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Open Access

Peer-reviewed

Research Article

Detection of motor-related mu rhythm desynchronization by ear EEG

  • Masaya Ueda ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Visualization, Writing – original draft

    uedam@omu.ac.jp

    Affiliation Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan

    ⨯
  • Keita Ueno,

    Roles Data curation, Formal analysis, Writing – review & editing

    Affiliation Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan

    ⨯
  • Takao Inoue,

    Roles Data curation, Writing – review & editing

    Affiliation Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan

    ⨯
  • Misao Sakiyama,

    Roles Data curation, Writing – review & editing

    Affiliation Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan

    ⨯
  • China Shiroma,

    Roles Data curation, Writing – review & editing

    Affiliations Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan, Rehabilitation Unit, Murata Hospital, Osaka, Japan

    ⨯
  • Ryouhei Ishii,

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    Affiliations Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan, Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan

    ⨯
  • Yasuo Naito

    Roles Methodology, Project administration, Supervision, Writing – review & editing

    Affiliation Department of Occupational Therapy, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan

    ⨯

Abstract

Event-related desynchronization (ERD) of the mu rhythm (8–13 Hz) is an important indicator of motor execution, neurofeedback, and brain-computer interface in EEG. This study investigated the feasibility of an ear electroencephalography (EEG) device monitoring mu-ERD during hand grasp and release movements. The EEG data of the right hand movement and the eye opened resting condition were measured with an ear EEG device. We calculated and compared mu rhythm power and time-frequency data from 20 healthy participants during right hand movement and eye opened resting. Our results showed a significant difference of mean mu rhythm power between the eye opened rest condition and the right hand movement condition and significant suppression in the 9–12.5 Hz frequency band in the time-frequency data. These results support the utility of ear EEG in detecting motor activity-related mu-ERD. Ear EEG could be instrumental in refining rehabilitation strategies by providing in-situ assessment of motor function and tailored feedback.

Citation: Ueda M, Ueno K, Inoue T, Sakiyama M, Shiroma C, Ishii R, et al. (2025) Detection of motor-related mu rhythm desynchronization by ear EEG. PLoS ONE 20(4): e0321107. https://doi.org/10.1371/journal.pone.0321107

Editor: Fali Li, UESTC: University of Electronic Science and Technology of China, CHINA

Received: August 6, 2024; Accepted: March 1, 2025; Published: April 8, 2025

Copyright: © 2025 Ueda et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All EEG data or demographic data are available from the Dryad database (accession URL: http://datadryad.org/stash/share/UZfy8id2Y9zB0VPLsEkDRTZXdrlqXAFd81RXhrtwTxw).

Funding: Initials of the authors who received each award: M.U Grant numbers awarded to each author: 23K16581 and 22K21212 The full name of each funder: Japan Society for the Promotion of Science(JSPS) URL of each funder website: https://www.jsps.go.jp. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Introduction

In neurorehabilitation, finding metrics that are accurate and user-friendly for assessing motor function and recovery is critical, particularly in stroke rehabilitation. Electroencephalography (EEG) is a leading method for non-invasive measurement of human brain activity. Typical examples are the event-related desynchronization (ERD) of the mu rhythm (8-13 Hz) serving as an important indicator of motor execution and planning [1–6]. Mu-ERD is not only associated with recovery from stroke-induced motor disorder but has also been established as an indicator of neurofeedback and brain-computer interface [7–10]. While traditional scalp EEG setups can capture cortical activity with fine temporal precision, they often have spatial limitations due to electrode placement and are not suitable for everyday rehabilitation practice due to their laboratory-bound nature [9,11,12].

The field has witnessed a shift towards portable and wearable EEG systems due to technological advancements, making brain activity monitoring feasible in everyday settings [13–15]. However, most nonclinical EEG devices are designed for general-use EEG applications and lack support for sophisticated signal processing and effective feedback generation [16]. The use of wearable EEG devices has been limited primarily to frontal scalp measurements, providing a narrow view of motor-related brain activity [11,17]. Ear EEG is a new approach that holds the potential to enhance the feasibility of long-term observation of brain activity in clinical settings [18–20]. Ear EEG has been validated using common EEG paradigms such as alpha attenuation and auditory evoked potentials and is able to accurately characterize the frequency spectrum and has similar signal-to-noise ratios as nearby scalp electrodes [13,19]. In addition, previous studies have shown a high correlation between the ear EEG and electrodes located in the temporal and inferior parietal lobe [19,21,22]. Nevertheless, the efficacy of ear EEG in recording motor-related brain dynamics, particularly mu-ERD, during specific motor tasks is not yet fully established. This represents an important area for further research to validate the ability of ear EEG to identify motor-related neural signatures, such as mu-ERD, across different motor tasks, potentially expanding the application of EEG in neurorehabilitation [12,23].

Our study examined the feasibility of using an ear EEG device to monitor the ERD of the mu rhythm during the hand movement. We analysed the EEG data of the right hand movement and the following eye opened resting condition were measured with an ear EEG device. Confirmation of its utility would validate ear EEG as a tool for assessing motor-related brain activity, providing an accessible and less invasive alternative to traditional EEG setups. It would also underscore the potential of ear EEG to support the development of novel, personalized rehabilitation approaches.

Methods

Participants

This study involved 27 healthy volunteers (mean age: 22.6 ±  4.6 years, six males). Minors were not included in the participants. Participants were recruited from August 1, 2023, to January 31, 2024. The dominant hand was evaluated in FLANDERS Handedness Questionnaire Japanese. Before recruiting, informed consent was obtained in writing from all participants included in the study. This study was conducted with the approval of the Osaka Prefecture University Graduate School General Rehabilitation Studies Ethics Committee (2023–211). All the procedures were conducted following the tenets of the Declaration of Helsinki.

EEG device and setup

We used XHOLOS Sounds Ear EEG Device (CyberneX Corporation, Japan) for measuring EEG. This device features a unique 1-channel system. The measurement electrode is the right ear canal, and the reference and ground electrodes are the left ear canal using ear-tips (Ear-tip type flexible electrode, base material: Si rubber) as electrodes (Fig 1). It has a sampling rate of 600 Hz and meets the Japanese Industrial Standards (JIS, T 1203) for medical EEG equipment in terms of signal quality, impedance, and noise level. EEG signals were transmitted via Bluetooth to the measurement laptop PC by HP 16-wf0000 (HP Corporation, Japan) and recorded using the measurement application (CyberneX Corporation, Japan). The electrode was sat in both ear canals after being wiped with an alcohol swab. The sampling rate was set to 600 Hz.

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Fig 1. Photograph of the ear EEG device and ear-tip.

This ear EEG device features a unique 1-channel system. The measurement electrode is the right ear canal, and the reference and ground electrodes are the left ear canal using ear tips (Ear-tip type flexible electrode, base material: Si rubber) as electrodes.

https://doi.org/10.1371/journal.pone.0321107.g001

Experimental procedure

The EEG measurements were performed in the relaxed sitting position. All participants completed eye closed resting, eye opened resting, and right-hand movement condition. The alpha-blocking, which an open/closed-eye task can confirm, was used to verify the accuracy of EEG measurements. Below is an overview of the tasks (Fig 2). EEG signals were recorded under the following three conditions.

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Fig 2. Overview of the experimental task for EEG measurement.

https://doi.org/10.1371/journal.pone.0321107.g002

Eye closed resting condition.

Participants were instructed to close their eyes while sitting on a chair. The eyes were then kept closed for 2 min.

Eye opened resting condition.

Participants were instructed to open their eyes while sitting in a chair and to look at a fixation point on the table in front of them while relaxing for 2 minutes.

Right-hand movement condition.

Participants were instructed to perform 15 seconds of repetitive right-hand grasp and release movements with eyes opened, followed by 15 seconds of eye opened resting. This sequence was repeated eight times during the session. The hand-grasp and release movements were designed to simulate functional tasks to ensure the relevance of the EEG data to motor activities. The timing of the start and end of the hand movement was confirmed by video recordings synchronized with the EEG. We chose the right-hand movement because most of the subjects were right-handed. Previous studies show that handedness influences the proportion of pre-movement mu-ERD in the left and right peri-rolandic areas [24,25].

EEG data acquisition and analysis

EEG data obtained from 27 participants underwent thorough visual inspection and assessment for the presence of alpha-blocking phenomenon and overall signal quality by a physician certified by the Japanese Society of Clinical Neurophysiology.

The EEG data from the right hand movement condition and the subsequent eye open resting period were analyzed using Brain Electrical Source Analysis: BESA Research 7.1 software developed by BESA GmbH, Germany. As part of the offline processing, a high-pass filter set at 4.0 Hz and a low-pass filter set at 40.0 Hz were applied. The EEG data from each condition were divided into 2-second segments for statistical analysis, and the epoch-included potential artifacts, such as blinks and electromyograms, were visually eliminated, while further artifact detection scans were performed with an amplitude threshold set at 100 µ V. The average number of remaining epochs during the eye opened resting condition was calculated to be 54 ±  2, and for the right hand movement condition, the average number was 52 ±  4. First, analyses with Fourier transforms were computed using a multi-taper method for each condition [26,27] at a 0.25 Hz resolution. This resulted in estimates of absolute spectral power sampled for every 1 Hz bin during the interval of 4–40 Hz. Second, the brain oscillatory activity changes during right hand movement condition and eye opened resting condition were transformed into the time-frequency domain by using complex demodulation [28,29] (for detailed information on this methodology, see reference No.26,27). The frequency spectrum ranged from 4.0 Hz to 30.0 Hz in 0.5 Hz increments with a time sampling rate of 100 ms. After that, mu rhythm power (8-13 Hz) and the time-frequency data of the right hand movement condition followed by the eye open rest condition were analyzed for each participant and a grand average was made for all participants.

Statistical analysis

To compare the averaged mu rhythm power (8-13 Hz) of each participant between the right hand movement condition versus the eye open rest condition, Wilcoxon signed rank test analyses were performed using SPSS software version 28. The significance level was set at 0.05.

Statistical differences of time-frequency data between the right hand movement condition and the open eye rest condition were analyzed by permutation test based on t-test and cluster analysis using BESA Statistics 2.1 (BESA GmbH, Germany). We set the average over time to average the 2-second windows, and a paired t-test with 1000 permutations was used to compare the EEG data of each condition. When comparing the differences in time-frequency data, a comparison is made for each voxel that is output according to the time-frequency resolution. Therefore, it is necessary to correct for multiplicity. BESA Statistics uses parameter-free permutation testing based on the Student’s t-test [30,31]. In this study, there were no predefined clusters because BESA Statistics automatically identifies clusters in time and frequency that are significantly different between the two conditions. The null hypothesis of “the data under the experimental conditions comes from the same probability distribution” was rejected if at least one t-value was above the critical threshold for p <  0.05 determined by 1000 permutation.

Results

Participants and signal quality EEG data

We recruited 27 healthy volunteers (mean age: 22.6 ±  4.6 years, 6 males). However, 7 of the 27 participants did not complete the measurement protocol because the size of the ear tips did not fit the ear canal. Finally, 20 participants (mean age: 22.9 ±  4.9 years, 6 males) were included in the analysis. Results of the FLANDERS Handedness Questionnaire Japanese: 2 participants were left-handed.

In the EEG data of the 20 participants analysed, alpha-blocking was visually verified in the closed-eye condition. However, mu rhythm suppression could not be visually confirmed.

Mu rhythm (8–13Hz) power

The time course of the grand average for the mu rhythm power is shown in Fig 3 and the mean mu rhythm power (μV2) for each participant in the right-hand movement condition and eye opened resting condition is shown in Fig 4. The mean and standard deviation of mu rhythm power in the eye opened resting condition was 5.46 ±  2.44 μV2 and in the right hand movement condition was 4.92 ±  2.18 μV2. The Wilcoxon signed rank test revealed a significant difference in the averaged mu rhythm power between each condition (P < 0.001).

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Fig 3. The time course of the grand average for the mu rhythm power.

The mean Mu rhythm power of all participants was plotted for 30 seconds, starting 5 seconds before the right hand movement. The right hand movement continued for 15 seconds, after which participants were instructed to rest for 15 seconds with their eyes open. Each participant repeated this 8 times and the average Mu rhythm power for the 8 trials was calculated.

https://doi.org/10.1371/journal.pone.0321107.g003

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Fig 4. Mu rhythm power for each participant.

The bar graph represents the mean mu rhythm power (μV2) for each participant in the right-hand movement condition and eye opened resting condition. The blue bar graph illustrates the mean mu rhythm power values in the right-hand movement condition, while the red bar graph depicts the mean mu rhythm power values in the eye opened resting condition.

https://doi.org/10.1371/journal.pone.0321107.g004

Time-frequency data

The grand average of the time-frequency data for 15 seconds of the eye opened resting condition and the right hand movement condition was showed Fig 5. The paired t-test with 1000 permutations for the time-frequency data showed a significant suppression in the 9-12.5 Hz frequency band during the right hand movements condition compared to the eye opened resting condition (p <  0.001) (Table 1). Interestingly, other significant reductions were also found at 4-6 Hz and 25-25.5 Hz (p =  0.001).

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Table 1. Results of the paired t-test comparing the time-frequency data of the right hand movement condition and the eye opened resting condition.

https://doi.org/10.1371/journal.pone.0321107.t001

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Fig 5. The grand average of the amplitude-based time-frequency data for 15 seconds of eye-opened rest condition and the right-hand movement condition in 20 participants.

Amplitude higher than baseline at 15 seconds of eye opened resting was shown in red and lower activity in blue. Mu rhythm (8-13Hz) ERD compared to eye opened resting was showed during right hand movement (the right side).

https://doi.org/10.1371/journal.pone.0321107.g005

Discussion

Our investigation examined the ability of the ear EEG device to monitor mu-ERDD during hand movement. While visual confirmation of mu rhythm suppression on the waveform was elusive, statistical analysis showed a significant difference in mu rhythm power between the eye opened rest condition and the right hand movement condition and significant suppression in the 9-12.5 Hz frequency band in the time-frequency data. These findings support the utility of ear EEG in capturing motor activity-related mu-ERD, are consistent with established research linking mu-ERD to sensorimotor cortex engagement during motor tasks [2,4,9,32,33], and demonstrate the potential of ear EEG in such applications.

The innovation of this study is the implementation of an ear EEG device, which is emerging as a practical tool for unobtrusive ambulatory monitoring of brain activity [12,14]. The validity of ear EEG has been demonstrated through its use with common EEG paradigms, including alpha attenuation and auditory evoked potentials. It has been shown to accurately characterize the frequency spectrum and have a signal-to-noise ratio comparable to that of nearby scalp electrodes [13,19]. Previous studies suggest that ear EEG is comparable to conventional scalp EEG in terms of performance and accuracy for brain sources close to the ear, such as the temporal lobe [19,21,22]. In addition, several studies have shown a high correlation between the ear EEG and electrodes located in the forehead and inferior parietal [21,22]. These phenomena can possible to explained by volume conduction of electrical signals by structures other than the brain parenchyma, such as skin, skull, and cerebrospinal fluid [34,35]. Cerebrospinal fluid has high conductivity and negligible interindividual variability [36]. In our study, mu-ERD was measured with ear EEG, which may also be affected by volume conduction. Indeed, mu-ERD has also been shown to be measured in the lower part of the sensorimotor cortex and in the lower parietal lobes, compared to beta and gamma ERD [37,38]. Previous studies have demonstrated that mu-ERD is observed bilaterally during movement execution [39–41]. Consequently, we concluded that mu-ERD was observed during right-hand movement using this ear EEG device with only a 1-channel system. This research pioneers the detection of movement-related oscillatory activity via ear EEG. It is important to note that our study did not use conventional electroencephalography or other neuroimaging measures in combination, so it is impossible to confirm whether the study captured signals originating from the sensorimotor cortex. Nevertheless, the implications for neurorehabilitation are significant, particularly for the development of personalized wearable neurofeedback systems for motor impairments [9,10,23,42].

Interestingly, the time-frequency analysis revealed a significant decrease in the theta (4–6 Hz) and beta (25–25.5 Hz) frequency bands during active movement compared to a resting state. Such oscillatory changes in the alpha and beta bands, both before and after voluntary movements, are well documented [4,43–46]. In particular, beta oscillations are associated with motor improvements and have been proposed as markers of recovery in brain-computer interface therapies for post-stroke rehabilitation [47–49]. In addition, theta oscillations are often involved in brain activation during movement and are associated with cognitive functions related to the hippocampus and medial prefrontal cortex, affecting motor control, attention, and memory [50–55]. Ketenci et al. (2019) reported that the presence of alpha, beta, and theta rhythms provides superior monitoring of motor-related brain activity, highlighting the critical role of theta rhythm in movement initiation and execution [56]. Our results suggest that ear EEG may be able to detect these crucial oscillatory activities, highlighting its potential utility in the development of customized neurofeedback systems for rehabilitation purposes.

However, we must acknowledge the limitations of this study, such as the sample size and the exclusive focus on healthy individuals [57–59]. In addition, the absence of a control condition or comparison to a standard scalp EEG setup limits the ability to draw meaningful conclusions about the superiority or equivalence of ear EEG for detecting mu-ERD during hand movements. The ear EEG device was constrained by its capacity to measure only a single channel in this study. Consequently, the analysis of spatial information, such as a topographical map or current source density, was not feasible. Comparative validation of ear electroencephalography with standard scalp electroencephalography has been done in previous studies [19,21,22]. Additionally, electromyographic signals were not recorded in this study. The timing of the start and end of the hand movement was confirmed by video recordings synchronized with the EEG. Future studies should expand the demographic scope, including patients with neurological disorders, to evaluate the clinical applicability of ear EEG in rehabilitation settings. Such efforts could provide important insights into the neural underpinnings of motor recovery and inform the development of more effective rehabilitation protocols.

Conclusions

The purpose of this study was to evaluate the feasibility of the ear EEG device for monitoring mu-ERDERD during hand grasp and release movements. The statistical analysis showed a significant difference in mean mu rhythm power between the eye opened rest condition and the right hand movement condition and significant suppression in the 9-12.5 Hz frequency band in the time-frequency data. Our results suggest that ear EEG may be able to detect mu-ERD associated with hand movement, highlighting its potential utility in the development of neurofeedback systems for rehabilitation purposes. Ear EEG could be instrumental in refining rehabilitation strategies by providing in-situ assessment of motor function and tailored feedback.

Acknowledgments

The authors would like to sincerely thank Mr. Kiichirou Arikawa, Mr. Ryosuke Sensui, and Mr. Yuto Doura of CyberneX Corporation for providing the ear EEG device and technical support.

References

  1. 1. Pfurtscheller G, Neuper C, Ramoser H, Müller-Gerking J. Visually guided motor imagery activates sensorimotor areas in humans. Neurosci Lett. 1999;269(3):153–6. pmid:10454155
  2. 2. Neuper C, Müller-Putz GR, Scherer R, Pfurtscheller G. Motor imagery and EEG-based control of spelling devices and neuroprostheses. Prog Brain Res. 2006;159:393–409. pmid:17071244
  3. 3. Neuper C, Pfurtscheller G. Evidence for distinct beta resonance frequencies in human EEG related to specific sensorimotor cortical areas. Clin Neurophysiol. 2001;112(11):2084–97. pmid:11682347
  4. 4. Inamoto T, Ueda M, Ueno K, Shiroma C, Morita R, Naito Y, et al. Motor-Related Mu/Beta Rhythm in Older Adults: A Comprehensive Review. Brain Sci. 2023;13(5):751. pmid:37239223
  5. 5. Andrew C, Pfurtscheller G. On the existence of different alpha band rhythms in the hand area of man. Neurosci Lett. 1997;222(2):103–6. pmid:9111739
  6. 6. Francuz P, Zapała D. The suppression of the μ rhythm during the creation of imagery representation of movement. Neurosci Lett. 2011;495(1):39–43. pmid:21406213
  7. 7. Tsuchimoto S, Shindo K, Hotta F, Hanakawa T, Liu M, Ushiba J. Sensorimotor connectivity after motor exercise with neurofeedback in post-stroke patients with hemiplegia. Neuroscience. 2019;416:109–25. pmid:31356896
  8. 8. Soekadar SR, Witkowski M, Birbaumer N, Cohen LG. Enhancing Hebbian Learning to Control Brain Oscillatory Activity. Cereb Cortex. 2015;25(9):2409–15. pmid:24626608
  9. 9. Remsik AB, Williams L Jr, Gjini K, Dodd K, Thoma J, Jacobson T, et al. Ipsilesional Mu Rhythm Desynchronization and Changes in Motor Behavior Following Post Stroke BCI Intervention for Motor Rehabilitation. Front Neurosci. 2019;13:53. pmid:30899211
  10. 10. Bartur G, Pratt H, Soroker N. Changes in mu and beta amplitude of the EEG during upper limb movement correlate with motor impairment and structural damage in subacute stroke. Clin Neurophysiol. 2019;130(9):1644–51. pmid:31326646
  11. 11. Casson AJ. Wearable EEG and beyond. Biomed Eng Lett. 2019;9(1):53–71. pmid:30956880
  12. 12. Debener S, Emkes R, De Vos M, Bleichner M. Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Sci Rep. 2015;5:16743. pmid:26572314
  13. 13. Mikkelsen KB, Kappel SL, Mandic DP, Kidmose P. EEG Recorded from the Ear: Characterizing the Ear-EEG Method. Front Neurosci. 2015;9:438. pmid:26635514
  14. 14. Bleichner MG, Debener S. Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG. Front Hum Neurosci. 2017;11:163. pmid:28439233
  15. 15. Liao L-D, Chen C-Y, Wang I-J, Chen S-F, Li S-Y, Chen B-W, et al. Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors. J Neuroeng Rehabil. 2012;9:5. pmid:22284235
  16. 16. Mihajlovic V, Grundlehner B, Vullers R, Penders J. Wearable, wireless EEG solutions in daily life applications: what are we missing? IEEE J Biomed Health Inform. 2015;19(1):6–21. pmid:25486653
  17. 17. Ueno K, Ishii R, Ueda M, Yuri T, Shiroma C, Hata M, et al. Frontal midline theta rhythm and gamma activity measured by sheet-type wearable EEG device. Front Hum Neurosci. 2023;17:1145282. pmid:36992791
  18. 18. Athavipach C, Pan-Ngum S, Israsena P. A Wearable In-Ear EEG Device for Emotion Monitoring. Sensors (Basel). 2019;19(18):4014. pmid:31533329
  19. 19. Kidmose P, Looney D, Ungstrup M, Rank ML, Mandic DP. A study of evoked potentials from ear-EEG. IEEE Trans Biomed Eng. 2013;60(10):2824–30. pmid:23722447
  20. 20. Looney D, Kidmose P, Park C, Ungstrup M, Rank M, Rosenkranz K, et al. The in-the-ear recording concept: user-centered and wearable brain monitoring. IEEE Pulse. 2012;3(6):32–42. pmid:23247157
  21. 21. Zibrandtsen IC, Kidmose P, Christensen CB, Kjaer TW. Ear-EEG detects ictal and interictal abnormalities in focal and generalized epilepsy - A comparison with scalp EEG monitoring. Clin Neurophysiol. 2017;128(12):2454–61. pmid:29096220
  22. 22. Mandekar S, Holland A, Thielen M, Behbahani M, Melnykowycz M. Advancing towards Ubiquitous EEG, Correlation of In-Ear EEG with Forehead EEG. Sensors (Basel). 2022;22(4):1568. pmid:35214468
  23. 23. Niso G, Romero E, Moreau JT, Araujo A, Krol LR. Wireless EEG: A survey of systems and studies. Neuroimage. 2023;269:119774. pmid:36566924
  24. 24. Stancák A Jr, Pfurtscheller G. The effects of handedness and type of movement on the contralateral preponderance of mu-rhythm desynchronisation. Electroencephalogr Clin Neurophysiol. 1996;99(2):174–82. pmid:8761053
  25. 25. Leocani L, Cohen LG, Wassermann EM, Ikoma K, Hallett M. Human corticospinal excitability evaluated with transcranial magnetic stimulation during different reaction time paradigms. Brain. 2000;123(Pt 6):1161–73. pmid:10825355
  26. 26. Thomson DJ. Spectrum estimation and harmonic analysis. Proc IEEE. 1982;70(9):1055–96.
  27. 27. Mitra PP, Pesaran B. Analysis of dynamic brain imaging data. Biophys J. 1999;76(2):691–708. pmid:9929474
  28. 28. Hoechstetter K, Bornfleth H, Weckesser D, Ille N, Berg P, Scherg M. BESA source coherence: a new method to study cortical oscillatory coupling. Brain Topogr. 2004;16(4):233–8. pmid:15379219
  29. 29. Ueda M, Ueno K, Inamoto T, Shiroma C, Hata M, Ishii R, et al. Parietal Gamma Band Oscillation Induced by Self-Hand Recognition. Brain Sci. 2022;12(2):272. pmid:35204035
  30. 30. Maris E, Oostenveld R. Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods. 2007;164(1):177–90. pmid:17517438
  31. 31. Maris E. Statistical testing in electrophysiological studies. Psychophysiology. 2012;49(4):549–65. pmid:22176204
  32. 32. Pfurtscheller G, Neuper C. Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man. Neurosci Lett. 1994;174(1):93–6. pmid:7970165
  33. 33. Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842–57. pmid:10576479
  34. 34. van den Broek SP, Reinders F, Donderwinkel M, Peters MJ. Volume conduction effects in EEG and MEG. Electroencephalogr Clin Neurophysiol. 1998;106(6):522–34. pmid:9741752
  35. 35. Vorwerk J, Cho J-H, Rampp S, Hamer H, Knösche TR, Wolters CH. A guideline for head volume conductor modeling in EEG and MEG. Neuroimage. 2014;100:590–607. pmid:24971512
  36. 36. Baumann SB, Wozny DR, Kelly SK, Meno FM. The electrical conductivity of human cerebrospinal fluid at body temperature. IEEE Trans Biomed Eng. 1997;44: (3):220–3. pmid:9216137
  37. 37. Mizuhara H. Cortical dynamics of human scalp EEG origins in a visually guided motor execution. Neuroimage. 2012;62(3):1884–95. pmid:22659479
  38. 38. Zhao M, Marino M, Samogin J, Swinnen SP, Mantini D. Hand, foot and lip representations in primary sensorimotor cortex: a high-density electroencephalography study. Sci Rep. 2019;9(1):19464. pmid:31857602
  39. 39. Neuper C, Pfurtscheller G. Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. Int J Psychophysiol. 2001;43(1):41–58. pmid:11742684
  40. 40. Leocani L, Toro C, Zhuang P, Gerloff C, Hallett M. Event-related desynchronization in reaction time paradigms: a comparison with event-related potentials and corticospinal excitability. Clin Neurophysiol. 2001;112(5):923–30. pmid:11336910
  41. 41. Pfurtscheller G, Pregenzer M, Neuper C. Visualization of sensorimotor areas involved in preparation for hand movement based on classification of mu and central beta rhythms in single EEG trials in man. Neurosci Lett. 1994;181(1–2):43–6. pmid:7898767
  42. 42. Zich C, Debener S, Kranczioch C, Bleichner MG, Gutberlet I, De Vos M. Real-time EEG feedback during simultaneous EEG-fMRI identifies the cortical signature of motor imagery. Neuroimage. 2015;114:438–47. pmid:25887263
  43. 43. Pfurtscheller G, Berghold A. Patterns of cortical activation during planning of voluntary movement. Electroencephalogr Clin Neurophysiol. 1989;72(3):250–8. pmid:2465128
  44. 44. Derambure P, Dujardin K, Defebvre L, Bourriez JL, Jacquesson JM, Guieu JD. Spatiotemporal study of event-related desynchronization during self-paced movement. Neurophysiol Clin. 1993;23(4):337–51. pmid:8332109
  45. 45. Leocani L, Toro C, Manganotti P, Zhuang P, Hallett M. Event-related coherence and event-related desynchronization/synchronization in the 10 Hz and 20 Hz EEG during self-paced movements. Electroencephalogr Clin Neurophysiol. 1997;104(3):199–206. pmid:9186234
  46. 46. Honaga E, Ishii R, Kurimoto R, Canuet L, Ikezawa K, Takahashi H, et al. Post-movement beta rebound abnormality as indicator of mirror neuron system dysfunction in autistic spectrum disorder: an MEG study. Neurosci Lett. 2010;478(3):141–5. pmid:20452402
  47. 47. Shahid S, Sinha RK, Prasad G. Mu and beta rhythm modulations in motor imagery related post-stroke EEG: a study under BCI framework for post-stroke rehabilitation. BMC Neurosci. 2010;11(S1).
  48. 48. Maura RM, Rueda Parra S, Stevens RE, Weeks DL, Wolbrecht ET, Perry JC. Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability. J Neuroeng Rehabil. 2023;20(1):21. pmid:36793077
  49. 49. Yi W, Qiu S, Qi H, Zhang L, Wan B, Ming D. EEG feature comparison and classification of simple and compound limb motor imagery. J Neuroeng Rehabil. 2013;10:106. pmid:24119261
  50. 50. Ishii R, Shinosaki K, Ukai S, Inouye T, Ishihara T, Yoshimine T, et al. Medial prefrontal cortex generates frontal midline theta rhythm. NeuroReport. 1999;10(4):675–9.
  51. 51. Ishii R, Canuet L, Ishihara T, Aoki Y, Ikeda S, Hata M, et al. Frontal midline theta rhythm and gamma power changes during focused attention on mental calculation: an MEG beamformer analysis. Front Hum Neurosci. 2014;8:406. pmid:24966825
  52. 52. Hasselmo ME, Hay J, Ilyn M, Gorchetchnikov A. Neuromodulation, theta rhythm and rat spatial navigation. Neural Netw. 2002;15(4–6):689–707. pmid:12371520
  53. 53. Halgren E, Kaestner E, Marinkovic K, Cash SS, Wang C, Schomer DL, et al. Laminar profile of spontaneous and evoked theta: Rhythmic modulation of cortical processing during word integration. Neuropsychologia. 2015;76:108–24. pmid:25801916
  54. 54. Afzal A, Sedghizadeh MJ, Aghajan H, Vahabi Z. Gamma phase synchronization during olfactory stimulation as a marker for Alzheimer’s disease. Alzheimer’s & Dementia. 2021;17(S5).
  55. 55. M Aghajan Z, Schuette P, Fields TA, Tran ME, Siddiqui SM, Hasulak NR, et al. Theta Oscillations in the Human Medial Temporal Lobe during Real-World Ambulatory Movement. Curr Biol. 2017;27(24):3743–51.e3. pmid:29199073
  56. 56. Ketenci S, Kayikcioglu T. Investigation of Theta Rhythm Effect in Detection of Finger Movement. J Exp Neurosci. 2019;13:1179069519828737. pmid:30814845
  57. 57. Smith PL, Little DR. Small is beautiful: In defense of the small-N design. Psychon Bull Rev. 2018;25(6):2083–101. pmid:29557067
  58. 58. Picciotto M. Analytical Transparency and Reproducibility in Human Neuroimaging Studies. J Neurosci. 2018;38(14):3375–6. pmid:29618543
  59. 59. Friston K. Ten ironic rules for non-statistical reviewers. Neuroimage. 2012;61(4):1300–10. pmid:22521475