Decoding index finger position from EEG using random forests
This work addresses the challenge of extracting detailed motor information from non-invasive brain signals for applications in brain-computer interfaces, though it is incremental as it builds on prior knowledge of EEG decoding.
The study tackled the problem of decoding index finger position from non-invasive EEG recordings, achieving above chance-level accuracy in distinguishing different finger positions using a random forest classifier with leave-one-subject-out cross-validation.
While invasively recorded brain activity is known to provide detailed information on motor commands, it is an open question at what level of detail information about positions of body parts can be decoded from non-invasively acquired signals. In this work it is shown that index finger positions can be differentiated from non-invasive electroencephalographic (EEG) recordings in healthy human subjects. Using a leave-one-subject-out cross-validation procedure, a random forest distinguished different index finger positions on a numerical keyboard above chance-level accuracy. Among the different spectral features investigated, high $β$-power (20-30 Hz) over contralateral sensorimotor cortex carried most information about finger position. Thus, these findings indicate that finger position is in principle decodable from non-invasive features of brain activity that generalize across individuals.