The user of a BCI device controls it via his or her brain signal. The (EEG or other neuroimaging) signal is analyzed, processed, decoded, and/or classified. The end result is an intended movement.
But, what controls the user’s brain signal? Some might say task determines the signal: its location, frequency, amplitude and other characteristics.
I argue task is a secondary variable. The primary variable affecting the brain is the mind. Brain is influenced heavily by task, for sure. But it’s also affected by the environment, measurement situation, recent life events (good and bad) and other life context. All of these variables affect the mind.
The user’s mind — perception, recognition, meaning, thought, emotion, language, goals, imagination, intention etc. — is affected by many things, including the task. The brain signal thus reflects the mind primarily, task secondarily.
If a user’s mind, or mental processes, are activated optimally, and the brain signal processed and classified accordingly, the desired action will occur. Mind Target Hit → Functional Neural Network Activity → Brain Signal → Processing & Classification → (Desired) External Action.
For example, imagine a user’s mind dominated by a “move the cursor to the left” intention. In this case the corresponding functional neural network activity, and signal, will be classified as “move cursor left,” triggering that action.
What if the mind could be defined more accurately? This would allow more accurate brain signal labeling i.e. classification. Better mind definitions would enable more accurate, precise, and comprehensive classification of these signals.
More importantly, a group could use more optimally-defined mind/brain signatures as “targets.” The entire team — BCI designer, tester, trainer, and user — could use these to improve their performance. For example for the user, the targets would be more clear, precise, and personalized; desirable for the user to strive toward, easier to hit (strongly and consistently), and adjusted for specific movements, situations, and other context.
The contents of the conscious mind, contrary to what many brain scientists might say, are not a mystery. They are our experience of life. All of our awareness, moment by moment, is encapsulated by our mind, as it changes (or stays the same) inside the brain. The good news is these contents can be defined — or at least summarized – quite accurately!
The mind can also be seen, more precisely, as a set of active general memories. For example, when a user tries to moves a cursor to the left, a “me moving a cursor left” memory will activate. This memory is comprised predominately of (visual & somatosensory) perception: of “my arm & hand moving to the left,” a computer screen, a cursor (a range of cursor size & shapes), and cursor moving left (a range of cursor speeds and directions). These mental content ranges will activate as part of the larger memory “me moving a cursor left” (itself a general memory).
A memory range is the same thing as a general memory. For example the general memory “an apple” includes a range of apple characteristics: shapes, sizes, colors, tastes, etc.
The neural correlate of a general memory, I argue, = a functional neural network (FNN) range. Both are highly associative, function — simultaneously — as signal transmitters and receivers, and dominate their respective (mind/brain) systems.
Overall, a state of mind = (mostly) an active general memory set = (mostly) an active set of FNN ranges = (most of) the corresponding brain signal.
The current cognitive neuroscience framework, despite the best efforts of professionals more skilled and knowledgeable than myself, is very limiting when applied to BCI development and use. Working within it yields vague and inaccurate brain signal targets. There is no one’s fault. The brain sciences are comprised of smart, talented people. The problem is they are laboring under the wrong (cognitive neuroscience i.e. mind/brain) paradigm.
Current mind brain targets for BCI control are either ill-defined, or too task-focused. A target might be “move the cursor left” intention (not a bad start, but very incomplete), or “attend to and do whatever you did before to make that action happen” (vague and ill-defined). Or, the target might be 100% task-based, such as “whatever the brain does when the user performs movement X, under conditions Y and Z.”
Task-based targets are sub-optimal because they are much less strongly correlated with the brain signal than the mind. A given state of mind includes not only task influences, but those of environment, situation, recent life history, stage of learning, and other context.
Contrast brain signal labeling based on task with labeling based on mind, defined as a set of general memories. For instance, the intention “move the cursor left” will include the memories “cursor,” “a computer screen,” and “move left.” This intention — a general memory in its own right – is itself comprised of general memories. These are mainly visual and somatosensory. Among these are “seeing an object move left,” and “the feeling of my right hand & mouse as it moves left.”
The above memories also have strong associations. These include attention (“visuosomatosensory focus on the cursor”), visual imaging (“imagine it will move left”), and emotion (“feeling calm and relaxed”).
Why label the brain signal based on task when it can be more accurately labeled based on the mind? In fact, I argue it’s possible to define a memory set — its components and associations — for any common movement & its context. Movement context would include the person, occupation, lifestyle, movement situation (environmental, social, work/home/lab…), and stage of learning (beginner/intermediate/advanced).
Overall, more optimal brain signal targets — based on the mind not just the task — have the potential to greatly enhance BCI design, testing, learning, and use.