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ARTICLE OPEN
Wearable EEG electronics for a Brain–AI Closed-Loop
System to enhance autonomous machine decision-making
Joo Hwan Shin
1
, Junmo Kwon
2
, Jong Uk Kim
1
, Hyewon Ryu
1
, Jehyung Ok
1
, S. Joon Kwon
1,3
, Hyunjin Park
2,4
and Tae-il Kim
1,5
✉
Human nonverbal communication tools are very ambiguous and difficult to transfer to machines or artificial intelligence (AI). If the
AI understands the mental state behind a user’s decision, it can learn more appropriate decisions even in unclear situations. We
introduce the Brain–AI Closed-Loop System (BACLoS), a wireless interaction platform that enables human brain wave analysis and
transfers results to AI to verify and enhance AI decision-making. We developed a wireless earbud-like electroencephalography (EEG)
measurement device, combined with tattoo-like electrodes and connectors, which enables continuous recording of high-quality
EEG signals, especially the error-related potential (ErrP). The sensor measures the ErrP signals, which reflects the human cognitive
consequences of an unpredicted machine response. The AI corrects or reinforces decisions depending on the presence or absence
of the ErrP signals, which is determined by deep learning classification of the received EEG data. We demonstrate the BACLoS for AI-
based machines, including autonomous driving vehicles, maze solvers, and assistant interfaces.
npj Flexible Electronics (2022) 6:32 ; https://doi.org/10.1038/s41528-022-00164-w
INTRODUCTION
The recent development of bio-integrated technology based on
wearable and implantable electronics has made it possible to
continuously measure bioinformation, such as physiological,
electrophysiological, and chemical signals
1–5
. Such devices can
be utilized in a human–machine interface (HMI) integrated with
advanced Internet of Things (IoT) and artificial intelligence (AI)
technologies
6–13
. In particular, the brain–machine interface (BMI),
which enables interaction between the brain and machines
14–16
,
can be established using electrophysiological sensors, such as
brain-surface
17
, brain-penetrating
18,19
, and skin-surface electro-
des
20,21
, to assess electrocorticography (ECoG), local field poten-
tials (LFPs), and electroencephalography (EEG), respectively. Even
with heavy signal processing, the signals from the brain are
usually converted into simplified and unreciprocated orders for
the machine and involve uncomfortable use (for instance, by
watching flickering displays to guide the direction of a wheel-
chair)
20–22
.
An ideal approach for expanding these interactions is the basis
for Steven Spielberg’sfilm A.I. Artificial Intelligence (2001), which
features a robot that attempts to understand human emotions
and independently revise/reinforce behavior with bidirectional
interactions
23
. Although such interaction has not yet been
realized, basic collaborations with machines have recently been
suggested
24–32
. The AI recognizes error-related potential (ErrP),
one of the human brain wave patterns, and is able to accelerate
the reinforcement of its decision to increase yield of the decision-
making. The ErrP is an event-related potential as a result of an
error stimulus. It is a complex pattern consisting of error-related
negativity (ERN), N200/feedback-related negativity (FRN), and
P300/Error positivity (Pe)
33
. The systems that continuously
reinforce the functions of AI by accepting the ErrP as human
guidance is very convenient because it does not require conscious
effort compared to the method that uses hands, voice, and facial
expressions
34–36
. This approach is inevitable in the current era, in
which AI-powered machines are developing rapidly, replacing
human decision-making, and required continuous feedback
37,38
.
However, current technologies are being researched and applied
only under very limited conditions. In most studies, (1) wet
electrolyte-based electrodes that dry over time are used, the
impedance changes over time, making it difficult for long-term
measurement
24–32
; (2) using multiple electrodes and fixing them
in the form of a helmet or headset causes inconvenience to users
and generates noise due to its heavyweight
24–32
; (3) EEG
measurement device does not support wireless communication,
noise is generated from the connector, and it cannot be moved
away from the data collection device or target agent
24,25,27–29,31,32
;
(4) accordingly, unrealistic research is conducted only with a
monitor, not with autonomous machines
24,25,28–30
; (5) the only
theoretical studies are conducted without closed-loop demonstra-
tion that enables continuous learning by the ErrP feedback
24,25,28
(Supplementary Table 1). For a practical and convenient BMI
system, it will be necessary to conduct integration that considers
electrodes, connectors, and wireless system as well as optimized
geometries for fixation method and even the closed-loop
configurations
39
.
Thus, we introduce a wireless, neuroadaptable (adaptable in
relation to the subject’s mindset), intuitive Brain–AI Closed-Loop
System (BACLoS) based on an earbud-like wireless EEG device (e-
EEGd) composed of tattoo-like electrodes, connectors, and a
wireless EEG earbud. The neuroadaptable BACLoS can revise and
reinforce the autonomous decision of the AI-based machine based
on the error-related potential (ErrP) in the volunteer’s brain
signals, which results from undesired AI decisions
33,40
. In this
study, (1) tattoo-like dry electrodes without wet electrolyte,
making it suitable for long-term and high-quality EEG measure-
ments; (2) 8.24 g of single-channel e-EEGd conformally adheres to
the skin on the head and does not cause discomfort due to the
1
School of Chemical Engineering, Sungkyunkwan University (SKKU), Suwon 16419, Republic of Korea.
2
Department of Electrical and Computer Engineering, Sungkyunkwan
University (SKKU), Suwon 16419, Korea.
3
SKKU Institute of Energy Science and Technology (SIEST), Sungkyunkwan University (SKKU), Suwon 16419, Korea.
4
Center for
Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Korea.
5
Biomedical Institue for Convergence (BICS) SKKU, Suwon 16419, Korea.
✉email: taeilkim@skku.edu
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fixation method; (3) as e-EEGd is supported by Bluetooth Low
Energy (BLE) communication, it can achieve high-quality EEG
signal from the target agent with minimized artifacts. The
conformal tattoo-like electrodes, unswaying tattoo-like connec-
tors, light-weight earbud devices, and engineering to successfully
connect and integrate them are the reason of e-EEGd generates
8.5-times less artifactual noise than a commercial device. The
e-EEGd can continuously record EEG signals, even while the
volunteer is walking, driving, or engaged in other activities, which
expands the variety of situations in which the BACLoS can be
used; (4) accordingly, it was possible to conduct an ErrP study for
autonomous driving remote-controlled (RC) cars in a situation
where volunteers were not restricted. As a result, we identified the
ErrP by unexpected autonomous machine behaviors in the
transmitted EEG signals from the e-EEGd. We also conducted a
signal processing and classifier optimization study to enable
reasonable and fast real-time classification to be performed on AI-
based machines; (5) finally, we built trained classifier into the RC
car to demonstrate that the RC car automatically corrects or
reinforces its decisions within a closed-loop.
RESULTS
Concept of the BACLoS
Figure 1a shows an overview of the BACLoS in which humans and
AI interact in a closed loop. In the BACLoS, the human subject
observes the AI’s autonomous decision-making (①in Fig. 1a)
without any behavioral restrictions. Human satisfaction (yes) or
dissatisfaction (no) concerning the AI’s previous decision (②in Fig.
1a) is reflected in the EEG as ErrP (③in Fig. 1a), which are
automatically transmitted to the AI in real time. Depending on the
deep learning classification of the EEG, the AI corrects or reinforces
the existing decision-making process (④in Fig. 1a). As this closed-
loop iterates, the AI is increasingly able to make accurate decisions
without ErrP feedback from humans.
The e-EEGd measures the EEG through a single channel
consisting of working, ground, and reference electrodes attached
to the forehead, temple, and mastoid, respectively (Fig. 1b). The
frontal lobe area where the working electrode is located avoids
the hairy site and can be used practically, and it is known that
P300 and ErrP patterns can be measured
33
. Tattoo-like electrodes
(900 nm) and connectors (2.8 μm) are conformally attached to the
skin, and their ultrathin geometries and method of attachment
minimize mechanical stress caused by skin deformation and
motion artifacts (Supplementary Fig. 1). An optimized design that
gradually increases in thickness from thin tattoo electrodes to
thick earbuds allows thin and soft electrodes to be connected to
thick and rigid electronics with minimal mechanical stress
(Supplementary Figs. 2 and 3). The wireless EEG earbud is
encapsulated in 3D-printed plastic, in which electronic compo-
nents and batteries are mounted around a miniature (12.5 ×
34.5 mm), multilayered printed circuit board (Fig. 1c and
Supplementary Fig. 4). The e-EEGd is lightweight (8.24 g)
compared with other miniaturized single-channel EEG devices
(Supplementary Tables 2 and 3) and tightly fixed in the ear so that
motion artifacts are reduced and the user is comfortable when the
EEG is measured. The e-EEGd is time synchronized with the AI via
wireless communication, which is essential for time-domain
analysis of ErrP signals and deep learning classification. A
flowchart for the closed loop, which improves the decision-
making system between humans and AI, is shown in Fig. 1d. The
AI automatically generates new output using an independent
decision-making process based on input that is perceived from
the environment (Supplementary Table 4). The human compares
the output created by the AI with the input recognized from the
environment. Depending on whether the AI output is unexpected
or expected, the ErrP signals will (red EEG record in Fig. 1d) or will
not (blue EEG record in Fig. 1d) be present. EEG signals are
wirelessly transmitted to the AI and classified using deep learning.
Based on these results, an emergency interrupting system or a
user-customized reinforcement system is activated. This config-
uration is repeated in a closed loop, and humans can use the
BACLoS naturally by wearing the e-EEGd and observing the AI
output (Supplementary Fig. 5).
Quantitative comparison of the motion artifacts
The tattoo-like electrode has a low bending stiffness similar to
human skin and can effectively make conformal contact with
rough and uneven human skin
41,42
. This conformal contact
minimizes the gaps between the skin and the electrodes, which
allows the tattoo-like electrode to have low electrode–skin
impedance comparable to the commercial electrode without
using wet electrolytes. In addition, it is very effective in reducing
motion artifacts to minimize the inconsistent gaps that are
induced by human motion or external vibration
41–44
. Not only the
electrode but also the connector is another reason for the motion
artifacts. The main problem with unfixed and dangling connectors
is that they generate indirect noise through random swinging
motions across different frequency bands of vibration
45–47
. The
tattoo-like connectors are very light and securely fixed to the skin,
do not vibrate, and have a negligible effect on vibration,
considerably reducing noise. However, the most critical issue for
using tattoo-like bioelectronics is thickness mismatch between
extremely-thin components and a thick and heavy measurement
system. There is a risk of adhesion failure due to stress on the
connection interface. To solve the problem, we increased the
thickness in several steps from the tattoo-like electrode (900 nm),
tattoo-like connector (2.8 μm), anisotropic conductive film (ACF)
cable (12 μm), ACF contact pad of flexible printed circuit (FPC)
connecter (80 μm), to SMD contact pad of FPC connecter (295 μm).
As a result, it was possible to successfully maintain adhesion and
reduce overall motion artifacts while connecting 900 nm electro-
des to a 1 cm earbud.
Figure 2a shows two wireless EEG measurement systems for
comparing motion artifacts. The tattoo-like sensor-based EEG
device (e-EEGd) can be seen on the left side of the subject and a
commercial sensor-based EEG device (c-EEGd) on the right. To
highlight the difference from the electrodes (tattoo-like vs. wet
commercial) and connectors (tattoo-like vs. swaying commercial),
the measurement device of c-EEGd uses a module based on the
RHD2216 amplifier chip same as the e-EEGd. Figure 2b shows EEG
measurement while walking; significant EEG signal fluctuation can
be seen in the c-EEGd (bottom of Fig. 2b) compared with that in
the e-EEGd (top of Fig. 2b). The average root mean square (RMS)
values of the EEG signals with the volunteer sitting, shown in Fig.
2c, were 11.16 μV and 11.28 μV for e-EEGd and c-EEGd,
respectively. However, when the subject walked around, the
average RMS values were 18.57 μV and 45.49 μV for e-EEGd and c-
EEGd, respectively.
To verify the motion artifacts of the electrodes and connectors,
the EEG was measured when the volunteer walked at a pace of
2–3 steps per second using different combinations of electrodes
and connectors (Supplementary Fig. 6). Figure 2e and f show the
results of a fast Fourier transform (FFT) analysis in the 0–2 Hz and
2–4 Hz frequency bands, respectively. When the long, dangling
connectors were replaced with a tattoo type connector (red) with
commercial electrodes, the noise in the 0–2 Hz band was reduced,
but noise in the 2–4 Hz band was not. On the other hand, when all
the connectors and electrodes were changed to the tattoo-like
connectors and electrodes (purple), the noise in the 2–4 Hz band
was significantly reduced. Taken together, these results indicate
that the tattoo-like electrodes were effective in reducing direct
noise from motion artifacts (2–4 Hz) and that the tattoo-like
connector was effective in reducing indirect noise (0–2 Hz). We
J.H. Shin et al.
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Fig. 1 Concept of the Brain-AI Closed-Loop System (BACLoS) and images of wearable electroencephalography (EEG) devices composed
of tattoo-like electronics and a wireless EEG earbud device (e-EEGd). a Conceptual illustration of the BACLoS. Humans and artificial
intelligence (AI) interact through an EEG measured by the e-EEGd, forming a wireless closed loop. Erroneous AI decisions result in error-related
potential (ErrP) in the EEG signals. The AI recognizes the ErrP feedback through deep learning and corrects/reinforces the next decision.
Compared to the unidirectional commands of conventional machines, this machine can automatically learn appropriate decisions and revise
inaccurate decisions using the EEG signals. bPhoto of a volunteer who has the e-EEGd (left), i.e., a wireless EEG measurement earbud and
three open-mesh, structured, tattoo-like electrodes and connectors attached on recording sites. Images (right) show recording (top), ground
(middle), and reference (bottom) electrodes mounted on the mastoid, temple, and forehead of the volunteer, respectively. cZoomed-in
illustration of the e-EEGd. dFlowchart of the BACLoS implemented in the closed-loop feedback algorithm based on ErrP signals achieved by
the e-EEGd for reinforcement and correction of the machine’s autonomous decision-making. The e-EEGd only transmits the EEG wirelessly and
does not process classification or filtering. Receiving EEG, performing preprocessing, and checking the existence of ERP patterns through deep
learning are all done on the AI side.
J.H. Shin et al.
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Published in partnership with Nanjing Tech University npj Flexible Electronics (2022) 32
demonstrated the e-EEGd, with a short length of tightly fixed
connector incorporated with tattoo-like electrodes on the skin,
reduced direct (2–4 Hz) and indirect noise (0–2 Hz) and motion
artifacts (Supplementary Video 1).
The noise-reduction effect of the e-EEGd allowed EEG
measurement in noisy outdoor environments while a participant
was walking, riding a bicycle, riding a scooter, and driving a car
(Fig. 2g). Figure 2h shows the EEG results during outdoor activities
measured by e-EEGd (colored) and c-EEGd (gray). The EEG
fluctuations were very severe with the c-EEGd, whereas negligible
EEG amplitudes were associated with the e-EEGd. The average
RMS value of the continuous EEG across the four activities was
23.10 μV and 124.23 μV for the e-EEGd and c-EEGd, respectively
(Supplementary Fig. 7). In addition, it was possible to measure
alpha waves in the noisy walking condition (Supplementary Fig. 8).
We also investigated ocular artifacts caused by eye movement.
Electrooculogram (EOG) caused by eyelids close and open can act
as artifacts and make EEG analysis difficult. We confirmed that
artifacts due to eye blinking were observed in both e-EEGd and
c-EEGd. The blinking artifacts follow a specific pattern that rises
Fig. 2 Comparison of motion artifacts and signal qualities with two wireless electroencephalography (EEG) devices. a A photo of a subject
wearing both wireless EEG devices. The left portion of the image shows the wireless, earbud-like EEG device (e-EEGd) with tattoo-like
electrodes and connectors, and the right portion of the image shows a conventional wireless EEG device with commercial electrodes and
connectors (c-EEGd). b60 overlaid EEG recordings for 0.75 s while the subject was walking on the floor from the e-EEGd (top) and c-EEGd
(bottom). cRoot mean square values of the recorded EEG signals from both devices while sitting (left) and walking (right). dSkin–electrode
impedance of the tattoo-like electrode in the e-EEGd and commercial electrode in the c-EEGd. e,fFast Fourier transform (FFT) spectra of
recorded EEG signals during walking with different electrode and connector combinations; commercial connector with commercial electrode
(top) used in c-EEGd, tattoo-like connector with commercial electrode (middle), and both tattoo-like connector and electrode (bottom) used in
the e-EEGd. The subject walked 2–3 steps per second (~2 Hz). eFFT data between 0 and 2 Hz. fFFT data between 2 and 4 Hz. g,hEEG signal
and artifact comparisons between the e-EEGd and c-EEGd during walking, riding a bike, riding a scooter, and driving a car in daily-life
conditions.
J.H. Shin et al.
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rapidly according to eyes close, descends according to eyes open,
and then returns to the origin. This pattern is totally different with
the P300 and ErrP patterns to be measured later in shape/size, so
incorrect interpretation can be avoided (Supplementary Fig. 9a, b).
We also tested artifacts caused by gaze when observing the
driving RC car along a circular track (Supplementary Fig. 9c). As a
result, any significant artifacts were not observed in both e-EEGd
and c-EEGd, comparable to measured baseline signal in sitting/
inactive subjects (Supplementary Fig. 9d, e, ‘sit’of Fig. 2c). These
results became the basis for us to conduct research on ErrP using
e-EEGd and autonomous driving RC car.
P300 event-related potential and error-related potential (ErrP)
With the e-EEGd, we obtained a high-SNR EEG signal with less
noise from motion artifacts. To confirm whether such e-EEGd can
be utilized for ErrP research, we measured P300 (related to Pe), the
largest and most well-known of the ErrP complexes. We measured
the ERP when subjects (n=10, Supplementary Table 5) partici-
pated in three auditory ‘oddball’tests with single-channel tattoo-
like electrodes (Fig. 3a and Supplementary Video 2). The auditory
oddball tests randomly presented repeated sounds and rarely
presented the unusual sounds. The expected stimuli were a low-
pitch tone (red) and a human voice saying ‘no’(green and blue).
The unexpected stimuli consisted of a high-pitch tone (black) and
a human voice saying ‘yes’(gray and white). The P300 peak was
observed for all subjects approximately 300 ms after the
unexpected sound (Supplementary Fig. 10), with an average
P300 peak at 392, 414, and 371 ms in tests 1, 2, and 3, respectively
(Fig. 3b and Supplementary Table 6). The difference in the average
EEG results measured by repeated trials in several participants
clearly showed the P300 pattern caused by the target stimulus
(Fig. 3c). Also, there was no significant difference in signal patterns
for P300 when measured simultaneously with c-EEGd. It was
confirmed that the e-EEGd based on the tattoo-like electrodes
exhibit reliable characteristics comparable to the commercial
device based on the wet commercial electrodes in a low-noise
environment (Supplementary Fig. 11 and Table 6). Compared to
the RMS value at baseline, the measured P300 peaks in each
subject showed a significant amplitude, even with single-channel
electrodes (Supplementary Fig. 12). The dominant P300 frequency
band perfectly matches the dominant frequency band of motion
artifacts below 4 Hz
48,49
; thus, motion artifacts can easily distort
the P300 signal (Fig. 3d). The e-EEGd, which effectively blocks
motion artifacts, is therefore, well suited for P300 measurements
in noisy conditions. To confirm this effect, we measured P300 with
the auditory test when walking in place. As a result, it was
confirmed that P300 measurement was difficult for c-EEGd, which
is susceptible to motion artifact, unlike e-EEGd (Supplementary
Fig. 8c, d).
Subsequently, we experimented using the e-EEGd and an
autonomous driving RC car to determine if the unexpected
behavior of the AI-based machine caused the ErrP signal (Fig. 3e
and Supplementary Fig. 13). The RC car drives on the track line
and immediately pauses when the stop line is electronically
recognized. However, we programmed the RC car to drive without
stopping at the stop line with a specified probability (violation
probabilities of 2%, 5%, 10%, 20%, and 50%). We assessed
whether the ErrP signal was detected with the e-EEGd after the RC
car continued or stopped. The ErrP pattern was detected after the
RC car committed a violation and crossed the stop line
(Supplementary Video 3). When the RC car stopped at the stop
line, the ErrP signal pattern could not be identified. Among the
various peaks of the ErrP, Ern peak is observed between 50 and
100 msec, N200/FRN peak is observed around 200 msec, and
P300/Pe peak can be observed around 300 msec. Additionally,
because the probability of the RC car crossing the stop line
decreased, the subjects were less likely to expect the violation,
and the amplitude of the P300/Pe peak increased when a violation
occurred (Fig. 3f). We also measured ErrP patterns while walking
along a moving RC car using two devices (e-EEGd and c-EEGd). In
the case of c-EEGd, it was difficult to observe the ErrP pattern due
to the excessive noise (about 50 times) caused by vibration, but in
the case of e-EEGd, the ErrP pattern was confirmed by showing
motion artifact resistance (Supplementary Fig. 14).
Deep learning classification of the ErrP patterns
The ErrP component follows a specific pattern, but the peak
amplitude and latency change according to various conditions,
such as subject, situation, violation probability, etc
33,40
. This
phenomenon makes it difficult to determine whether the EEG
contains the ErrP pattern with simple conditional statements, such
as if and else. Thus, we proposed a deep learning classification to
determine that the transmitted complex EEG contains ErrP
patterns
50–53
. The most common deep learning networks nowa-
days are fully connected (FC) deep neural networks (DNNs),
convolution neural networks (CNNs), and recurrent neural net-
works (RNNs)
50,51
. They are also used individually but are usually
combined to allow for more effective classification
51–53
. Among
these three networks, we tested DNNs and long short-term
memory (LSTM) RNNs except CNNs. Because CNNs are excellent at
reducing features, are less effective in dealing with single-channel-
based EEG signals
51
. We compared several classifiers with fivefold
cross-validation to find a high-performance classifier and an
optimized input dimension, which is the time interval of the input
EEG data. There are two reasons for finding an efficient input
dimension: (1) An immediate response is only possible if the AI
can classify the presence/absence of an ErrP in the shortest
possible time from when the EEG is received, and (2) higher input
dimensions could worsen the performance of a classification
model by fitting on a random noise rather than the trend of input
data. A lower input dimension could help generalize a classifica-
tion model that may alleviate the impact of the curse of
dimensionality and overfitting problems. Input EEG data for
classification are preprocessed through a finite impulse response
(FIR) bandpass filter of 1–8 Hz based on simple optimizing study
(Supplementary Fig. 15) and parsed according to specific input
dimensions. As a result, the use of a FC DNNs and LSTM of RNNs
with FC (Fig. 3g, h) allows for the binary detection of the ErrP with
high accuracy and area under curve (AUC) rather than traditional
machine learning classifiers (Fig. 3i, Supplementary Fig. 16 and
Table 7). Linear discriminant analysis (LDA) and support vector
machine (SVM) are representative machine learning algorithms
that are very widely used for the ErrP pattern classifica-
tion
24,26,27,31,32
. The highest accuracy of 83.81% and area under
the curve (AUC) of 0.849 were achieved when single-trial EEG was
classified using LSTM with 30 input dimensions were set from
0.05 s to 0.35 s of EEG. This seems to be a valid result given that
the Ern, N200/Frn, and P300/Pe peaks are located within 0.05 s to
0.35 s, and the LSTM network is well suited for training time-series
data. This result suggests that ErrP classification is possible even
with a single-channel and dry electrode-based e-EEGd at a
reasonable level similar to the accuracy seen in other multi-
channel and wet electrode-based studies
24–32
. This is Additionally,
as the probability of unexpected behavior by the RC car
decreased, the P300/Pe peak intensity and the classification
accuracy increased. When the chance of an unexpected response
(i.e., not stopping) was 2%, the classifier accuracy increased to
100% for LSTM and 92% for DNN. When the probability of
stopping was 50%, the P300/Pe peak intensity was small, which
made it challenging to classify; however, the accuracy maintained
a certain level of performance, which was 81% for LSTM and 77%
for DNN (Fig. 3j). Then, we tested a classification on the signals
measured in e-EEGd and c-EEGd while moving. As a result, it was
confirmed that the signal measured in e-EEGd has a slight
J.H. Shin et al.
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decrease in accuracy, but it is still possible to classify ErrP patterns,
but c-EEGd does not (Supplementary Table 8). AI-based machines,
which are intended to be improved by BACLoS, show mostly high
accuracy. However, AI-based machines still cause unexpected
automation behaviors with low probability, causing inconvenience
and risking safety
54
. We believe that the higher-intensity ErrP due
Fig. 3 P300 event-related potential (ERP) and error-related potential (ErrP) obtained from 12 participants and their classifications. a–
dP300 measurements using the wireless earbud-like encephalography device (e-EEGd) with different auditory oddball tests. aThree different
auditory oddball tests used to record P300. bA scatter plot of the amplitude and latency of recorded P300. P300 ERPs evoked approximately
300 ms after recognition of unexpected events (high pitch in test 1, ‘no’voice in tests 2 and 3), which is the general latency for P300 (n=360
recordings; ten subjects). cP300 based on three different oddball tests: test 1 (top, n=190 cases), test 2 (middle, n=120 cases), and test 3
(bottom, n=228 cases). Each average result from ten subjects is overlaid on the background. dFast Fourier transform (FFT) spectra of
recorded EEG from tests from ten subjects. eErrP measurement setup using a remote-controlled (RC) car. The probability (2%, 5%, 10%, 20%,
and 50%) that the RC car unexpectedly moves without pausing at the stop line was manipulated. fAverage ErrP (n=295 cases) based on
probabilities of the unexpected ‘malfunction’(i.e., failure to stop) of the car. The lowest probability of the unexpected result caused the
highest ErrP intensity. gDeep neural network (DNN) architecture with fully connected and multiple layers. hLong short-term memory (LSTM)
architecture of recurrent neural network with multiple layers. iSingle-trial classification accuracies by time range of the input EEG data for
training and validation using deep learning and traditional machine learning algorithms: logistic regression (LR), linear discriminant analysis
(LDA), random forest (RF), and support vector machine (SVM). jClassification accuracy based on probability of an unexpected response.
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to the low probability of the unexpected working, results in high
classification accuracy, which is very suitable for compensating for
the malfunction of AI.
Real-time demonstration of the BACLoS
The BACLoS, including the e-EEGd, was used to demonstrate the
following two autonomous and reinforced systems for AI-based
machines. The first is an emergency interrupting system (EIS) that
urgently blocks an operation when an ErrP signal occurs due to an
AI malfunction that induces ERP signals. The second system is a
user-customized reinforcement system (UCRS) that predicts a
user’s satisfaction based on past decisions and is influenced by
ErrP feedback that reinforces particular decisions. The AI-based
machine receives an EEG from the e-EEGd, processes the signal
through a finite impulse response (FIR) filter (1–8 Hz), and then
checks the presence/absence of the ErrP component in real time
through the built-in pretrained classification model. Supplemen-
tary Fig. 17a shows a flowchart for the EIS. The AI requests EEG
data from the e-EEGd immediately after an instance of decision-
making. The e-EEGd sends EEG data in real time to the AI after
receiving the request. The AI system determines whether there is
an ErrP component in the EEG using deep learning classification. If
the ErrP component is found, which indicates that previous
decision-making may cause danger to the user, the AI operation is
urgently stopped or modified. We conducted a demonstration of
the EIS with an RC car (Supplementary Video 4). We tested the
emergency braking of an RC car using the EIS and a remote
controller to compare the performance of the BACLoS to the
physical reaction of the human subject (Fig. 4a). The braking
distance with the BACLoS was shorter, and there was less braking
distance variation than when braking the RC car by hand (Fig. 4b,
c). We analyzed the reaction time between ErrP feedback and the
physical response using a normal distribution graph (Fig. 4d and
Supplementary Fig. 18). The average reaction time using ErrP
feedback was 0.35 s, which was 0.13 s faster than the physical
reaction time. If someone is driving at 110 km/h, 0.13 s can shorten
the braking distance by 4 m and possibly prevent accidents. In
addition, the overall standard deviation using ErrP feedback was
1.8 times smaller than that using the hand directly. The response
distribution from the ErrP feedback peaked at ~350 ms, without
significant variation. In contrast, the physical responses exhibited
wide variation.
Supplementary Fig. 17b shows a flowchart of the UCRS. The
process by which the AI received ErrP feedback from human users
was the same as the process for the EIS. The ErrP feedback was
input to the AI as a reward or punishment. If an ErrP signal was
found in the user’s EEG, it functioned as a punishment for the AI.
Conversely, if an ErrP signal was not found in the EEG, it
functioned as a reward for the AI. Then, the AI matched and stored
the previous decision, action, and reward based on the ErrP
feedback. As a result, the AI was able to predict the reward for
making new decisions based on the stored information. Finally,
the AI was able to reinforce decision-making (i.e., an appropriate
response) that did not result in ErrP feedback. We also conducted
a demonstration of the UCRS using an RC car and a track that
included three forked roads (Supplementary Video 5). If the RC car
encountered a fork in the track and proceeded on a path that the
user did not want, the RC car received ErrP feedback, which acted
as a penalty. On the other hand, if the RC car continued on the
path the user wanted, the RC car did not receive ErrP feedback,
which served as a reward (Fig. 4e). When the RC car encountered a
forked road, it was constantly reinforced by the UCRS to behave as
desired by the user. This result is shown in Fig. 4f, g as
superimposed photographs of a moving RC car on the track. In
the initial attempt, the RC car, without any information about the
route, determined the course with a 50:50 probability of making a
particular choice at a fork in the road (Fig. 4f). After learning the
user’s preferred choices at the forks in the road through the UCRS,
the RC car selected the route that the user wanted with a high
probability (Fig. 4g). Classification accuracy also increased when
classification was based on the average ErrP data obtained by
overlapping the ErrP feedback. The DNN classification accuracy
increased to 92.29 ± 5.3% and 98.57 ± 1.7% when the ErrP
feedback overlapped in three and five trials, respectively (Fig.
4h). This result suggests that the AI can more accurately decide
what the user expects and prefers using UCRS.
We also adopted an autonomous maze solver, which is a
common application in robot and game algorithms. Figure 4i
shows a participant with an e-EEGd observing a maze solver. The
autonomous maze solver used the right-hand rule to determine
the exit after entering the maze. The right-hand rule is a method
of solving almost all mazes, but it cannot find the shortest
distance because it progresses in the wrong direction and through
trial and error. We reduced the time required to finish the maze
from 36.47 s to 14.12 s by providing ErrP feedback through the
BACLoS to the AI using the right-hand rule to solve the maze. After
the AI learned the shortest distance for solving the maze based on
ErrP feedback, the AI solved the maze in 13.37 s (Fig. 4j and
Supplementary Video 6). In addition, we demonstrated the user
interface of the AI assistant for BACLoS (Supplementary Fig. 19
and Supplementary Video 7).
DISCUSSION
The integration strategy presented here includes tattoo-like
electronics, wearable devices, wireless technology, bioelectric
signal measurement, deep learning, and embedded systems.
Elaborate engineering made the successful connection between
tattoo-like electronics and rigid wearable devices while maintain-
ing the low noise performances of tattoo-like electronics. It
enables a neuroadaptable, interactive BACLoS that is based on
high-SNR EEG signals and minimal motion artifacts in real-life
conditions. The device can continuously monitor EEG signals in
response to a diverse range of auditory and visual stimuli and
identify ErrP signals among the EEG signals. With the BACLoS
platform and wireless devices, we can offer a perspective on the
advanced brain–machine interface in real life, which can be
helpful in autonomous AI-based machines. We believe that a
personalized AI system can use human electrophysiological
signals to optimize machine decisions using reinforcement. For
further development, more interest in various fields and multi-
faceted research are required. The performance of BACLoS can be
further improved by adopting a recently explored conductive, soft,
and functional nanomembrane or hydrogel/polymer-based elec-
trodes. Another approach is applying an amplifier with low noise
and high precision and an Analog-to-Digital Converter (ADC) with
high resolution (>16 bit). More realistic studies with AI-based
machines and the BACLoS, such as experiments on the road with
self-driving cars, should be conducted. Notably, because the
BACLoS allows continuous biosignal collection in harsh conditions,
it can be used for advanced diagnosis using miniaturized
electronics and AI for ‘human-in-the-loop’machine learning. We
also believe that the strategies and results from this study will
provide inspiration and guidelines for experts in many fields.
METHODS
Tattoo-like electrode and interconnector
Temporary tattoo paper (Silhouette) served as the base substrate for
fabrication of the tattoo-like electrode. A parylene layer (300 nm thick) was
coated onto the tattoo paper by high-vacuum chemical vapor deposition
(CVD). A 5 nm chromium layer for adhesion and a 100 nm gold layer were
deposited by thermal evaporation through a serpentine open mesh
patterned shadow mask. The tattoo paper was cut using a mechanical
cutter to obtain the desired size and shape. The total thickness of the
J.H. Shin et al.
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Published in partnership with Nanjing Tech University npj Flexible Electronics (2022) 32
tattoo-like electrodes transferred onto the skin was approximately 900 nm.
The flexible interconnectors included tattoo-like connectors, anisotropic
conductive film (ACF) cables, and flexible printed circuit (FPC) connectors.
The tattoo-like connector was prepared using conventional microfabrica-
tion techniques, including thermal evaporation and reactive ion etching.
The details of the microfabrication and encapsulation methods are
provided in the Supplementary Note. The tattoo-like connector was
fabricated with a polyimide (PI, 1.2 μm)/gold (100 nm)/PI (1.2 μm) sandwich
structure to avoid mechanical mismatch by locating the metal part on a
neutral mechanical plane. The total thickness of the tattoo-like connector
on the skin is 2.8 μm. An ACF cable was used to bond both the tattoo-like
connector to the FPC connector. The ACF cable requires high-temperature
(>150 °C) and high-pressure (30–40 kg cm^-2) environments to ensure
adhesion. The thickness and width of the conducting area of the ACF cable
were 30 and 250 μm, respectively. The female FPC connector was
manufactured considering the width of the conducting area of the ACF
cable and the thickness of the male surface mount device (SMD)-type FPC
connector on the EEG measurement earbud (Supplementary Fig. 3). The
thickness of the FPC connector varied from 80 to 300 μm. Consequently,
the final interconnector has a step-up thickness gradient, which makes it
more stable, even if it is connected to a conventional rigid connector
(Supplementary Fig. 2).
Fig. 4 Enhancing machine decision-making using error-related potential (ErrP) feedback with the Brain-AI Closed-Loop System (BACLoS)
in real time. a–dDemonstration of an emergency interrupting system (EIS) based on ErrP feedback with the BACLoS with an autonomous
remote-controlled (RC) car. aSubject stops the vehicle using the EIS when it unexpectedly crosses the stop line (left) and uses a remotely
operated stop button (right). bOverlaid photos of RC cars stopped by ErrP feedback from human neural activity. cOverlaid photo of RC cars
stopped by a stop button via human physical activity. dNormal distributions of time required to stop after passing the stop line. Difference in
the mean braking time using ErrP feedback and the manual stop button is 0.13 s. e–hUser-customized reinforcement system (UCRS) on a GPS-
like navigation system of an autonomous RC car. eAn autonomous navigation system must make a decision at a fork in the road. ErrP is
detected when the navigation system chooses a route that the user did not prefer. Using ErrP signals, the machine reinforces the navigating
system for the most preferable route. fOverlaid photos of a vehicle repeating the track before the reinforcement, with 50% probability for
either decision at each fork in the road. gOverlaid photos of a vehicle repeating the track after user ErrP reinforcement. hClassification
accuracies based on intensification sequences. The ErrP feedback data for chosen routes overlap as the vehicle repeatedly traverses the track.
i, j An autonomous maze solver with the BACLoS. iA subject wearing the e-EEGd observes the maze solver in the display. jMaze results using
different strategies: right-hand rule, ErrP feedback based on EIS, and reinforcement based on UCRS.
J.H. Shin et al.
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npj Flexible Electronics (2022) 32 Published in partnership with Nanjing Tech University
Preparation of the wireless EEG measurement earbud
We used a four-layer printed circuit board (PCB) manufactured by JLCPCB
(Shenzhen, China). An electrophysiology amplifier chip (RHD2216, INTAN),
8-bit microcontroller (ATMEGA328P-MMH, Microchip), Bluetooth low
energy (BLE) module (Bot-NLE522, CHIPSEN), FPC connector (IMSA-
11501S-08C, IRISO), linear voltage regulator (TLV70033QDDCRQ1, Texas
Instruments), rechargeable 3.3 V lithium polymer battery, and four
capacitors (0603 size, Samsung) were soldered onto the PCB board using
solder paste (SMD291SNL10T5, Chip Quick) (Supplementary Fig. 4 and
Supplementary Table 9). Polyethylene resin (FLDUCL02, Formlabs) was
used for the 3D-printed case of the soldered PCB board. The 3D-printed
case is composed of upper and lower parts that are designed in line with
the shape of the soldered PCB board and battery. Atmel Studio 7.0 (Atmel)
and Arduino IDE 1.8.13 (Arduino) were used to write bootload and upload
software for the microcontroller. The programmable sampling rate used for
the EEG measurement was 100 Hz for the basic setup. The resolution of the
integrated analog-to-digital converter (ADC) was 16-bit, and the differ-
ential gain of the amplifier was 192. Continuous transmission of EEG
signals in wireless conditions is possible for 8 h with a fully charged battery
(Supplementary Fig. 20).
Fabrication of the autonomously driving RC car
All components were mechanically fixed to the main acrylic frame and
electrically connected using breadboard and jumper wires. A motor driver
(L9110s, SMG), artificial intelligence (AI)-embedded microcontroller (Ardu-
ino Nano 33 BLE, Arduino), 8-bit microcontrollers (Arduino UNO R3,
Arduino), BLE module (Bot-NLE522D, CHIPSEN), microSD card socket (SZH-
EKBZ-005, SMG), microSD card (SDSQUAR-016G, Sandisk), DC motors
(NP01D-288, OEM), reflective photointerrupter (TCRT5000, Vishay), passive
components, and batteries were used (Supplementary Fig. 13 and
Supplementary Table 10). The outer frame was generated using a 3D
printer and polyamide (nylon 12) powder. There was a black line on the
track located between the two floor-facing reflective photointerrupters of
the RC car. When the photointerrupter was placed on the black line, the
light could not be reflected, and the photointerrupter reading changed.
The RC car minutely adjusted the driving direction according to the
photointerrupter values and always maintained the black line between the
two photointerrupters. As a result, the RC car could follow the track
without leaving the line.
Measurement of skin–electrode impedance
Two tattoo-like electrodes with tattoo-like connectors were attached to the
skin with 10 cm between them. One electrode was the working (cathode)
electrode, and the other was the reference/counter (anode) electrode for
the electrochemical impedance spectroscopy measurement. Impedance
was measured with a single-channel potentiostat (PalmSens4, PalmSens B.
V.) and software (PSTrace, PalmSens B.V.) from 1 to 1000 Hz every 30 min
for 10 h.
Continuous EEG recording while moving
Two volunteers participated in the EEG recording under several different
motion conditions. The tattoo-like electrodes were attached to the side of
the forehead as a working electrode, the temple as the ground electrode,
and mastoid as the reference electrode. The tattoo-like connectors
between the electrodes and interconnector were attached to the side of
the face, and the EEG measurement earbud was plugged into one ear. The
electrodes, connectors, and measurement systems were connected (e-
EEGd) to transmit EEG data wirelessly. EEG data were received wirelessly
via Arduino Nano BLE (Arduino) and stored on a computer. Volunteers
performed various motions such as sitting, standing, walking, cycling,
riding, and driving. The same measurement was also performed with the
volunteer’s eyes closed and opened to measure the alpha waves under
these conditions. Extra experiments were conducted by replacing the
following components with conventional components (c-EEGd): the
electrodes (2223H, 3 M), lead cable (MIKROE-2457, MikroElektronika), and
measurement system (RHD2216 Arduino shield, INTAN); this was done to
conduct a noise comparison analysis. MATLAB R2019 a (MathWorks) was
used to create the main platform of the EEG signal processing toolbox,
BrainStorm (Tadel et al. 2011). An FIR bandpass filter (8–12 Hz) and Morlet
wavelet transform (5–15 Hz) were used to analyze the alpha waves. The
noise characteristics analysis included calculation of the root mean square
value and fast Fourier transform (FFT) of continuous EEG data.
P300 ERP and ErrP signal acquisition and analysis
Twelve volunteers participated in an experiment to obtain P300 event-
related potential measurements. The experiment included three different
setups: auditory oddball tests (test 1, test 2, and test 3) for P300
measurement; a visual oddball test with an autonomously driving RC car
that moves in various unexpected ways for ErrP measurement; and a test
of the assistant interface, which randomly gave undesirable answers for
ErrP measurement. All data were recorded for 1 s after each stimulus. An
FIR bandpass filter (1–30 Hz and 1–8 Hz) was applied as a preprocessing
method for the analysis. The average amplitudes of the P300 peak from
each subject were derived from the average ERP waveforms. The signal-to-
noise ratio was calculated by dividing the P300 peak amplitude value by
the RMS value at the baseline, before the target stimuli. FFT analysis was
performed to reveal the domain frequency band of the P300 pattern.
P300 measurement using auditory oddball tests (tests 1, 2,
and 3)
Both e-EEGd and c-EEGd are used for these tests. The wet commercial
electrodes are attached right next to the tattoo-like electrodes of c-EEGd.
The duration and interval of the stimuli were 0.1 and 0.9 s, respectively. The
target stimuli were randomly organized and presented with a 10%
probability. All subjects were asked to count the number of target stimuli
in the test to ensure that their attention was on the tests and close the
eyes for avoiding ocular artifacts. Test 1 used a high-pitched sound
(1000 Hz) for the target stimuli and a low-pitched sound (250 Hz) for
nontarget stimuli. Test 2 included a human voice saying ‘no’for the target
stimuli and ‘yes’for the nontarget stimuli. Test 3 included a human voice
saying ‘no’for the target stimuli and silence for the nontarget stimuli. We
also measured P300 as an auditory test when subjects walked slow. To
acquire the P300 data wirelessly in the oddball tests, an 8-bit
microcontroller (Arduino UNO R3, Arduino), BLE module (Bot-NLE522D,
CHIPSEN), MP3 player module (KE0092, KEYES), and mini speaker were
connected to a computer. CoolTermWin (Roger Meier) was used for serial
communication between the wireless receiver and computer to save
wireless data in the EEG measurement system.
ErrP measurement using autonomously driving RC car:
various unexpected motions
To measure EEG signals that reflect more realistic situations, a wireless
recording was obtained as the volunteer observed the wireless autono-
mous driving RC car on a custom-designed track (black line). The RC car
was designed to follow the black line on the track using reflective infrared
sensors and to stop immediately when it reached the stop line (crossroad).
However, with a certain programmed probability (2%, 5%, 10%, 20%, and
50%), the RC car did not stop at the stop line and exhibited abnormal
behavior. Whenever the RC car detected a stop line, it wirelessly sent a
timing cue for temporal synchronization to the EEG earbud. The e-EEG
system then measured the EEG signal for 1 s from this cue point while
simultaneously transmitting the signal back to the RC car. The EEG data
transmitted by the earbud were saved to the SD card that was connected
to the RC car. If the RC car operated abnormally, the EEG was stored as ErrP
measurements according to the target stimulus; if it operated normally, the
EEG was stored as ErrP measurements according to the nontarget stimulus.
ErrP measurement using assistant interface: undesirable
answers at random
The assistant interface answered the user’s request for a phone call. The
assistant interface had a preprogrammed probability (10%) of malfunction,
similar to the autonomous driving RC car. When the assistant interface
answered the user, it sent a wireless timing cue to the EEG earbud. The EEG
earbud system recorded the EEG signal for 1 s after receiving the cue from
the assistant interface and sent EEG data back to the assistant interface in
real time. This EEG data was saved on a computer connected to the
assistant interface.
Machine learning classification
Input EEG data are 295 cases of ErrP pattern data recorded from
experiment with autonomous driving RC car and e-EEGd. RC car usually
works normally rather than malfunction, so the measured data has more
un-target data than target data. If all of these biased proportions of data
are used for classifier training, there is a high probability that incorrect
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Published in partnership with Nanjing Tech University npj Flexible Electronics (2022) 32
training will proceed. For classifier training, we configured input data with
the same ratio of target data and un-target data. We adopted five
classification models for ErrP pattern detection: logistic regression (LR),
linear discriminant analysis (LDA), k-nearest neighbors algorithm (k-NN),
random forest (RF), and support vector machine (SVM). For linear
regression, a limited-memory Broyden–Fletcher–Goldfarb–Shanno solver
with a ridge penalty was applied. Singular value decomposition was used
to fit the input data for the LDA model. For the k-NN model, the number of
neighbors was set to five. The Minkowski distance was applied. In the RF
model, 128 trees were used. The Gini impurity was used as a splitting
condition for each decision tree. The SVM model was implemented based
on libsvm. The penalty parameter was set to 1.0. A linear kernel with a
degree of three was used to boost the classification performance by
embedding the EEG signal into a higher-dimensional space. To optimize
the input dimensionality, which refers to the size and starting point of the
input data, we repeatedly conducted training and 5-fold cross-validation of
each classifier. The accuracy was determined according to the dimensions
and starting points of the input data. We also calculated the area under the
curve of the receiver operating characteristic curve, which is one of the
most widely used metrics for representing the performance of a classifier.
Deep learning classification
The input EEG data is the same as the data used for training machine
learning classifiers. We adopted deep neural networks (DNNs) and long
short-term memory (LSTM) models for ErrP pattern detection. These
models were optimized using the Keras platform with a TensorFlow
(Google) backend. The input EEG data from a single recording channel
were preprocessed with an FIR bandpass filter and parsed into data of 30
input dimensions (0.3 s interval). We used two dense layers, including 16
and 8 units with ReLU activation, and one output layer with sigmoid
activation for the DNN model. In addition, we used one LSTM layer
including 90 units with tanh activation, one dense layer including 18 units
with ReLU activation, and one output layer with sigmoid activation for the
LSTM model. We repeatedly conducted training and 5-fold cross-validation
of each classifier with a constant learning rate (Adam, learning rate =
0.001, beta_1 =0.9, beta_2 =0.999)
55
and computed the mean of the
squares of errors between labels and predictions to optimize classifier
performance.
Real-time deep learning classification
TensorFlow Lite (Google) and TensorFlow (Google) were used to deploy
pretrained classification models and train classification models using a
deep learning algorithm for the BACLoS. The recorded ErrP data were used
to train the classification model. The time interval of the input EEG data for
training and examination of the classification model was fixed at 0.3 s
based on the optimization study. The pretrained classification model was
converted to the TensorFlow Lite format without quantization. The
converted TensorFlow Lite file was then encoded in an Arduino header
file to upload and utilize in the Arduino Nano 33 BLE (Arduino) with the
Arduino IDE (Arduino). When the Arduino Nano 33 BLE with a built-in
trained model received EEG data from the e-EEGd wirelessly, it processed
the data with an FIR filter (1–8 Hz) in the same format as the trained data,
parsed it as a 0.3 s input dimension, and then inputted it to the trained
model to test print the result in real time.
EIS and UCRS for an RC car via BACLoS with the e-EEGd
Two controllers, the Arduino Nano 33 BLE and Arduino Uno, were built into
the RC car to control its movements. The Arduino Nano wirelessly received
EEG signals and performed classification using a deep learning algorithm
in real time. The Arduino Uno controlled all the components of the RC car,
including sensors, motors, and drivers. The digital ports of the Arduino
Nano and Uno were connected by commercial jumper cables. The Arduino
Uno sent timing cue information to the Arduino Nano through a digital
port when it detected a stop line or a fork in the track. Consequently, the
Arduino Nano wirelessly received the EEG signal from the e-EEGd and
performed ErrP classification simultaneously. The output of the ErrP
classification was sent to the Arduino Uno through the digital port to
control the RC car instantly to demonstrate the EIS. For the UCRS
demonstration, the classification result was used to choose the appropriate
path when the RC car encountered the same fork in the road for a second
time. Before this point, there was no information on the user’s preference
for a particular fork, so the route was selected at random. After the route
was selected, there was ErrP feedback about the selected route, which
made it the preferred route. The Arduino Uno then reinforced the internal
programs for route selection on the forked road according to the user’s
preference information. The RC car more accurately reflected the user’s
preference with repeated reinforcement.
Demonstration of the BACLoS for the maze solver
The maze solver was composed of an AI-embedded microcontroller
(Arduino Nano 33 BLE, Arduino) and Processing, which is the software that
creates the maze interface. Before reinforcement, the maze solver solved
all the mazes using the right-hand rule algorithm. The maze was generated
by an internal program (Processing). Users with an e-EEGd thought about
the shortest path to solve the maze and observed how the maze solver
solved the maze. The maze solver sent a timing cue to the e-EEGd
whenever it determined the direction of progress using the right-hand rule
algorithm. The e-EEGd measured and transmitted the EEG signals from the
user in real time after receiving the request. Then the maze solver analyzed
the transmitted EEG signals using the trained classifier to confirm that the
ErrP pattern was included. If an ErrP pattern was identified in the EEG
signal through classification, the maze solver deemed the previous
decision inefficient. Based on this series of processes, the maze solver
immediately reversed the direction of progress, returned to the previous
decision point, and proceeded in the opposite decision. The maze solver
saved the decision information and proceeded with reinforcement to
revise the internal algorithm to find the path. After repeated reinforce-
ment, the maze solver was able to find the shortest path through
the maze.
Demonstration of the BACLoS for the assistant interface
The speech recognition kit (SZH-KI001, SMG), BLE module (Bot-NLE522D,
CHIPSEN), AI-embedded microcontrollers (Arduino Nano 33 BLE, Arduino),
and 8-bit microcontrollers (Arduino UNO R3, Arduino) were the main
components of the assistant interface hardware. Processing Software was
used to create a user interface for the assistant interface. Whenever the
assistant interface responded to a user’s request, the assistant interface
processed the transmitted EEG signal through the trained classifier to
determine whether the ErrP pattern was included. If an ErrP pattern was
identified in the EEG through classification, the assistant interface
determined that the previous response was incorrect and presented
another, more suitable, response to the user.
IRB approval for the study of human subjects
All measurements were conducted following the protocol approved by the
Institutional Review Board (IRB) of Sungkyunkwan University (ISKKU 2019-
06-011). All participants were given a comprehensive set of instructions
regarding the tests, agreed to the testing procedures, and provided written
informed consents to take part in the study.
DATA AVAILABILITY
All data are available in the main text or supplementary materials. All information can
be requested from one of the corresponding authors.
CODE AVAILABILITY
The code for the machine learning and deep learning models are available on GitHub
(https://github.com/JooHwanS/ErrP). Additional information can be requested from
one of the corresponding authors.
Received: 29 November 2021; Accepted: 20 April 2022;
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ACKNOWLEDGEMENTS
The authors would like to thank Dr. Soonkwon Paik (Hyundai Motors) for helpful
discussions concerning the devices. The authors thank to Sungho Gong for
participating the measurement experiment and providing photos. This research
was proposed by the Hyundai-NGV project, but was mainly supported by the
National Research Foundation (NRF) funded by the Korean government (MSIT) (NRF-
2019M3C7A1032076, and NRF-2020M3C1B8016137). Also, the work is partially
funded by SKKU Research Project (S-2021-2151-000 International A).
J.H. Shin et al.
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Published in partnership with Nanjing Tech University npj Flexible Electronics (2022) 32
AUTHOR CONTRIBUTIONS
J.H.S. and T.-i.K. designed the experiments; J.H.S., H.R., and J.O. performed the
experiments; J.H.S. and T.-i.K. led this work; J.K., S.J.K., and H.P. analyzed the EEG data;
and J.H.S., J.U.K., and T.-i.K. wrote the paper.
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41528-022-00164-w.
Correspondence and requests for materials should be addressed to Tae-il Kim.
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J.H. Shin et al.
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npj Flexible Electronics (2022) 32 Published in partnership with Nanjing Tech University
