Real-Time Wireless Sonification of Brain Signals
In this paper, an alternative representation of EEG is investigated, in particular, translation of EEG into sound; patterns in the EEG then correspond to sequences of notes. The aim is to provide an alternative tool for analysing and exploring brain signals, e.g., for diagnosis of neurological diseases. Specifically, a system is proposed that transforms EEG signals, recorded by a wireless headset, into sounds in real-time. In order to assess the resulting representation of EEG as sounds, the proposed sonification system is applied to EEG signals of Alzheimer’s (AD) patients and healthy age-matched control subjects (recorded by a high-quality wired EEG system). Fifteen volunteers were asked to classify the sounds generated from the EEG of 5 AD patients and five healthy subjects; the volunteers labeled most sounds correctly, in particular, an overall sensitivity and specificity of 93.3% and 97.3% respectively was obtained, suggesting that the sound sequences generated by the sonification system contain relevant information about EEG signals and underlying brain activity.
Mohamed Elgendi and Justin Dauwels would like to thank the Institute for Media Innovation (IMI) at Nanyang Technological University (NTU) for partially supporting this project (Grant M58B40020).
- 2.Berger, H.: On the Electroencephalogram of Man. Electroencephalography and Clinical Neurophysiology (1969) 28:133Google Scholar
- 3.Lucier, A.: Statement on: music for solo performer. Biofeedback and the Arts: Results of Early Experiments (Vancouver, Canada: Aesthetic Research Centre of Canada) (1967)Google Scholar
- 4.EmotivSystems. Emotiv - brain computer interface technology. http://emotiv.com.
- 6.NeuroFocus: http://www.neurofocus.com/.
- 9.Vialatte, F., Musha, T., Cichocki, A.: Sparse Bump Sonification: a New Tool for Multichannel EEG Diagnosis of Brain Disorders. Artificial Intelligence in Medicine (2010)Google Scholar
- 10.BCI2000 - General-Purpose System for Brain Computer Interface http://www.bci2000.org/BCI2000/Home.html.
- 11.Dauwels, J., Srinivasan, K., Reddy, R., Musha, T., Vialatte, F., Latchoumane, C., Jeong, J., Cichocki, A.: Slowing and loss of complexity in Alzheimer’s EEG: Two sides of the same coin? International Journal of Alzheimer's Disease((in press)) (2011)Google Scholar
- 12.Vialatte, F., Cichocki, A., Dreyfus, G., Musha, T., Rutkowski, T.M., Gervais, R.: Blind Source Separation and Sparse Bump Modelling of Time Frequency Representation of Eeg Signals: New Tools for Early Detection of Alzheimer's Disease. Paper presented at the IEEE Workshop on Machine Learning for Signal Processing, 28–28 Sept. 2005Google Scholar
- 19.Dauwels, J., Vialatte, F., Latchoumane, C., Jeong, J., Cichocki, A.: EEG synchrony analysis for early diagnosis of alzheimer’s disease: A study with several synchrony measures and EEG data sets. Paper presented at the 31st Annual International Conference of the IEEE EMBS, Minneapolis, Minnesota, USA,Google Scholar