Auditory Feedback for Brain Computer Interface Management - An EEG Data Sonification Approach

An auditory feedback for Brain Computer Interface (BCI) applications is proposed. This is achieved based on the so-called sonification of the mental states of humans, captured by Electro-Encephalogram (EEG) recordings. Two time-frequency signal decomposition techniques, the Bump Modelling and Empirical Mode Decomposition (EMD), are used to map the EEG recordings onto musical scores. This auditory feedback proves to have extremely high potential in the development of on-line BCI interfaces. Examples based on the responses from visual stimuli support the analysis.

[1]  Emil Jovanov,et al.  EEG analysis in a telemedical virtual world , 1999, Future Gener. Comput. Syst..

[2]  A. Walker Electroencephalography, Basic Principles, Clinical Applications and Related Fields , 1982 .

[3]  Eduardo Reck Miranda,et al.  Interfacing the Brain Directly with Musical Systems: On Developing Systems for Making Music with Brain Signals , 2005, Leonardo.

[4]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[5]  Petri Toiviainen,et al.  MIR In Matlab: The MIDI Toolbox , 2004, ISMIR.

[6]  Tomasz M. Rutkowski,et al.  Processing of the incomplete representation of the visual world , 2004 .

[7]  R. Gervais,et al.  Blind Source Separation and Sparse Bump Modelling of Time Frequency Representation of Eeg Signals: New Tools for Early Detection of Alzheimer's Disease , 2022 .

[8]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[9]  Simon P. Kelly,et al.  Visual spatial attention control in an independent brain-computer interface , 2005, IEEE Transactions on Biomedical Engineering.

[10]  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.

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

[12]  F. Vialatte Modélisation en bosses pour l'analyse de motifs oscillatoires reproductibles dans l'activité de populations neuronales: applications à l'apprentissage olfactif chez l'animal et à la détection précoce de la maladie d'Alzheimer , 2005 .