Unsupervised event detection and classification of multichannel signals

• An unsupervised method to classify MC signals with unknown events is presented.
• An optimal detection and characterization of events is found.
• Hand movements are classified using EMG recordings.

In this paper, we present a new unsupervised method to classify a set of Multichanel Signals (MC) with unknown events. Each signal is characterized by a sequence of events where the number of events, the start time and the duration between events can change randomly. The proposed method helps in the classification and event detection of the MC signals by an expert which usually becomes a tedious and difficult task. To this end, first, the problem of classification of MC signals characterized by a succession of events is analyzed by transforming the MC signals into a set of temporal sequences of easy interpretation. The algorithm detects events by means of an optimal unsupervised classification. It is not necessary to know the nature of the events and formulate hypotheses regarding their behavior. Then, a set of multichannel electromyographic (EMG) signals with events is generated. These MC signals are used to test the proposed method.