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EEG-Based Analysis of Brain Responses in Multi-Modal Human-Robot Interaction: Modulating Engagement

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A robotic task that combines image, word, and movement demands produces measurably higher EEG engagement markers than a motor-only task, with the effect strongest during the planning phase.

desk verdict Clean within-subject EEG contrast between multimodal and motor-only tasks, but the engagement interpretation rests on an untested mapping and relative-power normalization; send to review with required revisions. read the letter →

arxiv 2411.18587 v1 pith:ICKT5JCX submitted 2024-11-27 cs.HC eess.SPq-bio.NC

Suzanne Oliver , Tomoko Kitago , Adam Buchwald , S. Farokh Atashzar This is my paper · ORCID
classification cs.HCeess.SPq-bio.NC
keywords EEGengagementbiomarkershuman-robotinteractionroboticrehabilitationrelativealphapowertheta-alpharatiomulti-modaltaskmotorlearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that combining visual, auditory, cognitive, and motor demands in a single robot-driven task engages the brain more than a motor-only task, and that the effect shows up in objective EEG markers. Fifteen healthy adults performed 100 matching trials, where they moved a robot handle to indicate whether a spoken word matched an on-screen image, and 100 motor-only trials, where they moved the handle to a target. In the one-second planning window before movement, the matching task produced significantly lower relative alpha power across the brain and a significantly higher theta-alpha ratio in every brain region, with the majority of participants following the trend. The matching task also kept its theta-alpha ratio stable across the session, while the motor-only task showed a decline. The authors present this as the first neural evidence that a comprehensive multi-modal robotic intervention increases engagement in healthy subjects, which matters because engagement is thought to drive neuroplasticity and adherence in rehabilitation.

What carries the argument

The argument runs on frequency-band power ratios computed from 64-channel EEG, especially relative power in the alpha band (8-13 Hz) and the theta-alpha ratio (TAR), defined as summed theta power divided by summed alpha power. Relative power is band power divided by total 1-100 Hz power. The plan/movement split is derived from surface EMG movement onset, so the one second before motion captures stimulus processing and the one second after captures execution. Lower relative alpha and higher TAR are treated as engagement markers, and TAR computed separately on the first and last third of trials tracks how engagement changes over time.

What would settle it

A decisive test would run the same protocol while measuring absolute (non-normalized) alpha power plus a subjective engagement rating or behavioral performance metric; if absolute alpha does not differ between tasks, or if perceived engagement fails to track the EEG differences, the central claim would not be supported.

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Extended reading notes

Core claim

The central claim is that EEG biomarkers, particularly relative alpha power, show statistically significant improvements in engagement during the multi-modal matching task compared with the motor-only task. The strongest evidence comes from the planning phase, where relative alpha power is significantly lower and the theta-alpha ratio significantly higher for the matching task across most brain regions, and the theta-alpha ratio remains stable over trials. The authors interpret these changes as greater and more sustained engagement, and they conclude that a robot task integrating image, word, and movement demands can keep users engaged for longer therapy sessions.

Load-bearing premise

The load-bearing premise is that lower relative alpha power and a higher theta-alpha ratio are valid markers of engagement, a mapping taken from prior literature and applied to every result without an independent engagement check in this study.

Editorial extensions

If this is right

  • Multi-modal matching tasks could support longer robotic rehabilitation sessions, because theta-alpha ratio stayed level across 100 trials instead of dropping as it did in the motor-only task.
  • EEG engagement markers could be monitored continuously during therapy, replacing subjective questionnaires that interrupt the session.
  • The planning-phase effect localizes most of the engagement gain to stimulus processing, so task design should emphasize the moment when users compare visual and auditory information.
  • If engagement indeed promotes neuroplasticity, this task format is a promising candidate for combined motor and language rehabilitation in stroke and aphasia populations.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct extension would compare the matching task with a version where the image and spoken word are irrelevant to the movement, isolating task-relevant cognitive processing from mere sensory stimulation.
  • Because relative alpha power is normalized by total 1-100 Hz power, recomputing the analysis with absolute band power would show whether the effect reflects true alpha suppression or a shift in slower bands.
  • The stable theta-alpha ratio over trials could be developed into a control signal for adaptive assist-as-needed controllers that increase difficulty when engagement drops.
  • Patient populations may respond differently, since stroke survivors with language or motor deficits could find the combined task more demanding rather than more engaging.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper presents an EEG study with 15 healthy participants who performed a multi-modal 'Matching' robot task (visual image plus spoken word plus motor response) and a motor-only task using the H-Man haptic robot. The authors compute relative power in the delta, theta, and alpha bands and the theta-alpha ratio (TAR) during a 'planning' phase (1 s before movement onset, identified from sEMG) and a 'movement' phase (1 s after onset), then compare the two tasks across six brain regions. They report significantly lower relative alpha power and higher TAR in the Matching task during the planning phase, and a decline in TAR over the session in the motor-only task but not the Matching task. The central claim is that the multi-modal task increases user engagement, as indexed by these EEG biomarkers.

Significance. If the assumed EEG-to-engagement mapping is valid, the result is a useful proof-of-concept for designing robotic rehabilitation protocols that sustain engagement; the within-subject design, the 100-trial-per-condition protocol, the sEMG-based movement-onset detection, and the multi-region analysis are notable strengths. The subject-level consistency (93-100% of participants following the reported trends for the main planning-phase effects) is also a positive feature. However, the central inference depends on an external mapping from relative alpha suppression and TAR increases to engagement, and the relative-power normalization creates a specific artifact risk that the paper does not address. The significance is therefore conditional on validation (absolute-power analysis or a behavioral/subjective engagement measure) that the manuscript does not currently provide.

major comments (3)
  1. [Section II-B-3, Figs. 2-5] The definition of relative power in Section II-B-3 (band PSD summed and divided by total 1-100 Hz PSD) means that a broadband increase in total power during the Matching task, for example from visual and speech processing or general arousal, would mechanically lower relative alpha power even if absolute alpha power were unchanged or increased. The paper reports only relative power and never reports absolute band power, so the statement in Section III-A that the data 'strongly support the hypothesis that the users are more engaged' is not directly supported by the presented quantities. Please report absolute band power or otherwise demonstrate that the effect is specific to alpha suppression rather than to normalization by total power.
  2. [Introduction, Section III-A, Section IV] The interpretation of every result as 'engagement' rests on the assumption, taken from refs. [26]-[31], that decreased alpha and increased theta/delta/TAR index engagement. The manuscript provides no within-study validation of this mapping: there is no subjective engagement rating, no behavioral performance measure (accuracy or reaction time), and no manipulation check. Because the Matching task differs from the motor-only task in visual, auditory, and cognitive demands, the observed EEG differences may reflect generic sensory or cognitive load rather than engagement per se. Please add at least one validation measure, or substantially temper the conclusion to state that the multi-modal task modulates EEG markers 'associated with' engagement, leaving construct validation as future work.
  3. [Section II-B-4 and Section III] The analysis involves a large number of statistical tests (three frequency bands, five to six brain regions plus whole brain, two phases, and the over-time TAR comparison), but no correction for multiple comparisons is applied; instead, four nominal significance thresholds are reported. Given this multiplicity, the pattern of significant results, particularly in the movement phase, should be interpreted with caution. Please report corrected p-values (e.g., FDR) or explicitly frame the analysis as exploratory before drawing the strong conclusion in Section IV that the biomarkers 'showed increased engagement during the Matching task.'
minor comments (5)
  1. [Section III-A, first paragraph] There is a typo: 'The Motor-Only results are shown on the right, and the Matching results are on the right' should likely read 'on the left' and 'on the right'; please correct the figure description.
  2. [Section II-A] The damping coefficient is given as 125 N/ms; the SI unit for damping is typically N·s/m (or N·s/m), so please check and correct the unit.
  3. [Abstract and Section IV] The phrase 'statistically significant improvements in engagement' conflates statistical significance with a value judgment; consider rephrasing to 'statistically significant increases in EEG engagement markers.'
  4. [Section II-B-1] The text says 'the EEGLAB run ica function was used to identify and remove artifacts'; more precisely, ICA identifies components and then artifact components are selected for removal, so the wording could be clarified.
  5. [General] The paper would benefit from reporting effect sizes or confidence intervals for the key planning-phase comparisons (e.g., mean relative alpha at POz), since the significance thresholds alone do not convey the magnitude of the effects.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the EEG engagement markers are applied from external literature and the reported differences are measured, not fitted or derived from the study's own conclusion.

full rationale

The paper contains no formal derivation chain in which a predicted quantity is equivalent to a fitted input or to an author-defined quantity. Relative alpha, theta, delta, and theta-alpha ratio are computed directly from recorded EEG PSDs, and the statistical comparison between the Matching and Motor-Only tasks is a standard measurement contrast, not a prediction based on fitted parameters. The interpretation that decreased relative alpha and increased theta-alpha ratio indicate engagement is an assumption imported from external references ([26]-[29], [31], [35], [36]), not from the authors' own prior work, and it is not used to construct the reported statistics. The self-citations that appear ([9], [21]) support background claims about motor learning and rehabilitation robotics and are not load-bearing for the EEG-engagement inference. The main weakness is a validity concern rather than circularity: because relative band power is normalized by total 1-100 Hz power, a broadband increase in power during the Matching task could mechanically lower relative alpha even without true alpha suppression, and no within-study subjective or behavioral engagement measure validates the construct mapping. That concern affects interpretation but does not make the result equivalent to its inputs by construction. No circular step can be exhibited from the paper's equations or citation chain, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No model parameters are fitted; the central claim rests on an external EEG-engagement mapping, on relative-power normalization, and on the assumption that sEMG-defined epochs isolate planning from movement. No new physical or theoretical entities are introduced.

assumptions (4)
  • domain assumption Relative alpha power decrease and theta or delta increases index engagement (based on cited prior work).
    The entire inference from EEG to engagement rests on this external mapping; no within-study validation with subjective or behavioral engagement measures is provided. Entered in the Introduction and applied throughout Section III.
  • domain assumption Normalizing band power by total 1-100 Hz power preserves engagement-related effects.
    Section II.B.3 uses relative power throughout. If total power changes differ by band, relative decreases can occur without absolute suppression, so this normalization assumption is load-bearing.
  • domain assumption sEMG-based onset detection marks the boundary between planning and movement phases.
    Section II.B.2 defines one-second planning and movement epochs from peak rate of change in mean sEMG RMS. Variable cognitive delays or onset-detection errors would misalign the EEG epochs across tasks.
  • domain assumption Artifact rejection via kurtosis-based channel removal, interpolation, and ICA does not bias band-power estimates.
    Section II.B.1 relies on standard EEGLAB preprocessing. The assumption that these steps remove noise without removing task-related neural signal is not explicitly tested.

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Pith. "Pith review of EEG-Based Analysis of Brain Responses in Multi-Modal Human-Robot Interaction: Modulating Engagement." pith.science (2026). https://pith.science/paper/ICKT5JCX

@misc{pith2026241118587,
  author       = {Pith},
  title        = {Pith review of: EEG-Based Analysis of Brain Responses in Multi-Modal Human-Robot Interaction: Modulating Engagement},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ICKT5JCX}},
  note         = {Machine review of arXiv:2411.18587}
}
read the original abstract

User engagement, cognitive participation, and motivation during task execution in physical human-robot interaction are crucial for motor learning. These factors are especially important in contexts like robotic rehabilitation, where neuroplasticity is targeted. However, traditional robotic rehabilitation systems often face challenges in maintaining user engagement, leading to unpredictable therapeutic outcomes. To address this issue, various techniques, such as assist-as-needed controllers, have been developed to prevent user slacking and encourage active participation. In this paper, we introduce a new direction through a novel multi-modal robotic interaction designed to enhance user engagement by synergistically integrating visual, motor, cognitive, and auditory (speech recognition) tasks into a single, comprehensive activity. To assess engagement quantitatively, we compared multiple electroencephalography (EEG) biomarkers between this multi-modal protocol and a traditional motor-only protocol. Fifteen healthy adult participants completed 100 trials of each task type. Our findings revealed that EEG biomarkers, particularly relative alpha power, showed statistically significant improvements in engagement during the multi-modal task compared to the motor-only task. Moreover, while engagement decreased over time in the motor-only task, the multi-modal protocol maintained consistent engagement, suggesting that users could remain engaged for longer therapy sessions. Our observations on neural responses during interaction indicate that the proposed multi-modal approach can effectively enhance user engagement, which is critical for improving outcomes. This is the first time that objective neural response highlights the benefit of a comprehensive robotic intervention combining motor, cognitive, and auditory functions in healthy subjects.

Figures

Figures reproduced from arXiv: 2411.18587 by the authors.

Figure 1
Figure 1. Overview of experiment design and processing pipeline, showing (A) the experimental setup, (B) the EEG processing steps and brain regions, and (C) an example of sEMG epoching. torso. All subjects were right-handed and performed the task with their right arm. Subjects were asked to complete a series of trials using the robot to control a cursor on the screen in front of them. These trials were divided into two types:… view at source ↗
Figure 2
Figure 2. Mean relative PSD heatmaps of the brain during the planning phase of the Motor-Only and Matching tasks. Results are shown for (A) delta band, (B) theta band, and (C) alpha band. Heatmaps of the mean (across subjects) relative PSD of each of the three frequency bands during the planning phase are shown in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Boxplots showing relative PSD for different regions of the brain during the planning phase of the Motor-Only and Matching tasks. Results are shown for (A) delta band, (B) theta band, and (C) alpha band. Significant differences, per the paired t-test, are indicated with asterisks. *: p < 0.05, **: p < 0.01, ***: p < 0.001, and ****: p < 0.0001. processing and attention [41]. Overall, the higher relative PSD in the de… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Mean relative PSD heatmaps of the brain during the movement phase of the Motor-Only and Matching tasks. Results are shown for (A) delta band, (B) theta band, and (C) alpha band. phase, this is not the case for the movement phase. In fact, in this phase, the relative th…
Figure 5
Figure 5. Figure 5: Boxplots showing relative PSD for different regions of the brain during the movement phase of the Motor-Only and Matching tasks. Results are shown for (A) delta band, (B) theta band, and (C) alpha band. Significant differences, per the paired t-test, are indicated with…
Figure 6
Figure 6. Figure 6: Boxplots and mean heatmaps for Theta-Alpha Ratios of the Motor-Only and Matching task for (A) the planning phase and (B) the movement phase. In the boxplots, significant differences, per the Wilcoxon signed-rank test, are indicated with asterisks. *: p < 0.05, **: p < …
Figure 7
Figure 7. Figure 7: Boxplots comparing the Theta-Alpha Ratios for the first third of trials and the last third of trials for both the Motor-Only and Matching tasks. The results are shown for (A) the planning phase and (B) the movement phase. Significant differences (p < 0.05) are indicati…

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Reference graph

Works this paper leans on

43 extracted references · 43 canonical work pages

  1. [26]

    Detecting fluctuations in student engagement and retention during video lectures using electroencephalography,

    I. Davidesco, N. Glaser, I. H. Stevenson, and O. Dagan, “Detecting fluctuations in student engagement and retention during video lectures using electroencephalography,” British Journal of Educational Technol- ogy, vol. 54, no. 6, pp. 1895–1916, 2023

    work page 1916

  2. [31]

    Single-channel eeg measurement of engagement in virtual rehabilitation: A validation study,

    J. M. Rogers, J. Jensen, J. T. Valderrama, S. J. Johnstone, and P. H. Wilson, “Single-channel eeg measurement of engagement in virtual rehabilitation: A validation study,” Virtual Reality , vol. 25, no. 2, pp. 357–366, 2021

    work page 2021

  3. [1]

    2024 heart disease and stroke statistics: a report of us and global data from the american heart association,

    S. S. Martin, A. W. Aday, Z. I. Almarzooq, C. A. Anderson, P. Arora, C. L. Avery, C. M. Baker-Smith, B. Barone Gibbs, A. Z. Beaton, A. K. Boehme, et al. , “2024 heart disease and stroke statistics: a report of us and global data from the american heart association,” Circulation, vol. 149, no. 8, pp. e347–e913, 2024

    work page 2024

  4. [2]

    Projected global trends in ischemic stroke incidence, deaths and disability-adjusted life years from 2020 to 2030,

    L. Pu, L. Wang, R. Zhang, T. Zhao, Y . Jiang, and L. Han, “Projected global trends in ischemic stroke incidence, deaths and disability-adjusted life years from 2020 to 2030,” Stroke, vol. 54, no. 5, pp. 1330–1339, 2023

    work page 2020

  5. [3]

    A survey on robotic devices for upper limb rehabilita- tion,

    P. Maciejasz, J. Eschweiler, K. Gerlach-Hahn, A. Jansen-Troy, and S. Leonhardt, “A survey on robotic devices for upper limb rehabilita- tion,” Journal of neuroengineering and rehabilitation, vol. 11, pp. 1–29, 2014

    work page 2014

  6. [4]

    Robot assistance of motor learning: A neuro- cognitive perspective,

    H. Heuer and J. Luettgen, “Robot assistance of motor learning: A neuro- cognitive perspective,” Neuroscience & Biobehavioral Reviews, vol. 56, pp. 222–240, 2015

    work page 2015

  7. [5]

    Haptic adaptive feedback to promote motor learning with a robotic ankle exoskeleton integrated with a video game,

    G. As ´ın-Prieto, A. Mart ´ınez-Exp´osito, F. O. Barroso, E. J. Urendes, J. Gonzalez-Vargas, F. S. Alnajjar, C. Gonz´alez-Alted, S. Shimoda, J. L. Pons, and J. C. Moreno, “Haptic adaptive feedback to promote motor learning with a robotic ankle exoskeleton integrated with a video game,” Frontiers in Bioengineering and Biotechnology , vol. 8, p. 113, 2020

    work page 2020

  8. [6]

    Designing robots that challenge to optimize motor learning,

    D. A. Brown, T. D. Lee, D. J. Reinkensmeyer, and J. E. Duarte, “Designing robots that challenge to optimize motor learning,” Neurore- habilitation technology, pp. 39–58, 2016

    work page 2016

Show all 43 references
  1. [7]

    Expanding stroke telerehabilitation services to rural veterans: a qualitative study on patient experiences using the robotic stroke therapy delivery and monitoring system program,

    C. O. Cherry, N. R. Chumbler, K. Richards, A. Huff, D. Wu, L. M. Tilghman, and A. Butler, “Expanding stroke telerehabilitation services to rural veterans: a qualitative study on patient experiences using the robotic stroke therapy delivery and monitoring system program,” Disab...

    2017

  2. [8]

    Robotic home-based rehabilitation systems design: from a literature review to a conceptual framework for community-based remote therapy during covid-19 pan- demic,

    A. Akbari, F. Haghverd, and S. Behbahani, “Robotic home-based rehabilitation systems design: from a literature review to a conceptual framework for community-based remote therapy during covid-19 pan- demic,” Frontiers in Robotics and AI , vol. 8, p. 612331, 2021

    2021

  3. [9]

    Motor learning principles for neuroreha- bilitation,

    T. Kitago and J. W. Krakauer, “Motor learning principles for neuroreha- bilitation,” Handbook of clinical neurology, vol. 110, pp. 93–103, 2013

    2013

  4. [10]

    Robot assisted training for the upper limb after stroke (ratuls): a multicentre randomised controlled trial,

    H. Rodgers, H. Bosomworth, H. I. Krebs, F. van Wijck, D. Howel, N. Wilson, L. Aird, N. Alvarado, S. Andole, D. L. Cohen, et al., “Robot assisted training for the upper limb after stroke (ratuls): a multicentre randomised controlled trial,” The Lancet, vol. 394, no. 10192, pp. ...

    2019

  5. [11]

    Upper limb robots for recovery of motor arm function in patients with stroke: A systematic review and meta-analysis,

    L. De Iaco, J. M. Veerbeek, J. C. Ket, and G. Kwakkel, “Upper limb robots for recovery of motor arm function in patients with stroke: A systematic review and meta-analysis,” Neurology, vol. 103, no. 2, p. e209495, 2024

    2024

  6. [12]

    Current trends in robot-assisted upper-limb stroke rehabilitation: pro- moting patient engagement in therapy,

    A. A. Blank, J. A. French, A. U. Pehlivan, and M. K. O’Malley, “Current trends in robot-assisted upper-limb stroke rehabilitation: pro- moting patient engagement in therapy,” Current physical medicine and rehabilitation reports, vol. 2, pp. 184–195, 2014

    2014

  7. [13]

    Role of voluntary drive in encoding an elementary motor memory,

    A. Kaelin-Lang, L. Sawaki, and L. G. Cohen, “Role of voluntary drive in encoding an elementary motor memory,” Journal of neurophysiology, vol. 93, no. 2, pp. 1099–1103, 2005

    2005

  8. [14]

    Facili- tating neurorehabilitation through principles of engagement,

    M. M. Danzl, N. M. Etter, R. D. Andreatta, and P. H. Kitzman, “Facili- tating neurorehabilitation through principles of engagement,” Journal of allied health, vol. 41, no. 1, pp. 35–41, 2012

    2012

  9. [15]

    Towards effective neurore- habilitation for stroke patients,

    J. Krupinski, J. Secades, and R. Shiraliyeva, “Towards effective neurore- habilitation for stroke patients,” Int J Phys Med Rehabil , vol. 2, p. 2, 2014

    2014

  10. [16]

    Developing connections for engagement in stroke rehabilita- tion,

    N. M. Kayes, C. Cummins, K. M. McPherson, L. Worrall, and F. A. Bright, “Developing connections for engagement in stroke rehabilita- tion,” Brain Impairment, vol. 23, no. 1, pp. 42–59, 2022

    2022

  11. [17]

    Emergence of virtual reality as a tool for upper limb rehabilitation: incorporation of motor control and motor learning principles,

    M. F. Levin, P. L. Weiss, and E. A. Keshner, “Emergence of virtual reality as a tool for upper limb rehabilitation: incorporation of motor control and motor learning principles,” Physical therapy, vol. 95, no. 3, pp. 415–425, 2015

    2015

  12. [18]

    Identifying the factors affecting ‘patient engagement’in exercise rehabilitation,

    J. L. Teo, Z. Zheng, and S. R. Bird, “Identifying the factors affecting ‘patient engagement’in exercise rehabilitation,” BMC Sports Science, Medicine and Rehabilitation , vol. 14, no. 1, p. 18, 2022

    2022

  13. [19]

    Video games and rehabilitation: using design principles to enhance engagement in physical therapy,

    K. Lohse, N. Shirzad, A. Verster, N. Hodges, and H. M. Van der Loos, “Video games and rehabilitation: using design principles to enhance engagement in physical therapy,”Journal of neurologic physical therapy, vol. 37, no. 4, pp. 166–175, 2013

    2013

  14. [20]

    Engagement enhancement based on bayesian optimization for adaptive assist-as-needed controller,

    X. Li, H. Zeng, J. Zhang, and A. Song, “Engagement enhancement based on bayesian optimization for adaptive assist-as-needed controller,” IEEE Robotics and Automation Letters , vol. 7, no. 1, pp. 49–56, 2021

    2021

  15. [21]

    Robotics- assisted mirror rehabilitation therapy: A therapist-in-the-loop assist- as-needed architecture,

    M. Shahbazi, S. F. Atashzar, M. Tavakoli, and R. V . Patel, “Robotics- assisted mirror rehabilitation therapy: A therapist-in-the-loop assist- as-needed architecture,” IEEE/ASME Transactions on Mechatronics , vol. 21, no. 4, pp. 1954–1965, 2016

    1954

  16. [22]

    Increasing patient engagement during virtual reality-based motor re- habilitation,

    L. Zimmerli, M. Jacky, L. L ¨unenburger, R. Riener, and M. Bolliger, “Increasing patient engagement during virtual reality-based motor re- habilitation,” Archives of physical medicine and rehabilitation , vol. 94, no. 9, pp. 1737–1746, 2013

    2013

  17. [23]

    Transcranial direct current stimulation vs sham stimulation to treat aphasia after stroke: a randomized clinical trial,

    J. Fridriksson, C. Rorden, J. Elm, S. Sen, M. S. George, and L. Bonilha, “Transcranial direct current stimulation vs sham stimulation to treat aphasia after stroke: a randomized clinical trial,” JAMA neurology , vol. 75, no. 12, pp. 1470–1476, 2018

    2018

  18. [24]

    Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research,

    B. A. Greene, “Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research,” Educational Psychologist, vol. 50, no. 1, pp. 14–30, 2015

    2015

  19. [25]

    Machine learning for motor learning: eeg-based continuous assessment of cognitive engagement for adap- tive rehabilitation robots,

    N. Kumar and K. P. Michmizos, “Machine learning for motor learning: eeg-based continuous assessment of cognitive engagement for adap- tive rehabilitation robots,” in 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) , pp. 521–...

    2020

  20. [27]

    Comparison between concentration and immersion based on eeg analysis,

    S. Lim, M. Yeo, and G. Yoon, “Comparison between concentration and immersion based on eeg analysis,” Sensors, vol. 19, no. 7, p. 1669, 2019. OLIVER et al.: EEG-BASED ANAL YSIS OF BRAIN RESPONSES IN MULTI-MODAL HUMAN-ROBOT INTERACTION: MODULATING ENGAGEMENT 9

    2019

  21. [28]

    Real-time estimation of eeg-based engagement in different tasks,

    A. Natalizio, S. Sieghartsleitner, L. Schreiner, M. Walchshofer, A. Es- posito, J. Scharinger, H. Pretl, P. Arpaia, M. Parvis, J. Sol ´e-Casals, et al., “Real-time estimation of eeg-based engagement in different tasks,” Journal of Neural Engineering , vol. 21, no. 1, p. 016014, 2024

    2024

  22. [29]

    Eeg alpha power is modulated by attentional changes during cognitive tasks and virtual reality immersion,

    E. Magosso, F. De Crescenzio, G. Ricci, S. Piastra, and M. Ursino, “Eeg alpha power is modulated by attentional changes during cognitive tasks and virtual reality immersion,” Computational intelligence and neuroscience, vol. 2019, no. 1, p. 7051079, 2019

    2019

  23. [30]

    I must have missed that: Alpha-band oscillations track attention to spoken language,

    M. Boudewyn and C. Carter, “I must have missed that: Alpha-band oscillations track attention to spoken language,” Neuropsychologia, vol. 117, pp. 148–155, 2018

    2018

  24. [32]

    Eeg power spectral measures of cognitive workload: A meta-analysis,

    S. Chikhi, N. Matton, and S. Blanchet, “Eeg power spectral measures of cognitive workload: A meta-analysis,” Psychophysiology, vol. 59, no. 6, p. e14009, 2022

    2022

  25. [33]

    The functional significance of delta oscillations in cog- nitive processing,

    T. Harmony, “The functional significance of delta oscillations in cog- nitive processing,” Frontiers in integrative neuroscience , vol. 7, p. 83, 2013

    2013

  26. [34]

    Event-related delta and theta synchronization during explicit and implicit emotion processing,

    G. Knyazev, J. Y . Slobodskoj-Plusnin, and A. Bocharov, “Event-related delta and theta synchronization during explicit and implicit emotion processing,” Neuroscience, vol. 164, no. 4, pp. 1588–1600, 2009

    2009

  27. [35]

    Alpha/theta ratio neurofeedback training for attention enhancement in normal developing children: A brief report,

    W. Nan, M. Wan, Y . Jiang, X. Shi, F. Wan, and D. Cai, “Alpha/theta ratio neurofeedback training for attention enhancement in normal developing children: A brief report,” Applied Psychophysiology and Biofeedback , vol. 47, no. 3, pp. 223–229, 2022

    2022

  28. [36]

    Index of theta/alpha ratio to quantify visual- spatial attention in dyslexics using electroencephalogram,

    P. Pavithran, K. Arunkumar, N. G. Seshadri, B. K. Singh, V . Mahesh, and B. Geethanjali, “Index of theta/alpha ratio to quantify visual- spatial attention in dyslexics using electroencephalogram,” in 2019 5th International Conference on Advanced Computing & Communication Syste...

    2019

  29. [37]

    The recommendations for sensors and sensor placement procedures for surface electromyography,

    B. Frericks, “The recommendations for sensors and sensor placement procedures for surface electromyography,” SENIAM deliverable 8, Euro- pean Recommendations for Surface Electromyography, pp. 15–53, 1999

    1999

  30. [38]

    Barbero, R

    M. Barbero, R. Merletti, and A. Rainoldi, Atlas of muscle innervation zones: understanding surface electromyography and its applications . Springer Science & Business Media, 2012

    2012

  31. [39]

    Recommendations for the standardization of lead positions in surface electromyography,

    P. Zipp, “Recommendations for the standardization of lead positions in surface electromyography,” European journal of applied physiology and occupational physiology, vol. 50, no. 1, pp. 41–54, 1982

    1982

  32. [40]

    Eeglab: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis,

    A. Delorme and S. Makeig, “Eeglab: an open source toolbox for analysis of single-trial eeg dynamics including independent component analysis,” Journal of neuroscience methods , vol. 134, no. 1, pp. 9–21, 2004

    2004

  33. [41]

    Functional anatomy of the major lobes,

    L. Sibilla, “Functional anatomy of the major lobes,” Neuroimaging: anatomy meets function , pp. 81–99, 2018

    2018

  34. [42]

    Predicting task-general mind- wandering with eeg,

    C. Y . Jin, J. P. Borst, and M. K. Van Vugt, “Predicting task-general mind- wandering with eeg,” Cognitive, Affective, & Behavioral Neuroscience , vol. 19, pp. 1059–1073, 2019

    2019

  35. [43]

    A systematic review of bilateral upper limb training devices for poststroke rehabilita- tion,

    A. E. van Delden, C. E. Peper, G. Kwakkel, and P. J. Beek, “A systematic review of bilateral upper limb training devices for poststroke rehabilita- tion,” Stroke research and treatment, vol. 2012, no. 1, p. 972069, 2012

    2012

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.