Elsevier

NeuroImage

Volume 90, 15 April 2014, Pages 140-145
NeuroImage

Ultra-dense EEG sampling results in two-fold increase of functional brain information

https://doi.org/10.1016/j.neuroimage.2013.12.041Get rights and content

Highlights

  • Ultra-dense EEG array reveals surprisingly strong potential variation at 1 cm scale.

  • ud-EEG provides twice SNR for brain-response classification than high-density EEG.

  • Paradigm shift: ultra-dense sampling is required to collect all functional EEG.

  • ud-EEG systems (700–800 sensors) can significantly impact neuroscience research.

Abstract

Contemporary high-density electroencephalographic systems (hd-EEG) comprising up to 256 electrodes have inter-electrode separations of 2–4 cm. Because electric currents of the brain are believed to strongly diffuse before reaching the scalp surface, higher-density electrode coverage is often deemed unnecessary. We used an ultra-dense electroencephalography (ud-EEG) sensor array to reveal strong potential variation at 1 cm scale and discovered that it reflects functional brain activity. A new classification paradigm demonstrates that ud-EEG provides twice the signal to noise ratio for brain-response classification compared with contemporary hd-EEG. These results suggest a paradigm shift from current thinking by showing that higher spatial resolution sampling of EEG is required and leads to increased functional brain information that is useful for diverse neurological applications.

Introduction

The earliest EEG recordings detected electric activity of the whole brain via a pair of sensors positioned across the head (Pravdich-Neminsky, 1913, Berger, 1929). It was soon discovered that EEG varied substantially over the scalp (Adrian, 1934, Adrian, 1935), which instigated simultaneous recordings from several EEG sensors and resulted in the standardized “10–20” electrode placement system of 21 scalp electrodes (electrode separation > 6 cm) adopted half a century ago (Jasper, 1958). Although this system remains in wide use among clinicians, high-density (64–256 sensors, electrode separation > 2 cm) EEG systems, which appeared two decades ago (Gevins, 1990, Tucker, 1993) quickly became a popular choice for EEG scientists and have since made their way into some clinics. Given the advances in amplifier miniaturization and wireless data transmission, EEG systems with up to 1000 sensors are well within reach for modern technology. The increased spatial information could significantly impact basic neuroscience research as well as a broad spectrum of practical applications from brain–computer interfaces to localization of epileptic sources. Hence, the important question: how many sensors are sufficient to capture all information on brain activity provided by EEG?

Theoretical and experimental analyses have shown that at least 128 EEG scalp channels are necessary to meet the Nyquist requirement for spatial sampling (Srinivasan, 1998, Spitzer, 1989), but the upper limit remains unknown due to our incomplete knowledge of the electric properties of head tissues and difficulties in accurately modeling real heads. Computational modeling using simplistic head models (Ryynänen, 2004, Ryynänen, 2006, Malmivuo, 2011) and some preeminent EEG methods books (Srinivasan, 2005, Nunez, 2006) suggest that electrodes spaced by 2–3 cm, characteristic of hd-EEG sensor arrays, can provide sufficiently dense EEG sampling, implying that further resolution is unnecessary. A more realistic head modeling based on the finite elements method (FEM) applied to MRI images indicated that a much smaller (~ 0.3 cm) electrode spacing is needed for extraction of cortical patterns from scalp EEGs in humans (Ramon, 2009). This modeling result agrees with experimental measurements demonstrating that electrode spacing of 0.5–1 cm is required to capture spatial EEG pattern without undersampling (Freeman, 2003, Odabaee, 2013). Although these experiments demonstrated that human EEG contains significant high spatial frequencies, it remains unknown how much of the extra information captured at these frequencies is of practical significance, e.g., whether it provides more accurate EEG source localization or improves discrimination between brain states. Here, we use a classification paradigm to show that sampling EEG signals at high spatial frequencies significantly increases classification accuracy derived from EEG responses evoked by two visual stimuli. This demonstrates that ultra-dense (1 cm or less) electrode spacing is needed to fully capture functional brain information from EEG.

Section snippets

Paradigm

Arguably, the best way to determine the sufficient number of EEG sensors is to measure the amount of functional brain information I contained in EEG data as a function of the number of sensors, I(n). For some value of n the function might saturate sufficiently to deem further increase of the number of sensors unnecessary. However, covering the whole scalp with hundreds of sensors is a challenging technical task. Similarly to previous studies (Freeman, 2003, Odabaee, 2013) the compromise we

Results

We used a prototype ud-EEG array: a square 4 × 4 grid of small diameter EEG electrodes with inter-electrode separation of 1 cm (Fig. 1a, Section 2.4). A signal classification paradigm was used to demonstrate that the ud-EEG array provides additional information about brain activity compared to hd-EEG. In this paradigm, eleven subjects viewed images of words presented one by one on a computer monitor (Stimuli section). On each trial, a word appeared on the screen for half a second followed by a

Discussion

The importance of a higher spatial resolution is becoming increasingly appreciated. Our results are consistent with a recent study, where a linear ultra-dense array (2.5 mm inter-electrode spacing) was used to demonstrate that sensor distances 6–10 mm are required to capture the full spatial texture of EEG signal on neonatal scalp (Odabaee, 2013). While one could argue that large fontanels over which the linear array was positioned could cause the increased spatial variation of EEG for neonatals,

Summary

To summarize, we observed surprisingly strong variations of VEPs at 1 cm scale. This result explains a nearly two-fold increase in the classification SNR between the most commonly used hd-EEG sensor densities (~ 0.1 cm 2, see the Comparison with existing hd-EEG systems section) and the density of sensors in our prototype ud-EEG array (1 cm 2). Given the advances in amplifier miniaturization and wireless data transmission, ud-EEG helmets with 700–800 sensors needed for full scalp coverage at 1 cm 2

Acknowledgments

This work was supported by NSF-IIP-1264216, NSF-DGE-0965843 and a Northeastern University Tier1 award grants.

Conflict of interest

The authors declare that they have no competing financial interests.

References (25)

  • H. Berger

    Über das Elektrenkephalogramm des Menschen

    Arch. Psychiatr. Nervenkr.

    (1929)
  • A. a Gevins

    Beyond topographic mapping: towards functional–anatomical imaging with 124-channel EEGs and 3-D MRIs

    Brain Topogr.

    (1990)
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