Sensorimotor-rhythm modulation feedback with 3D vector-base amplitude panning — A brain-computer interface pilot study

A sensorimotor rhythm (SMR) brain-computer interface (BCI) not reliant upon the visual modality for feedback is desirable. Feedback is imperative to learning in a closed loop system and in enabling BCI users in learning to module their sensorimotor EEG rhythm. This pilot study demonstrates the feasibility of replacing the traditional visual feedback modality with a novel method of presenting auditory feedback: 3D vector-base amplitude panning (VBAP). Auditory feedback not only releases the visual channel for other uses but also offers an alternative modality for the vision impaired. 3D VBAP is compared with auditory feedback presented monaurally and stereophonically. VBAP feedback is presented in the form of an auditory asteroid avoidance game. This pilot study included two participants who demonstrate well above chance level that sensorimotor modulation is possible using all three presentation methods with VBAP, mono and stereo performing from best to worst respectively. Although the results are confounded by the number of subjects and sessions involved, this pilot study demonstrates for the first time that 3D VBAP can be used for SMR feedback in BCI and that users find it more appealing than other auditory feedback approaches.

[1]  N. Birbaumer,et al.  The Influence of Psychological State and Motivation on Brain–Computer Interface Performance in Patients with Amyotrophic Lateral Sclerosis – a Longitudinal Study , 2010, Front. Neuropharma..

[2]  Damien Coyle,et al.  Neural network based auto association and time-series prediction for biosignal processing in brain-computer interfaces , 2009, IEEE Computational Intelligence Magazine.

[3]  Clemens Brunner,et al.  Better than random? A closer look on BCI results , 2008 .

[4]  J. Blauert Spatial Hearing: The Psychophysics of Human Sound Localization , 1983 .

[5]  G Pfurtscheller,et al.  Estimating the Mutual Information of an EEG-based Brain-Computer Interface , 2002, Biomedizinische Technik. Biomedical engineering.

[6]  Thilo Hinterberger,et al.  An Auditory Brain-Computer Interface Based on the Self-Regulation of Slow Cortical Potentials , 2005, Neurorehabilitation and neural repair.

[7]  O. Hardiman,et al.  Ocular fixation instabilities in motor neurone disease , 2009, Journal of Neurology.

[8]  Benjamin Blankertz,et al.  A Novel 9-Class Auditory ERP Paradigm Driving a Predictive Text Entry System , 2011, Front. Neurosci..

[9]  M. Ohki,et al.  Ocular abnormalities in amyotrophic lateral sclerosis. , 1994, Acta oto-laryngologica. Supplementum.

[10]  B. Blankertz,et al.  A New Auditory Multi-Class Brain-Computer Interface Paradigm: Spatial Hearing as an Informative Cue , 2010, PloS one.

[11]  Andrzej Cichocki,et al.  Spatial Auditory BCI/BMI Paradigm - Multichannel EMD Approach to Brain Responses Estimation , 2010 .

[12]  T.M. McGinnity,et al.  A time-series prediction approach for feature extraction in a brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  D. McFarland,et al.  An auditory brain–computer interface (BCI) , 2008, Journal of Neuroscience Methods.

[14]  Girijesh Prasad,et al.  Learning to modulate sensorimotor rhythms with stereo auditory feedback for a brain-computer interface , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  A. Kübler,et al.  Motivation modulates the P300 amplitude during brain–computer interface use , 2010, Clinical Neurophysiology.

[16]  Ville Pulkki,et al.  Virtual Sound Source Positioning Using Vector Base Amplitude Panning , 1997 .

[17]  T. Martin McGinnity,et al.  EEG-based continuous control of a game using a 3 channel motor imagery BCI: BCI game , 2011, 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB).

[18]  N. Birbaumer,et al.  An auditory oddball brain–computer interface for binary choices , 2010, Clinical Neurophysiology.