A5061502119- EEG and Brain-Computer Interfaces
- Neurological disorders and treatments
- Neuroscience and Neural Engineering
- Sports Performance and Training
- Sleep and Wakefulness Research
- Sports injuries and prevention
- Lower Extremity Biomechanics and Pathologies
- Time Series Analysis and Forecasting
MRC Brain Network Dynamics Unit
2022-2023
University of Oxford
2022-2023
Islamic Azad University Boroujerd Branch
2018
This work explores the potential utility of neural network classifiers for real- time classification field-potential based biomarkers in next-generation responsive neuromodulation systems. Compared to classical filter-based classifiers, networks offer an ease patient-specific parameter tuning, promising reduce burden programming on clinicians. The paper a compact, feed - forward architecture only dozens units seizure-state refractory epilepsy. proposed classifier offers comparable accuracy...
Sleep Stage Classification (SSC) is a labor-intensive task, requiring experts to examine hours of electrophysiological recordings for manual classification. This limiting factor when it comes leveraging sleep stages therapeutic purposes. With increasing affordability and expansion wearable devices, automating SSC may enable deployment sleep-based therapies at scale. Deep Learning has gained attention as potential method automate this process. Previous research shown accuracy comparable...
Providing clinicians with objective outcomes of neuromodulation therapy is a key unmet need, especially in emerging areas such as epilepsy and mood disorders. These diseases have episodic behavior circadian/multidien rhythm characteristics that are difficult to capture short clinical follow-ups. This work presents preliminary validation evidence for an implantable system integrated physiological event monitoring, initial focus on seizure tracking epilepsy. The was developed address currently...
Sleep Stage Classification (SSC) is a labor-intensive task, requiring experts to examine hours of electrophysiological recordings for manual classification. This limiting factor when it comes leveraging sleep stages therapeutic purposes. With increasing affordability and expansion wearable devices, automating SSC may enable deployment sleep-based therapies at scale. Deep Learning has gained attention as potential method automate this process. Previous research shown accuracy comparable...
Electromyographic Activity of Selected Muscles During Squat Exercise With and Without Upper Limb Assistance
This work explores the potential utility of neural network classifiers for real-time classification field-potential based biomarkers in next-generation responsive neuromodulation systems. Compared to classical filter-based classifiers, networks offer an ease patient-specific parameter tuning, promising reduce burden programming on clinicians. The paper a compact, feed-forward architecture only dozens units seizure-state refractory epilepsy. proposed classifier offers comparable accuracy...