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Funded Ph.D. assistantship for the Fall of 2023 at the University of Nebraska, Omaha (UNO), United States.
Degree offered: Ph.D. in Biomechanics and Kinesiology. The assistantship is research-based. For further information, visit the Biomechanics program website.
Project 1 (BCI-NFT): The evaluation of a brain-computer interface (BCI) neurofeedback training (NFT) system to improve upper limb coordination in the pediatric population with neurological disorders. The System records participants' brain activity via electroencephalography (EEG) while they are attempting to move their upper limbs. The EEG signals are then used to activate sensory feedback in real-time, to stimulate the central nervous system, and thereby, create neuroplasticity. This exploratory study may lead to new neurorehabilitation strategies for children with neurological disorders.
For further detail about BCI-NFT, please read:
Behboodi et al. "Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review." 2022.
Responsibilities:
1) To train individuals with neurological disorders using the BCI-NFT system.
2) To analyze the outcomes of training at biomechanical and cortical levels.
3) To improve the precision and reliability of the system.
Minimum Qualifications:
Proficiency in coding, preferably in MATLAB.
Experience in biomechanical or biological signal (E.g., EEG, EMG, locomotion kinematic) Processing and data analysis.
Basic knowledge of functional anatomy and neuromechanics.
Preferred Qualification:
Brain Imaging; EEG signal processing; Brain-Computer Interfaces; Deep Learning.
Project 2 (NMES): Neuromuscular electrical stimulation (NMES) is an assistive technology in which electrical stimulation is applied to the skin's surface to initiate or augment skeletal muscle contraction through intact peripheral nerves. By eliciting appropriately timed muscle contractions, NMES may compensate for deficits in selective motor control in populations with neurological disorders, and thereby, may lead to more typical lower limb kinematics during walking. Additionally, by recruitment of Golgi tendon organs and muscle spindles, it provides rich sensory feedback via the afferent pathways. Therefore, NMES activates all available components of motor control and impacts cortical excitability, which may further translate into motor learning. The goal of this project is to investigate the therapeutic effects of training with a low-intensity multi-joint NMES-assisted walking system on the gait kinematics and cortical activity of populations with neurological disorders.
For further detail about BCI-NFT, please read:
Behboodi et al. “Use of a novel functional electrical stimulation gait training system in 2 adolescents with cerebral palsy: a case series exploring neurotherapeutic changes.” 2019.
Responsibilities:
1) To train individuals with neurological disorders using the NEMS-assisted walking system.
2) To record EEG signals while participants are training with the device.
3) To analyze the outcomes of training at biomechanical and cortical levels.
4) To improve the precision and reliability of the system.
Minimum Qualifications:
Experience in coding, preferably in LabVIEW or MATLAB.
Experience in biomechanical or biological signal (E.g., EEG, EMG, locomotion kinematic) Processing and data analysis.
Basic knowledge of functional anatomy and neuromechanics.
Preferred Qualification:
Microcontroller programming; Brain Imaging; EEG signal processing; Instrumented Motion Capture.
How to Apply:
Please apply electronically here at UNO's website.
Send a copy of your CV directly to the primary investigator: Ahad Behboodi <ahad.behboodi@NIH.gov>.
Degree offered: Ph.D. in Biomechanics and Kinesiology. The assistantship is research-based. For further information, visit the Biomechanics program website.
Project 1 (BCI-NFT): The evaluation of a brain-computer interface (BCI) neurofeedback training (NFT) system to improve upper limb coordination in the pediatric population with neurological disorders. The System records participants' brain activity via electroencephalography (EEG) while they are attempting to move their upper limbs. The EEG signals are then used to activate sensory feedback in real-time, to stimulate the central nervous system, and thereby, create neuroplasticity. This exploratory study may lead to new neurorehabilitation strategies for children with neurological disorders.
For further detail about BCI-NFT, please read:
Behboodi et al. "Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review." 2022.
Responsibilities:
1) To train individuals with neurological disorders using the BCI-NFT system.
2) To analyze the outcomes of training at biomechanical and cortical levels.
3) To improve the precision and reliability of the system.
Minimum Qualifications:
Proficiency in coding, preferably in MATLAB.
Experience in biomechanical or biological signal (E.g., EEG, EMG, locomotion kinematic) Processing and data analysis.
Basic knowledge of functional anatomy and neuromechanics.
Preferred Qualification:
Brain Imaging; EEG signal processing; Brain-Computer Interfaces; Deep Learning.
Project 2 (NMES): Neuromuscular electrical stimulation (NMES) is an assistive technology in which electrical stimulation is applied to the skin's surface to initiate or augment skeletal muscle contraction through intact peripheral nerves. By eliciting appropriately timed muscle contractions, NMES may compensate for deficits in selective motor control in populations with neurological disorders, and thereby, may lead to more typical lower limb kinematics during walking. Additionally, by recruitment of Golgi tendon organs and muscle spindles, it provides rich sensory feedback via the afferent pathways. Therefore, NMES activates all available components of motor control and impacts cortical excitability, which may further translate into motor learning. The goal of this project is to investigate the therapeutic effects of training with a low-intensity multi-joint NMES-assisted walking system on the gait kinematics and cortical activity of populations with neurological disorders.
For further detail about BCI-NFT, please read:
Behboodi et al. “Use of a novel functional electrical stimulation gait training system in 2 adolescents with cerebral palsy: a case series exploring neurotherapeutic changes.” 2019.
Responsibilities:
1) To train individuals with neurological disorders using the NEMS-assisted walking system.
2) To record EEG signals while participants are training with the device.
3) To analyze the outcomes of training at biomechanical and cortical levels.
4) To improve the precision and reliability of the system.
Minimum Qualifications:
Experience in coding, preferably in LabVIEW or MATLAB.
Experience in biomechanical or biological signal (E.g., EEG, EMG, locomotion kinematic) Processing and data analysis.
Basic knowledge of functional anatomy and neuromechanics.
Preferred Qualification:
Microcontroller programming; Brain Imaging; EEG signal processing; Instrumented Motion Capture.
How to Apply:
Please apply electronically here at UNO's website.
Send a copy of your CV directly to the primary investigator: Ahad Behboodi <ahad.behboodi@NIH.gov>.