Streaming brain and physiological signal acquisition system for IoT neuroscience application
We describe the development of a multi-channel, multi-signal, wearable brain and physiological sensing system that acquires raw EEG, EMG, and ECG data for upto 18 hour time periods. Acquired signals are streamed in real-Time to the cloud which enables powerful and novel analytics to be rapidly compu...
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| Main Authors: | , |
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| Format: | Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015677341&doi=10.1109%2fIECBES.2016.7843551&partnerID=40&md5=a6fba27a2e2ae6f765ed74a916355152 http://eprints.utp.edu.my/20159/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | We describe the development of a multi-channel, multi-signal, wearable brain and physiological sensing system that acquires raw EEG, EMG, and ECG data for upto 18 hour time periods. Acquired signals are streamed in real-Time to the cloud which enables powerful and novel analytics to be rapidly computed on the acquired data streams. The mobility and long continuous acquisition capability of our system enables new neuroscience applications. Integration with cloud computing enables complex and computationally intensive analytics on streaming data to be performed in real-Time. A wide range of different analytics can be performed on the acquired signals. This capability is the critical enabler for neurofeedback to be applied in neuroergonomics, brain computer interfaces and cognitive training. © 2016 IEEE. |
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