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

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 features. Here, we examine whether higher-order cross-frequency dynamics distinguish motor planning from execution during natural reach-to-grasp behavior. EEG was recorded in a cue-based paradigm during executed precision and power grips, enabling stage-resolved analysis of preparatory and execution-related neural activity. Cross-frequency bispectral analysis was used to compute bicoherence matrices across canonical frequency band pairs, from which magnitude- and phase-based features were extracted. Classification, permutation-based feature selection, and within-subject statistical testing showed that execution is characterized by substantially stronger and more discriminative nonlinear coupling than planning, with dominant contributions from beta- and gamma-driven interactions. In contrast, decoding of precision versus power grips achieved comparable performance during planning and execution, indicating that grasp-type representations emerge during planning and persist into execution. Spatial and spectral analyses further revealed that informative bispectral features reflect coordinated activity across prefrontal, central, and occipital regions. Despite substantial feature redundancy, effective dimensionality reduction preserved decoding performance. Together, these findings demonstrate that nonlinear cross-frequency coupling provides an interpretable and robust marker of motor planning and execution, extending bispectral EEG analysis to ecologically valid grasping and supporting its relevance for brain-computer interfaces and neuroprosthetic control.

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

Shared autonomy systems require principled methods for inferring user intent and determining appropriate assistance levels. This is a central challenge in human-robot interaction, where systems must be successful while being mindful of user agency. Previous approaches relied on static blending ratios or separated goal inference from assistance arbitration, leading to suboptimal performance in unstructured environments. We introduce BRACE (Bayesian Reinforcement Assistance with Context Encoding), a novel framework that fine-tunes Bayesian intent inference and context-adaptive assistance through an architecture enabling end-to-end gradient flow between intent inference and assistance arbitration. Our pipeline conditions collaborative control policies on environmental context and complete goal probability distributions. We provide analysis showing (1) optimal assistance levels should decrease with goal uncertainty and increase with environmental constraint severity, and (2) integrating belief information into policy learning yields a quadratic expected regret advantage over sequential approaches. We validated our algorithm against SOTA methods (IDA, DQN) using a three-part evaluation progressively isolating distinct challenges of end-effector control: (1) core human-interaction dynamics in a 2D human-in-the-loop cursor task, (2) non-linear dynamics of a robotic arm, and (3) integrated manipulation under goal ambiguity and environmental constraints. We demonstrate improvements over SOTA, achieving 6.3% higher success rates and 41% increased path efficiency, and 36.3% success rate and 87% path efficiency improvement over unassisted control. Our results confirmed that integrated optimization is most beneficial in complex, goal-ambiguous scenarios, and is generalizable across robotic domains requiring goal-directed assistance, advancing the SOTA for adaptive shared autonomy.

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

Current invasive assistive technologies are designed to infer high-dimensional motor control signals from severely paralyzed patients. However, they face significant challenges, including public acceptance, limited longevity, and barriers to commercialization. Meanwhile, noninvasive alternatives often rely on artifact-prone signals, require lengthy user training, and struggle to deliver robust high-dimensional control for dexterous tasks. To address these issues, this study introduces a novel human-centered multimodal AI approach as intelligent compensatory mechanisms for lost motor functions that could potentially enable patients with severe paralysis to control high-dimensional assistive devices, such as dexterous robotic arms, using limited and noninvasive inputs. In contrast to the current state-of-the-art (SoTA) noninvasive approaches, our context-aware, multimodal shared-autonomy framework integrates deep reinforcement learning algorithms to blend limited low-dimensional user input with real-time environmental perception, enabling adaptive, dynamic, and intelligent interpretation of human intent for complex dexterous manipulation tasks, such as pick-and-place. The results from our ARAS (Adaptive Reinforcement learning for Amplification of limited inputs in Shared autonomy) trained with synthetic users over 50,000 computer simulation episodes demonstrated the first successful implementation of the proposed closed-loop human-in-the-loop paradigm, outperforming the SoTA shared autonomy algorithms. Following a zero-shot sim-to-real transfer, ARAS was evaluated on 23 human subjects, demonstrating high accuracy in dynamic intent detection and smooth, stable 3D trajectory control for dexterous pick-and-place tasks. ARAS user study achieved a high task success rate of 92.88%, with short completion times comparable to those of SoTA invasive assistive technologies.