Classification of fine hand movements of the same limb through EEG signals.

Discriminating fine movements within the same limb using electroencephalography (EEG) signals is a current challenge to non-invasive BCIs systems due to the close spatial representations on the motor cortex area of the brain, the signal-to-noise ratio, and the stochastic nature of this kind of signals. This research presents the performance evaluation of different strategies of classification using Linear Discriminant Analysis (LDA) method and power spectral density (PSD) features for three tasks: make a fist, open the hand, and keep the anatomical position of the hand. For this, EEG signals were collected from 10 healthy subjects and evaluated with different cross-validation methods: Monte Carlo, to implement an Offline Analysis and Leave-one-out for a pseudo-online implementation. The results show that the average accuracy for classifying the start of each task is approximately 76% for offline and Pseudo-online Analysis, classifying just the start of movement is 54% and 62% respectively for same both methods and 45% for and 32% classifying between classes. Based on these results, it can be said that the implementation of a BCI based on PSD features and LDA method could work to detect the start of one of the proposed tasks, but to discriminate the movement it is necessary to implement a different strategy for increase accuracy in the classification problem.