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The research described herein was undertaken to develop and test a novel tongue interface based on classification of tongue motions from the surface electromyography (EMG) signals of the suprahyoid muscles detected at the underside of the jaw. The EMG signals are measured via 22 active surface electrodes mounted onto a special flexible boomerang-shaped base. Because of the sensor’s shape and flexibility, it can adapt to the underjaw skin contour. Tongue motion classification was achieved using a support vector machine (SVM) algorithm for pattern recognition where the root mean square (RMS) features and cepstrum coefficients (CC) features of the EMG signals were analyzed. The effectiveness of the approach was verified with a test for the classification of six tongue motions conducted with a group of five healthy adult volunteer subjects who had normal motor tongue functions. Results showed that the system classified all six tongue motions with high accuracy of 95.1 ± 1.9 %. The proposed method for control of assistive devices was evaluated using a test in which a computer simulation model of an electric wheelchair was controlled using six tongue motions. This interface system, which weighs only 13.6 g and which has a simple appearance, requires no installation of any sensor into the mouth cavity. Therefore, it does not hinder user activities such as swallowing, chewing, or talking. The number of tongue motions is sufficient for the control of most assistive devices.

Tongue interface based on surface EMG signals of suprahyoid muscles