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1. Flexion and Extension Capable Motor Tendon Actuated Exosuit Glove with Open Palm

   Patterson, G.; Zell, T.; Parker, D.; Grieve, J.; Watson, S.; Vikas, V. (August 2019, Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Patients suffering from medical conditions resulting in hand impairment experience difficulty in performing simple daily tasks, like getting dressed or using a pencil, resulting in a poorer quality of life. Rehabilitation attempts to help such individuals regain a sense of control and normalcy. In this context, recent advances in robotics have manifested in multiple designs of hand exoskeletons and exosuit gloves for assistance and rehabilitation. These designs are typically actuated using pneumatic, shape memory alloys and motor-tendon actuators. The proposed Motor Tendon Actuated Exosuit Glove (MTAEG) with an open palm is a soft material glove capable of both flexion and extension of all four fingers of the human hand. Its minimally invasive design maintains an open palm to facilitate haptic and tactile interaction with the environment. The MTAEG achieves flexion-extension motion with joint angles of 45° at the metacarpal joint which is 57% of the desired motion; 90° at the proximal interphalangeal joint which is 100% of the desired motion; and 50° at the distal interphalangeal joint which is 96% of the desired motion. The paper discusses the challenges in achieving the desired motion without the ability to directly model human tendons, and the inability to actuate joints individually. 

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2. Merging fNIRS-EEG Brain Monitoring and Body Motion Capture to Distinguish Parkinson’s Disease

   https://doi.org/10.1109/TNSRE.2020.2987888  

   Abtahi, Mohammadreza; Borgheai, Seyed Bahram; Jafari, Roohollah; Constant, Nicholas; Diouf, Rassoul; Shahriari, Yalda; Mankodiya, Kunal (January 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering)

   Functional connectivity between the brain and body kinematics has largely not been investigated due to the requirement of motionlessness in neuroimaging techniques such as functional magnetic resonance imaging (fMRI). However, this connectivity is disrupted in many neurodegenerative disorders, including Parkinson’s Disease (PD), a neurological progressive disorder characterized by movement symptoms including slowness of movement, stiffness, tremors at rest, and walking and standing instability. In this study, brain activity is recorded through functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), and body kinematics were captured by a motion capture system (Mocap) based on an inertial measurement unit (IMU) for gross movements (large movements such as limb kinematics), and the WearUp glove for fine movements (small range movements such as finger kinematics). PD and neurotypical (NT) participants were recruited to perform 8 different movement tasks. The recorded data from each modality have been analyzed individually, and the processed data has been used for classification between the PD and NT groups. The average changes in oxygenated hemoglobin (HbO2) from fNIRS, EEG power spectral density in the Theta, Alpha, and Beta bands, acceleration vector from Mocap, and normalized WearUp flex sensor data were used for classification. 12 different support vector machine (SVM) classifiers have been used on different datasets such as only fNIRS data, only EEG data, hybrid fNIRS/EEG data, and all the fused data for two classification scenarios: classifying PD and NT based on individual activities, and all activity data fused together. The PD and NT group could be distinguished with more than 83% accuracy for each individual activity. For all the fused data, the PD and NT groups are classified with 81.23%, 92.79%, 92.27%, and 93.40% accuracy for the fNIRS only, EEG only, hybrid fNIRS/EEG, and all fused data, respectively. The results indicate that the overall performance of classification in distinguishing PD and NT groups improves when using both brain and body data. 

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3. Integrating Electroencephalography Source Localization and Residual Convolutional Neural Network for Advanced Stroke Rehabilitation

   https://doi.org/10.3390/bioengineering11100967  

   Kaviri, Sina Makhdoomi; Vinjamuri, Ramana (October 2024, Bioengineering)

   Motor impairments caused by stroke significantly affect daily activities and reduce quality of life, highlighting the need for effective rehabilitation strategies. This study presents a novel approach to classifying motor tasks using EEG data from acute stroke patients, focusing on left-hand motor imagery, right-hand motor imagery, and rest states. By using advanced source localization techniques, such as Minimum Norm Estimation (MNE), dipole fitting, and beamforming, integrated with a customized Residual Convolutional Neural Network (ResNetCNN) architecture, we achieved superior spatial pattern recognition in EEG data. Our approach yielded classification accuracies of 91.03% with dipole fitting, 89.07% with MNE, and 87.17% with beamforming, markedly surpassing the 55.57% to 72.21% range of traditional sensor domain methods. These results highlight the efficacy of transitioning from sensor to source domain in capturing precise brain activity. The enhanced accuracy and reliability of our method hold significant potential for advancing brain–computer interfaces (BCIs) in neurorehabilitation. This study emphasizes the importance of using advanced EEG classification techniques to provide clinicians with precise tools for developing individualized therapy plans, potentially leading to substantial improvements in motor function recovery and overall patient outcomes. Future work will focus on integrating these techniques into practical BCI systems and assessing their long-term impact on stroke rehabilitation. 

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4. Uncovering Spatiotemporal Differences in Cortical Activity Corresponding to two Tasks Using Data-Driven ADASSO Algorithm

   https://doi.org/10.1109/ISBI60581.2025.10980883  

   Mai, Siwei; Haddad, Ali E; Najafizadeh, Laleh (April 2025, IEEE)

   Identifying spatiotemporal differences in brain functional dynamics corresponding to two tasks is critical for understanding how specific neural processes contribute to distinct tasks or cognitive functions. Traditional methods rely on imposing assumptions and limits on the location and timing of activities, while machine-learning-based methods generally lack offering interpretable insights. This highlights the need for new data-driven approaches to capture spatial and temporal differences in brain activity between two tasks, while also providing interpretable explanations of the neural processes underlying these differences. In this work, we formulate the problem of finding the spatial and temporal differences in the dynamics of brain function corresponding to two motor imagery (MI) tasks (left hand movement vs right hand movement) as a discriminative discrete basis problem (DDBP). We apply the data-driven asymmetric discriminative associative algorithm (ADASSO) to EEG data collected during these tasks to uncover the key functional components of the brain’s functional dynamics that differentiate between them. Results suggest that hand movements are strongly associated with high confidence activation in the motor cortex, verifying the effectiveness of the ADASSO algorithm in identifying the location and timing of cortical activities that distinguish between the two task classes. 

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5. Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals

   https://doi.org/10.3390/s22145349  

   Pei, Dingyi; Olikkal, Parthan; Adali, Tülay; Vinjamuri, Ramana (July 2022, Sensors)

   Brain-machine interfaces (BMIs) have become increasingly popular in restoring the lost motor function in individuals with disabilities. Several research studies suggest that the CNS may employ synergies or movement primitives to reduce the complexity of control rather than controlling each DoF independently, and the synergies can be used as an optimal control mechanism by the CNS in simplifying and achieving complex movements. Our group has previously demonstrated neural decoding of synergy-based hand movements and used synergies effectively in driving hand exoskeletons. In this study, ten healthy right-handed participants were asked to perform six types of hand grasps representative of the activities of daily living while their neural activities were recorded using electroencephalography (EEG). From half of the participants, hand kinematic synergies were derived, and a neural decoder was developed, based on the correlation between hand synergies and corresponding cortical activity, using multivariate linear regression. Using the synergies and the neural decoder derived from the first half of the participants and only cortical activities from the remaining half of the participants, their hand kinematics were reconstructed with an average accuracy above 70%. Potential applications of synergy-based BMIs for controlling assistive devices in individuals with upper limb motor deficits, implications of the results in individuals with stroke and the limitations of the study were discussed. 

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