Implicit mechanisms of intention.

High-level cortical regions encode motor decisions before or even absent awareness, suggesting that neural processes predetermine behavior before conscious choice. Such early neural encoding challenges popular conceptions of human agency. It also raises fundamental questions for brain-machine interfaces (BMIs) that traditionally assume that neural activity reflects the user's conscious intentions.

Preservation of Partially Mixed Selectivity in Human Posterior Parietal Cortex across Changes in Task Context.

Recent studies in posterior parietal cortex (PPC) have found multiple effectors and cognitive strategies represented within a shared neural substrate in a structure termed "partially mixed selectivity" (Zhang et al., 2017). In this study, we examine whether the structure of these representations is preserved across changes in task context and is thus a robust and generalizable property of the neural population.

Partially Mixed Selectivity in Human Posterior Parietal Association Cortex.

To clarify the organization of motor representations in posterior parietal cortex, we test how three motor variables (body side, body part, cognitive strategy) are coded in the human anterior intraparietal cortex. All tested movements were encoded, arguing against strict anatomical segregation of effectors. Single units coded for diverse conjunctions of variables, with different dimensions anatomically overlapping.

Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task.

Objective: To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control.

A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback.

Objective: Present day cortical brain-machine interfaces (BMIs) have made impressive advances using decoded brain signals to control extracorporeal devices. Although BMIs are used in a closed-loop fashion, sensory feedback typically is visual only. However medical case studies have shown that the loss of somesthesis in a limb greatly reduces the agility of the limb even when visual feedback is available.