The field of machine learning has played an important role in the development of brain-computer interfaces by providing techniques that can learn to map neural activity to appropriate control commands. Algorithms for machine learning can be broadly divided into two classes: supervised learning and unsupervised learning. In supervised learning, we are given training data that consists of a set of inputs and corresponding outputs. The goal is to learn the underlying function from the training data such that new test inputs are mapped to the correct outputs. If the outputs are discrete classes, the problem is called classification. If the outputs are continuous, the problem is equivalent to regression. Given the emphasis on discovering an underlying function, supervised learning is sometimes also called function approximation. Unsupervised learning, on the other hand, emphasizes discovery of hidden statistical structure in unlabeled data: the training data consists of inputs, which are typically high-dimensional vectors, and the goal is to learn a statistical model that may be compact or useful for subsequent analysis. We have already discussed two prominent unsupervised learning techniques (PCA and ICA) in the previous chapter.

In this chapter, we focus on the two major types of supervised learning techniques: classification and regression. Classification is the problem of assigning one of N labels to a new input signal, given labeled training data consisting of known inputs and their corresponding output labels. Regression is the problem of mapping input signals to a continuous output signal. Many BCIs based on EEG, ECoG, fMRI, and fNIR have relied on classification to generate discrete control outputs (e.g., move a cursor up or down by a small amount). BCIs based on neuronal recordings, on the other hand, have predominantly utilized regression to generate continuous output signals, such as position or velocity signals for a prosthetic device. In general, the choice of whether to use classification or regression when designing a BCI will depend on both the type of brain signal being recorded and the type of application being controlled.