arXiv subjects

Michelle J. Johnson

Publications and source records attributed to Michelle J. Johnson.

6 recordsLinked to original sources

arXiv2026

Lightweight Adaptation of EEG Foundation Models for Stroke Motor Imagery Decoding: Domain Shift and Subject-Level Robustness

Motor imagery (MI) electroencephalography (EEG) decoding could support post-stroke rehabilitation, but models developed on healthy cohorts may not transfer reliably to pathological EEG. We evaluated whether Low-Rank Adaptation (LoRA) can efficiently adapt three pretrained EEG foundation models (i.e., LaBraM-base, REVE-base, and REVE-large) for binary left- versus right-hand MI decoding. Frozen-backbone head-only baselines and LoRA adaptation were evaluated using subject-wise five-fold cross-validation on the PhysioNet EEG Motor Movement/Imagery Dataset and a binary subset of the UET175 dataset comprising 30 stroke participants. On EEGMMIDB, LoRA increased accuracy to 0.822 for LaBraM-base and 0.957 for REVE-base. On UET175, all head-only models performed near chance. With LoRA, LaBraM-base remained near chance (0.499$\pm$0.009), whereas REVE-base reached 0.847$\pm$0.194 and outperformed REVE-large (0.806$\pm$0.178), indicating that increased model capacity alone did not improve stroke-domain adaptation. The strongest stroke configuration, REVE-base LoRA, was further evaluated using within-cohort leave-one-subject-out cross-validation (LOOCV), showing 0.952 mean accuracy, but subject-wise accuracy ranged from 0.586 to 1.000, revealing a small low-performing tail. Zero-shot transfer from EEGMMIDB to UET175 remained near chance (0.464$\pm$0.072). These findings show that healthy-benchmark performance does not ensure transfer to stroke EEG. Translation of EEG foundation models to pseudo-online or real-time rehabilitation BCIs should therefore include target-domain adaptation and subject-level assessment of temporal informativeness, spatial sensitivity, and physiological discriminability.

cs.CE↗

arXiv2026

Robust EEG Functional Connectivity Metrics for Decoding Action Observation Conditions and Observed Actions

Action observation (AO) paradigms probe motor-system engagement, yet the electroencephalographic (EEG) functional connectivity (FC) metrics that best capture AO-related dynamics remain unclear. This pilot study benchmarked five sensor-level FC metrics, including coherence (COH), imaginary coherence (iCOH), phase-locking value (PLV), partial directed coherence (PDC), and spectral Granger causality (SpcG), for decoding AO stimuli in five healthy adults. EEG signals were recorded while participants observed upper-limb actions performed by human or robot agents, as well as non-action control stimuli. Ten motor-area channels were analyzed in the alpha (8-12 Hz) and beta (13-30 Hz) bands. Trial-wise 10 x 10 FC matrices were used as inputs to multiple classifiers for two tasks: (i) six-class AO-condition decoding and (ii) five-class action-type decoding. Across both tasks, metrics robust to volume conduction consistently outperformed their counterparts. iCOH achieved the highest macro-area under the receiver operating characteristic curve (macro-AUC) for most classifiers, with PDC and SpcG showing comparable performance. Graph neural networks (GNNs) provided the most robust and stable results across all FC metrics, while convolutional neural networks and random forests also performed strongly. These findings highlight the importance of suppressing zero-phase-lag interactions and incorporating directed connectivity when characterizing AO-related brain activity. They further demonstrate the ability of GNNs to exploit the inherent graph structure of FC representations, providing practical guidance for selecting connectivity measures and machine learning models in future large-scale studies of action observation.

cs.CE↗

arXiv2025

Learning Realistic Joint Space Boundaries for Range of Motion Analysis of Healthy and Impaired Human Arms

A realistic human kinematic model that satisfies anatomical constraints is essential for human-robot interaction, biomechanics and robot-assisted rehabilitation. Modeling realistic joint constraints, however, is challenging as human arm motion is constrained by joint limits, inter- and intra-joint dependencies, self-collisions, individual capabilities and muscular or neurological constraints which are difficult to represent. Hence, physicians and researchers have relied on simple box-constraints, ignoring important anatomical factors. In this paper, we propose a data-driven method to learn realistic anatomically constrained upper-limb range of motion (RoM) boundaries from motion capture data. This is achieved by fitting a one-class support vector machine to a dataset of upper-limb joint space exploration motions with an efficient hyper-parameter tuning scheme. Our approach outperforms similar works focused on valid RoM learning. Further, we propose an impairment index (II) metric that offers a quantitative assessment of capability/impairment when comparing healthy and impaired arms. We validate the metric on healthy subjects physically constrained to emulate hemiplegia and different disability levels as stroke patients. [https://sites.google.com/seas.upenn.edu/learning-rom]

cs.RO↗

arXiv2025

Exploring EEG Responses during Observation of Actions Performed by Human Actor and Humanoid Robot

Action observation (AO) therapy is a promising rehabilitative treatment for motor and language function in individuals recovering from neurological conditions, such as stroke. This pilot study aimed to investigate the potential of humanoid robots to support AO therapy in rehabilitation settings. The brain activity of three healthy right-handed participants was monitored with electroencephalography (EEG) while they observed eight different actions performed by two agents, a human actor and a robot, using their left and right arms. Their event-related spectral perturbations (ERSPs, changes in the spectral power of neural oscillations in response to an event or stimulus, compared to baseline) in sensorimotor regions were analyzed. The single-subject analysis showed variability in ERSP patterns among all participants, including power suppression in sensorimotor mu and beta rhythms. One participant showed stronger responses to "robot" AO conditions than to "human" conditions. Strong and positive correlations in ERSP across all conditions were observed for almost all participants and channels, implying common cognitive processes or neural networks at play in the mirror neuron system during AO. The results support the feasibility of using EEG to explore differences in neural responses to observation of robot- and human-induced actions.

cs.CE↗

arXiv2022

Feasibility and Acceptability of Remote Neuromotor Rehabilitation Interactions Using Social Robot Augmented Telepresence: A Case Study

There is a growing need to deliver rehabilitation care to patients remotely. Long term demographic changes, geographic shortages of care providers, and now a global pandemic contribute to this need. Telepresence provides an option for delivering this care. However, telepresence using video and audio alone does not provide an interaction of the same quality as in-person. To bridge this gap, we propose the use of social robot augmented telepresence (SRAT). We have constructed a demonstration SRAT system for upper extremity rehab, in which a humanoid, with a head, body, face, and arms, is attached to a mobile telepresence system, to collaborate with the patient and clinicians as an independent social entity. The humanoid can play games with the patient and demonstrate activities.These activities could be used both to perform assessments in support of self-directed rehab and to perform exercises. In this paper, we present a case series with six subjects who completed interactions with the robot, three subjects who have previously suffered a stroke and three pediatric subjects who are typically developing. Subjects performed a Simon Says activity and a target touch activity in person, using classical telepresence (CT), and using SRAT. Subjects were able to effectively work with the social robot guiding interactions and 5 of 6 rated SRAT better than CT. This study demonstrates the feasibility of SRAT and some of its benefits.

cs.RO↗

arXiv2018

Proceedings of the Workshop on Social Robots in Therapy: Focusing on Autonomy and Ethical Challenges

Robot-Assisted Therapy (RAT) has successfully been used in HRI research by including social robots in health-care interventions by virtue of their ability to engage human users both social and emotional dimensions. Research projects on this topic exist all over the globe in the USA, Europe, and Asia. All of these projects have the overall ambitious goal to increase the well-being of a vulnerable population. Typical work in RAT is performed using remote controlled robots; a technique called Wizard-of-Oz (WoZ). The robot is usually controlled, unbeknownst to the patient, by a human operator. However, WoZ has been demonstrated to not be a sustainable technique in the long-term. Providing the robots with autonomy (while remaining under the supervision of the therapist) has the potential to lighten the therapists burden, not only in the therapeutic session itself but also in longer-term diagnostic tasks. Therefore, there is a need for exploring several degrees of autonomy in social robots used in therapy. Increasing the autonomy of robots might also bring about a new set of challenges. In particular, there will be a need to answer new ethical questions regarding the use of robots with a vulnerable population, as well as a need to ensure ethically-compliant robot behaviours. Therefore, in this workshop we want to gather findings and explore which degree of autonomy might help to improve health-care interventions and how we can overcome the ethical challenges inherent to it.

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