% of the session. (B) Data rejection rate across subjects during baseline and piece-viewing, after artefactual channel removal. (C) EEG channel rejection rate based on the amplitude thresholding criteria. Red: Ground. Blue: Reference. For BPG, the electrodes removed around the reference correspond to lower-threshold rejections, although those channels did not exhibit digitization errors.
Figure 4
Figure 4
Results of kernel k-means clustering for electrode Fp1 (Gaussian kernel; σ = 26). (A) Three-dimensional visualization of the final clusters from kernel k-means. Each point in the scatter plot corresponds to the total normalized power (area under the PSD) in the delta, alpha, and gamma bands for a single 4 s window. (B) The pie charts show the contribution of each headset type to the PSD clusters. To the right, the last pie chart shows the overall distribution of the PSDs for each headset type. (C) The mean of the PSDs for each headset type is shown below each cluster's pie chart, along with the 5th and 95th percentiles as shaded regions. The PSDs from headset M32-A were excluded from visualization because they contain a prominent peak at 30 Hz from unknown source, not representative of the PSDs from headsets M32-B, M32-C, and M32-D. (C) Distribution of gender and condition information for the PDSs grouped in each cluster. *Indicates most aesthetically pleasing and **indicates most emotionally stimulating as reported in the questionnaire.
Figure 5
Figure 5
Gaussian kernel (σ = 22) k-means results for the parietal electrode F4. (A) Three-dimensional visualization of the final clusters from kernel k-means. Each point in the scatter plot corresponds to the total normalized power (area under the PSD) in the delta, alpha, and gamma bands for a single 4-s window. (B) The pie charts show the contribution of each headset type to the PSD clusters. To the right, the last pie chart shows the overall distribution of the PSDs for each headset type. (C) The mean of the PSDs for each headset type is shown below each cluster's pie chart, along with the 5th and 95th percentiles as shaded regions. The PSDs from headset M32-A were excluded from visualization because they contain a prominent peak at 30 Hz, not representative of the PSDs from headsets M32-B, M32-C, and M32-D. (C) Distribution of gender and condition information for the PDSs grouped in each cluster. *Indicates most aesthetically pleasing and **indicates most emotionally stimulating as reported in the questionnaire.
Figure 6
Figure 6
Gaussian kernel (σ = 20) k-means results for the parietal electrode O1. (A) Three-dimensional visualization of the final clusters from kernel k-means. Each point in the scatter plot corresponds to the total normalized power (area under the PSD) in the delta, alpha, and gamma bands for a single 4 s window. (B) The pie charts show the contribution of each headset type to the PSD clusters. To the right, the last pie chart shows the overall distribution of the PSDs for each headset type. (C) The mean of the PSDs for each headset type is shown below each cluster's pie chart, along with the 5th and 95th percentiles as shaded regions. The PSDs from headset M32-A were excluded from visualization because they contain a prominent peak at 30 Hz, not representative of the PSDs from headsets M32-B, M32-C and M32-D. (C) Distribution of gender and condition information for the PDSs grouped in each cluster. *Indicates most aesthetically pleasing and **indicates most emotionally stimulating as reported in the questionnaire.
Figure 7
Figure 7
Comparison of PSDs from two BPG-majority clusters. Each row shows the median with the 20th and 80th percentiles of the PSDs from BPG grouped together in the BPG-largest clusters. (Left) PSDs from BPG in cluster 1. The inset shows the proportion of Baseline (B) and Piece-viewing (PV) PSDs in that cluster. An asterisk (*) indicates that there is a statistically significant difference with a confidence level of 99% between the cluster proportion of B vs. PV and the total sample proportion B (0.21) vs. PV (0.79) collected for BPG in the experiment. (Right) PSDs from BPG in cluster 2.

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