rtMEG: A Real-Time Software Toolbox for Brain-Machine Interfaces Using Magnetoencephelography
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- Sudre G. et al. (2010) rtMEG: A Real-Time Software Toolbox for Brain-Machine Interfaces Using Magnetoencephelography. In: Supek S., Sušac A. (eds) 17th International Conference on Biomagnetism Advances in Biomagnetism – Biomag2010. IFMBE Proceedings, vol 28. Springer, Berlin, Heidelberg
Magnetoencephalography (MEG) is a non-invasive method to study brain functions with high temporal resolution. There is an emerging interest in studying the potential use of MEG for brain-machine interfaces (BMI) research. To date, the majority of studies have performed offline analysis to reveal detailed information about the spatial and temporal evolution of neural activity as it relates to a task, or to measure neuroplasticity resulting from an intervention. However, real-time MEG feedback could benefit many areas of research, including BMI. Currently there is no available method to capture the large amount of information from a 306-channel Elekta Neuromag® MEG system in order to provide real-time feedback. We have developed a toolbox that can stream in real-time MEG signals from this system to any computer. These signals can be processed with minimal delay (<30 ms) and used for various applications. Our MEG toolbox is integrated with BCI2000, a widely used open source software package for BMI research and development , and it can be easily configured to relay the real-time signal in binary format to any arbitrary host in the network. Preliminary results indicate that we can achieve an update-rate of approximately 35 Hz with 324 channels of data sampled at 1000 Hz, which is sufficient for many real-time BMI studies. This real-time software can be a valuable tool for real-time BMI research, including studies of neurofeedback training for stroke and spinal cord injury rehabilitation, and other general neuroscience research. The toolbox will be made available to the scientific research community as open source along with the BCI2000 software, and we hope that it can be used to support many new areas of real-time MEG research.
Keywordsreal-time Neuromag brain-machine interfaces magnetoencephalography
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