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An unsupervised method for on-chip neural spike detection in multi-electrode recording systems
- Source :
- EMBC
- Publication Year :
- 2013
-
Abstract
- Emerging multi-electrode-based brain-machine interfaces (BMIs) and large multi-electrode arrays used in in vitro experiments, enable recording of single neuron's activity on multiple electrodes and allow for an in-depth investigation of neural preparations, even at a sub-cellular level. However, the use of these devices entails stringent area and power consumption constraints for the signal-processing hardware units. In addition, the high autonomy of these units and an ability to automatically adapt to changes in the recorded neural preparations is required. Implementing spike detection in close proximity to recording electrodes offers the advantage of reducing the transmission data bandwidth. By eliminating the need of transmitting the full, redundant recordings of neural activity and by transmitting only the spike waveforms or spike times, significant power savings can be achieved in the majority of cases. Here, we present a low-complexity, unsupervised, adaptable, real-time spike-detection method targeting multi-electrode recording devices and compare this method to other spike-detection methods with regard to complexity and performance.
- Subjects :
- Neurons
business.industry
Computer science
Action Potentials
Signal Processing, Computer-Assisted
Neurophysiology
Signal-To-Noise Ratio
Article
Power (physics)
Signal-to-noise ratio
medicine.anatomical_structure
Transmission (telecommunications)
Brain-Computer Interfaces
Lab-On-A-Chip Devices
Electronic engineering
medicine
Waveform
Humans
Spike (software development)
Neuron
business
Electrodes
Computer hardware
Algorithms
Subjects
Details
- ISSN :
- 26940604
- Volume :
- 2013
- Database :
- OpenAIRE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Accession number :
- edsair.doi.dedup.....d776a4f34ac8a30fdbae5e194fc50417