Feature extraction using first and second derivative extrema (FSDE) for real-time and hardware-efficient spike sorting
| Autor: | Timothy G. Constandinou, Deren Y. Barsakcioglu, Amir Eftekhar, Mohammed R. Saberi, Sivylla E. Paraskevopoulou |
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| Rok vydání: | 2013 |
| Předmět: |
Databases
Factual Neural Prostheses Computational complexity theory Computer science Feature extraction Computer Systems Cluster Analysis Cluster analysis Second derivative Neurons Principal Component Analysis Computers business.industry Noise (signal processing) General Neuroscience Signal Processing Computer-Assisted Pattern recognition Electrophysiological Phenomena Maxima and minima Spike sorting Brain-Computer Interfaces Calibration Linear Models Spike (software development) Artificial intelligence business Algorithms Software Computer hardware |
| Zdroj: | Journal of Neuroscience Methods. 215:29-37 |
| ISSN: | 0165-0270 |
| Popis: | Next generation neural interfaces aspire to achieve real-time multi-channel systems by integrating spike sorting on chip to overcome limitations in communication channel capacity. The feasibility of this approach relies on developing highly efficient algorithms for feature extraction and clustering with the potential of low-power hardware implementation. We are proposing a feature extraction method, not requiring any calibration, based on first and second derivative features of the spike waveform. The accuracy and computational complexity of the proposed method are quantified and compared against commonly used feature extraction methods, through simulation across four datasets (with different single units) at multiple noise levels (ranging from 5 to 20% of the signal amplitude). The average classification error is shown to be below 7% with a computational complexity of 2N − 3, where N is the number of sample points of each spike. Overall, this method presents a good trade-off between accuracy and computational complexity and is thus particularly well-suited for hardware-efficient implementation. |
| Databáze: | OpenAIRE |
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