Brain–Muscle–Computer Interface: Mobile-Phone Prototype Development and Testing

We report prototype development and testing of a new mobile-phone-based brain-muscle-computer interface for severely paralyzed persons, based on previous results from our group showing that humans may actively create specified power levels in two separate frequency bands of a single surface electromyography (sEMG) signal. EMG activity on the surface of a single face muscle site (auricularis superior) is recorded with a standard electrode. This analog electrical signal is imported into an Android-based mobile phone and digitized via an internal A/D converter. The digital signal is split, and then simultaneously filtered with two band-pass filters to extract total power within two separate frequency bands. The user-modulated power in each frequency band serves as two separate control channels for machine control. After signal processing, the Android phone sends commands to external devices via a Bluetooth interface. Users are trained to use the device via visually based operant conditioning, with simple cursor-to-target activities on the phone screen. The mobile-phone prototype interface is formally evaluated on a single advanced Spinal Muscle Atrophy subject, who has successfully used the interface in his home in evaluation trials and for remote control of a television. Development of this new device will not only guide future interface design for community use, but will also serve as an information technology bridge for in situ data collection to quantify human sEMG manipulation abilities for a relevant population.

[1]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[2]  P. Dario,et al.  Control of multifunctional prosthetic hands by processing the electromyographic signal. , 2002, Critical reviews in biomedical engineering.

[3]  M. Mazo,et al.  System for assisted mobility using eye movements based on electrooculography , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Shirley G Fitzgerald,et al.  Comparison of virtual and real electric powered wheelchair driving using a position sensing joystick and an isometric joystick. , 2002, Medical engineering & physics.

[6]  Byron M. Yu,et al.  A high-performance brain–computer interface , 2006, Nature.

[7]  G. Pfurtscheller,et al.  How many people are able to operate an EEG-based brain-computer interface (BCI)? , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  R. N. Scott,et al.  Error rate in five-state myoelectric control systems , 2006, Medical and Biological Engineering and Computing.

[9]  Maysam Ghovanloo,et al.  Evaluation of a wireless wearable tongue–computer interface by individuals with high-level spinal cord injuries , 2010, Journal of neural engineering.

[10]  Sanjay S Joshi,et al.  Two-Dimensional Cursor-to-Target Control From Single Muscle Site sEMG Signals , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Edward M. Schmidt,et al.  Single neuron recording from motor cortex as a possible source of signals for control of external devices , 2006, Annals of Biomedical Engineering.

[12]  C. D. De Luca Physiology and Mathematics of Myoelectric Signals , 1979, IEEE Transactions on Biomedical Engineering.

[13]  David Orlikowski,et al.  Hand versus mouth for call–bell activation by DMD and Becker patients , 2007, Neuromuscular Disorders.

[14]  Richard Wright,et al.  The Vocal Joystick , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[15]  T. Yin,et al.  Behavioral Studies of Sound Localization in the Cat , 1998, The Journal of Neuroscience.

[16]  S. S. Joshi,et al.  Brain-muscle-computer interface using a single surface electromyographic signal: Initial results , 2011, 2011 5th International IEEE/EMBS Conference on Neural Engineering.

[17]  Geert Langereis,et al.  Contactless sensors for Surface Electromyography , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[19]  J. V. Basmajian,et al.  Control and Training of Individual Motor Units , 1963, Science.

[20]  E. Fetz Operant Conditioning of Cortical Unit Activity , 1969, Science.

[21]  Yu-Luen Chen,et al.  Application of tilt sensors in human-computer mouse interface for people with disabilities. , 2001, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[22]  G. Pfurtscheller,et al.  Rapid prototyping of an EEG-based brain-computer interface (BCI) , 2001, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Geert Langereis,et al.  Contactless EMG sensors embroidered onto textile , 2007, BSN.

[24]  John P. Donoghue,et al.  Connecting cortex to machines: recent advances in brain interfaces , 2002, Nature Neuroscience.

[25]  Rory A Cooper,et al.  Joystick control for powered mobility: current state of technology and future directions. , 2010, Physical medicine and rehabilitation clinics of North America.

[26]  E. Fetz Volitional control of neural activity: implications for brain–computer interfaces , 2007, The Journal of physiology.

[27]  R. N. Scott,et al.  A three-state myo-electric control , 1966, Medical and biological engineering.