The field of Brain Computer Interfaces reach out to signal processing, machine learning, computational intelligence, neuroscience, and cognitive science. The problems that people experience in BCI are similar to pattern recognition, time-series analysis, control systems, and robotics. Processing of the data depends on unknown parameters which can be person-specific or task-specific. You need to adapt to each specific person. The cortex folds differently for each individual person. This happens even in monozygotic twins. The functional map differs across individuals. Sensor locations can also differ across recording sessions. The brain dynamics are non-stationary at all time scales. They vary minute to minute, or day to day.
There’s a very difficult to deal with signal-to-noise ratio making sense measures hard to obtain. The relevant brain activity is small compared to it’s interfering artifacts and compared to brain background activity.
Large collections of neurons are involved in a lot of different activities, not just one. One functional area of neurons may also be acting for another cognitive function.
There’s also a lot of uncertainty about the brain’s functioning. EEG signals are also mathematically more complicated to handle, because all the sensors record almost the same signal due to the superposition of all brain activity. The signals need to be computationally disentangled for optimum performance. Everything that you need to do needs to be statistical. Sophisticated signal processing is needed to accurately portray the data. BCI systems should also be calibrated before it can be used. Calibration should use as much example data, databases, and prior knowledge about that person that we can get.
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