Electromyography (EMG) is a random biological signal that depends on the electrode
placement and the physiology of the individual. Currently, EMG control is practically limited
by this individualistic nature and requires per session training. This study investigates the
EMG signals based on six locations on the lower forearm during contraction. Gesture
classification was performed en-bloc across 20 subjects without retraining with the objective
of determining the most classifiable gestures based on the similarity of their resultant EMG
signals. Principle component analysis (PCA) and linear discriminant analysis (LDA) were the
principal tools for analysis. The results showed that many gesture pairs could be accurately
classified per channel with accuracies of over 85%. However, classification rates dropped to
unreliable levels when up to nine gestures were classified over the single channels. The
classification results show universal classification based on a common EMG database is
possible without retraining for limited gestures.