Journal of Neuroengineering and Rehabilitation Open Access a Brain-computer Interface with Vibrotactile Biofeedback for Haptic Information

BackgroundIt has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.MethodsA Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance.Results and ConclusionSubjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.

[1]  V. Mountcastle,et al.  Detection thresholds for stimuli in humans and monkeys: comparison with threshold events in mechanoreceptive afferent nerve fibers innervating the monkey hand. , 1972, Journal of neurophysiology.

[2]  G. Shannon.  Sensory feedback for artificial limbs. , 1979, Medical progress through technology.

[3]  S C Jacobsen,et al.  Extended physiologic taction: design and evaluation of a proportional force feedback system. , 1989, Journal of rehabilitation research and development.

[4]  R. Roeschlein,et al.  Factors related to successful upper extremity prosthetic use , 1989, Prosthetics and orthotics international.

[5]  W.J. Tompkins,et al.  Electrotactile and vibrotactile displays for sensory substitution systems , 1991, IEEE Transactions on Biomedical Engineering.

[6]  P E Patterson,et al.  Design and evaluation of a sensory feedback system that provides grasping pressure in a myoelectric hand. , 1992, Journal of rehabilitation research and development.

[7]  J. Wolpaw,et al.  Multichannel EEG-based brain-computer communication. , 1994, Electroencephalography and clinical neurophysiology.

[8]  B. Schreurs,et al.  A functional anatomical study of associative learning in humans. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[9]  D. Atkins,et al.  Epidemiologic Overview of Individuals with Upper-Limb Loss and Their Reported Research Priorities , 1996 .

[10]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[11]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[12]  G Pfurtscheller,et al.  Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[13]  Frank Tendick,et al.  Haptic guidance: experimental evaluation of a haptic training method for a perceptual motor skill , 2002, Proceedings 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. HAPTICS 2002.

[14]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

[15]  Piet M. T. Broersen,et al.  Autoregressive spectral estimation by application of the Burg algorithm to irregularly sampled data , 2002, IEEE Trans. Instrum. Meas..

[16]  M. Stokes,et al.  Probabilistic methods in BCI research , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[18]  A. A. Collins,et al.  Vibrotactile localization on the arm: Effects of place, space, and age , 2003, Perception & psychophysics.

[19]  W. A. Sarnacki,et al.  Brain–computer interface (BCI) operation: optimizing information transfer rates , 2003, Biological Psychology.

[20]  J. Pineda,et al.  Learning to control brain rhythms: making a brain-computer interface possible , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[22]  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.

[23]  Abderrahmane Kheddar,et al.  Tactile interfaces: a state-of-the-art survey , 2004 .

[24]  Thierry Pun,et al.  Analysis of bit-rate definitions for Brain-Computer Interfaces , 2005, CSREA HCI.

[25]  A. Kargov,et al.  Design and Evaluation of a Low-Cost Force Feedback System for Myoelectric Prosthetic Hands , 2006 .

[26]  Mark Huang,et al.  Limb deficiency and prosthetic management. 1. Decision making in prosthetic prescription and management. , 2006, Archives of physical medicine and rehabilitation.

[27]  L.J. Trejo,et al.  Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  G. F. Shannon,et al.  A comparison of alternative means of providing sensory feedback on upper limb prostheses , 2006, Medical and biological engineering.

[29]  A. Abbott.  Neuroprosthetics: In search of the sixth sense , 2006, Nature.

[30]  Andrew B. Schwartz,et al.  Brain-Controlled Interfaces: Movement Restoration with Neural Prosthetics , 2006, Neuron.

[31]  D.J. McFarland,et al.  The wadsworth BCI research and development program: at home with BCI , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[32]  J. Wolpaw,et al.  A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance , 2006, Biological Psychology.

[33]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[34]  Jonathan R Wolpaw,et al.  Guest Editorial The Third International Meeting on Brain-Computer Interface Technology: Making a Difference , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[35]  D.J. McFarland,et al.  An Evaluation of Autoregressive Spectral Estimation Model Order for Brain-Computer Interface Applications , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[36]  K.-R. Muller,et al.  The Berlin brain-computer interface: EEG-based communication without subject training , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.