Let’s consider the mind during voluntary movement. An intention to perform a particular movement is a central feature of it. Intention is also a common mind/brain signal used to control a device. But what exactly IS a movement intention? Can it be defined with precision? As it fluctuates across people, situations, time, and other variables? And, what about the dozens of other signals (noise, signal, and both) active at the same time?
I argue it’s possible to optimize the definition of any movement intention — if this is done within the context of overall state of mind. A mind (and corresponding brain signal) signature can be defined that is less noisy — i.e. more representative of the target state. A signature can be defined that is easier to repeat across conditions, more desirable (for user and BCI designer), and overall more useful than existing (task-based) signatures.
State of mind, and corresponding brain signal, signatures, or “targets,” can be defined with accuracy and precision. For instance, a movement intention is always accompanied by some state of emotion & arousal (ex: “calm and focused”). Somatosensation and visual perception are also part of any movement intention. To characterize these states of mind as signal, or noise, you first need to define them.
In an ideal world, the user’s state of mind (ex: “I want to pick up my phone — now”) will control the device with skill, ease, and reliability. The key is to look beyond task-based definitions of mind, to the mind as a whole DURING a task. The idea is to define the mind — its contents and function — across people, movement disorders, device types, situations (lab, home, work, devise/object spatial relationships…) and other variables.
The good news is this is do-able! The main components of a movement intention — and the mind overall — can be listed, weighted, and connected. And, its strongest associations can be accounted for (listed, weighted, and connected) as well.