Shimura et al.
Journal of Physiological Anthropology(2023) 42:24
https://doi.org/10.1186/s40101-023-00342-3

Journal of
Physiological Anthropology

Open Access

SHORT REPORT

Multipoint surface electromyographymeasurement using bull’s‑eye electrodes
for wide‑area topographic analysis
Megumi Shimura1* , Akihiko Mizumoto1, Yali Xia2 and Yoshihiro Shimomura2

Abstract
Background Surface electromyography (sEMG) is primarily used to analyze individual and neighboring muscle activity. However, using a broader approach can enable simultaneous measurement of multiple muscles, which is essential
for understanding muscular coordination. Using the “bull’s-eye electrode,” which allows bipolar derivation without directional dependence, enables wide-area multipoint sEMG measurements. This study aims to establish a multipoint measurement system and demonstrate its effectiveness and evaluates forearm fatigue and created topographic
maps during a grasping task.
Methods Nine healthy adults with no recent arm injuries or illnesses participated in this study. They performed
grasping tasks using their dominant hand, while bull’s-eye electrodes recorded their muscle activity. To validate
the effectiveness of the system, we calculated the root mean squares of muscle activity and entropy, an indicatorof muscle activity distribution, and compared them over time.
Results The entropy analysis demonstrated a significant time-course effect with increased entropy over time,suggesting increased forearm muscle uniformity, which is possibly indicative of fatigue. Topographic maps visuallydisplayed muscle activity, revealing notable intersubject variations.
Discussion Bull’s-eye electrodes facilitated the capture of nine homogeneous muscle activity points, enabling
the creation of topographic images. The entropy increased progressively, suggesting an adaptive muscle coordination
response to fatigue. Despite some limitations, such as inadequate measurement of the forearm muscles’ belly, the system is an unconventional measurement method.
Conclusion This study established a robust system for wide-area multipoint sEMG measurements using a bull’seye electrode setup. This system effectively evaluates muscle fatigue and provides a comprehensive topographic
view of muscle activity. These results mark a significant step towards developing a future multichannel sEMG systemwith enhanced measurement points and improved wearability.
Trial registration This study was approved by the Ethics Committee of Chiba University Graduate School of Engineering (acceptance number: R4-12, Acceptance date: November 04, 2022).
Keywords Multipoint surface electromyography, Entropy, Muscle groups, Measurement method*Correspondence:
Megumi Shimuram-shimura@chiba-u.jp
Full list of author information is available at the end of the article© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
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Shimura et al. Journal of Physiological Anthropology(2023) 42:24

Background
Surface electromyography (sEMG) is a widely used noninvasive method for measuring the electrical activity ofskeletal muscles, enabling muscle activity and fatigue
assessment [1]. Despite various analysis techniques[2, 3], the conventional sEMG methodology remains
unchanged. The two prevailing approaches include usinga pair of electrodes on a specific muscle [4, 5] or employing a dense grid of electrodes arranged in a small rectangular layout [6–8]. However, these methods focusedon individual or neighboring muscles and neglected theimportance of simultaneously measuring multiple muscles, which is critical for understanding muscular coordination in body control [9].
Multipoint sEMG measures muscle activity throughthe unipolar derivation of each electrode [10] or bipolar
derivation of adjacent electrodes [11, 12]. However, bothmethods encounter challenges when attempting a comprehensive measurement. Unipolar derivation, common
in electroencephalogram (EEG) measurements [13, 14],
faces issues with sEMG owing to motion-induced electrical potential changes and uncertain indifferent electrodelocations, making it susceptible to spatial noise [15]. Even
under noise-free conditions, forming equipotential lines
within the measurement area is not feasible, and the lowpass filter effect of biological tissue is amplified by theelectrode’s distance from the indifferent electrode, leading to varying frequency components requiring carefulpotential comparison.
In contrast, conventional bipolar derivation requires
the alignment of the two electrodes in the myofiber
direction, which becomes problematic at muscle boundaries or when assessing muscles with differing myofiber
directions [16]. To address this, the bull’s-eye electrode[17, 18], designed for anisotropy-free bipolar derivation,
employs a central pin electrode and a concentric ring
electrode configuration. This design eliminates the needfor electrode-muscle fiber alignment, conductive paste,or gel, making it suitable for wide-area multipoint sEMG
measurements.
This study aimed to establish a multipoint measurement system without directionally dependent measurement points. Muscle fatigue was assessed topographicallyusing grasping tasks involving multiple muscles, and theability to detect significant changes in integrated indicesin a time series was examined to demonstrate the effectiveness of the system.

Page 2 of 6participated (5 males and 4 females, aged 24 ± 2 years, bodyweight 60 ± 8 kg, height 167 ± 9 cm, and grip strength of
dominant hand 271 ± 53 N). All the participants were righthanded according to the Edinburgh Handedness Test.
Tasks

The participants sat on a chair with their dominant upper
limb hanging by their sides, the elbow joint flexed at 90°,
and the forearm resting on a desk. The forearm was ina neutral pronation, supination, flexion, and extension
position, with the palm oriented perpendicular to thedesk (Fig. 1). A pulley system was used with a handle
attached to one end of the string and a weight equal to
10% of the subject’s body weight on the other, providing a
constant external force. Subjects were instructed to grasp
the handle with their dominant hand, maintain a constant weight height while keeping their forearm muscles
engaged in a neutral position, and avoid lifting their arms
off the desk. Three repetitions of the task were performed
with rest periods between. Subsequently, the subjects
exerted a maximum grip force for 5 s in the same posture,and this measurement was taken twice.
Measurements

Bull’s-eye electrodes, which were made by the author,
were used in the experiment. These electrodes consistedof two brass rings: one large (20 mm diameter and 15 mm
inner diameter) and one small (7 mm diameter and 3 mm
inner diameter), with a plate thickness of 0.5 mm. Ninebull’s-eye electrodes were evenly spaced on the subject’s
forearm based on the forearm length and wrist circumference (Fig. 2). Before electrode placement, the skin was

Methods
Subjects

Nine healthy adults who did not sustain any injuries
or illnesses in their dominant arm in the last 6 months

Fig. 1 Subject’s posture and experimental apparatus

Shimura et al. Journal of Physiological Anthropology(2023) 42:24

Page 3 of 6

Fig. 2 Topographic map and electrodes position. Nine bull’s-eye electrodes were placed on the right forearm in a three-by-three grid basedon the length of the forearm (distance from the head to the styloid process of the radius). An Ag/AgCl ground electrode was placed on the ulnar
styloid process. The %RVC was used to create topographic maps via interpolation to estimate the values between electrodes. The positionof electrode 1 corresponds to the lower left coordinate (0,0) on the map

cleaned, and a conductive paste was applied. The potentials obtained from the electrodes were processed througha buffer amplifier, sampled at 2 kHz, amplified 2000 times,
and filtered through a 5–500-Hz bandpass filter (MP150,
BIOPAC System Inc.).
Data analysis

The root mean square (RMS) was calculated every 0.5 s foreach channel and during the central 3 s of maximum grip
force. The average of two measurements was used as thereference voluntary contraction (RVC) [19]. The RMS values during the tasks were divided by the RVC to calculate
the %RVC, which was used to generate topographic maps
(Fig. 2) using MATLAB (MathWorks Inc.).

Fig. 3 Entropy results. Shown as mean and standard deviation

Entropy analysis was used to evaluate fatigue in muscle
groups. Entropy measures the uncertainty or randomness
associated with signals or data sources and is widely used
in information theory [20]. In a previous study, entropy
was used to study changes in trapezius muscle activity during fatigue tasks [8]. Higher entropy values indicate a more uniform distribution of muscle activity andchanges in entropy are expected to occur with muscle
group fatigue owing to alterations in active alternations
and patterns. Entropy was calculated for each channel
every 0.5 s as follows:9p2 (i)log2 p2 (i)Entr = −i=1

Shimura et al. Journal of Physiological Anthropology(2023) 42:24

RMS i
p2 (i) = 9
k=1 RMS k
The probability, p2(i), which indicates the activity rateof one point relative to the total area of measurement,
was obtained by dividing the RMS of electrode i by thesum of nine RMS values. To normalize the entropy, theaverage of each subject’s task was calculated and subtracted. The task segment was divided into six equal segments of 30 s each, and the average of each segment wascalculated.
To analyze entropy, one-way repeated measures
ANOVA and a multiple comparison test (Tukey–Kramer
method) were performed, with the time course as aPage 4 of 6

factor. The significance level was set to 5%. Eight participants were included in the statistical analyses. One
subject was excluded from the analysis because someelectrodes floated off the skin during measurement,
resulting in missing data.

Results
The results of the entropy analysis revealed a significant
main effect for the time-course factor (F (5, 40) = 5.15,
p < 0.001), with significant increases in the 90–120 s(p = 0.027), 120–150 s (p = 0.002), and 150–180 s(p = 0.001) segments compared to the 1–30 s segment.
Additionally, there was a significant increase in the 120–
150 s (p = 0.002) and 150–180 s (p = 0.001) segments

Fig. 4 %RVC topographical maps. Topographic maps for eight subjects at 10, 60, 110, and 160 s after the start of the first trial are shown. The
orientation of all topographic maps is the same as shown in Fig. 2

Shimura et al. Journal of Physiological Anthropology(2023) 42:24(Fig. 3). Topographic maps of the eight subjects areshown in Fig. 4.

Discussion
Bull’s-eye electrodes allowed for the measurement of nine
homogeneous points, generating topographical images.
This allowed the visualization of muscle activity and variations in activity. In this study, the electrode placement
was defined based on two points on the radius. This
ensured within-subject reproducibility of the measurement points. In addition, because the muscles were not
located by palpation, the electrode application time was
reduced compared to the standard bipolar recording
method.
This study applied external force based on body weightrather than the maximum voluntary contraction (MVC)
to enable the subjects to start the task without fatigue.
In addition, grip strength and body weight are correlated
in healthy young adults [21]. The external force was converted to %MVC, resulting in an average of 22.3 ± 4.8%
MVC. Previous studies have revealed signs of fatigue inthe extensors and flexors within 10 and 30 s, respectively,
at 25% MVC [22], suggesting significant fatigue after
180 s of grasping.
Entropy exhibited a progressive increase over time,
indicating enhanced forearm muscle activity uniformity
at 150 s compared to that at the beginning of the task,
potentially due to muscle fatigue. When muscle fatigue
occurs, central fatigue reduces the firing frequency of the
motor units, leading to alternating recruitment to compensate for the loss of force production [23]. This adaptive mechanism maintains performance by resting the
motor units while activating others [24]. Such adaptations also occur at the muscle group level, where changes
in muscle coordination (number and composition ofactive muscles) serve as strategies to counteract fatigue[25]. Turpin et al. reported that, when fatigue occurs
during a cyclic task, flexible combinations of several
muscle synergy groups maintain muscle activity levels[25]. In this study, although a simple static task, multiple
muscle synergy groups in the forearm attempted to fire
and maintain performance, which likely resulted in an
increase in muscle activity uniformity in the latter halfof the task. Additionally, Fig. 4 displays intersubject differences in muscle activity areas, implying that even insimple tasks, individual variations in fatigue coping strategies can be observed through extensive topographicaldescriptions.
A limitation of this study was the inadequate measurement of the belly of the forearm muscles because
the electrodes were placed in 25–75% of the forearm.
Challenges in stably placing the device near the elbow

Page 5 of 6

joint due to uneven muscle belly and elbow fossa led tonoise generation. Future improvements to the device
will enable measurements closer to the elbow joint.
The positional relationship between the electrodes andmuscles will be further investigated to ensure stablemeasurements.

Conclusion
This study successfully established a system for measuring a wide range of multipoint sEMG using bull’s-eye
electrodes, enabling bipolar derivation without directional dependence, and reducing the burden of electrodepositioning. The system demonstrated its capability toevaluate fatigue using entropy and provided extensive
topographical descriptions of muscle activity across awide range. In the future, we aim to develop a multichannel sEMG system with more measurement points andimproved wearability to assess broader areas of muscle
activity.
Abbreviations
EEG	Electroencephalogram
RMS	Root mean square
RVC	Reference voluntary contractionsEMG	Surface electromyography
MVC	Maximum voluntary contraction
Acknowledgements
The authors thank Kazuhiro Yoshida, Kenichi Mori, and Yuji Furuta (SHINKO
ELECTRIC INDUSTRIES Co., Ltd.) for their technical assistance with the experiments and for sharing their expertise with us.
Authors’ contributions
MS performed and analyzed the experiments and wrote the manuscript. AM,
YX, and YS conceived of and drafted the manuscript. All the authors have readand approved the final version of this manuscript.
Authors’ information
YS is an executive of the Japan Society of Physiological Anthropology. YX is a
project researcher at Chiba University. AM is a student at Chiba University in
Chiba, Japan.
Funding
This study was supported by the JST SPRING (grant number: JPMJSP2109).
Availability of data and materials
The datasets analyzed in the current study are available from the corresponding author upon request.

Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of Chiba University Graduate School of Engineering (acceptance number: R4-12).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.

Shimura et al. Journal of Physiological Anthropology(2023) 42:24

Page 6 of 6

Author details
1
Graduate School of Science and Engineering, Chiba University, 1‑33
Yayoi‑Cho, Inage‑Ku, Chiba City 2638522, Japan. 2 Design Research Institute,
Chiba University, 1‑33 Yayoi‑Cho, Inage‑Ku, Chiba City 2638522, Japan.

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Received: 28 March 2023 Accepted: 16 October 2023

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