activity
20242026
most citedWavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types

5 citations · 10 across the 11 of their papers we have counts for

collaborators

12 papers

eess.SP2026

Cross-Frequency Bispectral EEG Analysis of Reach-to-Grasp Planning and Execution

Sima Ghafoori, Anna Cetera, Ali Rabiee +5

Neural control of grasping arises from nonlinear interactions across multiple brain rhythms, yet EEG-based motor decoding has largely relied on linear, second-order spectral featur…

cs.RO2026

End-to-end Optimization of Belief and Policy Learning in Shared Autonomy Paradigms

MH Farhadi, Ali Rabiee, Sima Ghafoori +3

Shared autonomy systems require principled methods for inferring user intent and determining appropriate assistance levels. This is a central challenge in human-robot interaction,…

eess.SP2025

Macroscopic EEG Reveals Discriminative Low-Frequency Oscillations in Plan-to-Grasp Visuomotor Tasks

Anna Cetera, Sima Ghafoori, Ali Rabiee +3

The vision-based grasping brain network integrates visual perception with cognitive and motor processes for visuomotor tasks. While invasive recordings have successfully decoded lo…

cs.HC2025

Human-Centered Shared Autonomy for Motor Planning, Learning, and Control Applications

MH Farhadi, Ali Rabiee, Sima Ghafoori +3

With recent advancements in AI and computational tools, intelligent paradigms have emerged to enhance fields like shared autonomy and human-machine teaming in healthcare. Advanced…

cs.RO2025

Learning Multimodal AI Algorithms for Amplifying Limited User Input into High-dimensional Control Space

Ali Rabiee, Sima Ghafoori, MH Farhadi +5

Current invasive assistive technologies are designed to infer high-dimensional motor control signals from severely paralyzed patients. However, they face significant challenges, in…

cs.RO2024

STREAMS: An Assistive Multimodal AI Framework for Empowering Biosignal Based Robotic Controls

Ali Rabiee, Sima Ghafoori, Xiangyu Bai +2

End-effector based assistive robots face persistent challenges in generating smooth and robust trajectories when controlled by human's noisy and unreliable biosignals such as muscl…

cs.RO2024

Toward human-centered shared autonomy AI paradigms for human-robot teaming in healthcare

Reza Abiri, Ali Rabiee, Sima Ghafoori +1

With recent advancements in AI and computation tools, intelligent paradigms emerged to empower different fields such as healthcare robots with new capabilities. Advanced AI robotic…

cs.RO2024★ 2 cited

A novel seamless magnetic-based actuating mechanism for end-effector-based robotic rehabilitation platforms

Sima Ghafoori, Ali Rabiee, Maryam Norouzi +2

Rehabilitation robotics continues to confront substantial challenges, particularly in achieving smooth, safe, and intuitive human-robot interactions for upper limb motor training.…

eess.SP2024

Classification of Emerging Neural Activity from Planning to Grasp Execution using a Novel EEG-Based BCI Platform

Anna Cetera, Ali Rabiee, Sima Ghafoori +1

There have been different reports of developing Brain-Computer Interface (BCI) platforms to investigate the noninvasive electroencephalography (EEG) signals associated with plan-to…

eess.SP2024★ 2 cited

A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems

Ali Rabiee, Sima Ghafoori, Anna Cetera +3

This study aims to enhance BCI applications for individuals with motor impairments by comparing the effectiveness of tripolar EEG (tEEG) with conventional EEG. The focus is on inte…

eess.SP2024★ 5 cited

Wavelet Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types

Ali Rabiee, Sima Ghafoori, Anna Cetera +1

This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for…

eess.SP2024★ 1 cited

Bispectrum Analysis of Noninvasive EEG Signals Discriminates Complex and Natural Grasp Types

Sima Ghafoori, Ali Rabiee, Anna Cetera +1

The bispectrum stands out as a revolutionary tool in frequency domain analysis, leaping the usual power spectrum by capturing crucial phase information between frequency components…