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Implementation
Neurofeedback
Once inserted into and attached to the brain, the N4NO neural dust mote enables neurofeedback, a type of biofeedback to increase a patient’s consciousness of and active control over their neural activity [20]. Using auditory or visual positive or negative feedback, a patient is alerted of desirable and undesirable neural activity in the process of conditioning. This will be used to train motor imagery, improving one’s control over their neuroprosthetic.
Filtration
While the neural dust mote would record neural signals, noise in the form of non-neurological signals can cause difficulty in the translation of signals to action. Filtration is essential for cleaning this data, with band-stop filters removing specific frequency bands from neurological data. In addition, Kalman filters are used for linear filtering and prediction [21], adapting to changes in neural signals to provide estimates of intended movements in real-time.
AI Interpretation
After filtration of the recorded neural signals, artificial neural networks enable its interpretation. The presence of desynchronized mu rhythms, indicative of motor cortex activation [22], can be detected through the use of AI models. Additionally, such models analyze the strength of brain waves, the rate of neuron firing as determined by action potentials (time series), and the rates of certain groups of neuron firing (frequency domain). Together, this data offers insight into a patient’s motor imagery, enabling their movement through analysis of their thoughts.
Impact
The widespread use of N4NO will enable an improved quality of life for neuroprosthetic patients without the dangers of craniotomy. Through the elimination of previous prosthetic designs that inherently hindered natural movements due to their reliance on physical operation as opposed to cognitive control, prosthetic devices utilizing neural dust will provide fluid movements similar to those of natural limbs [23].