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• W3129384614 startingPage "109103" @default.
• W3129384614 abstract "• Adaptive spike detection method for autonomous operation. • Avoids the need for training data or threshold re-calibration. • Low complexity hardware-efficient design for real-time, low power applications. • Low memory requirements. No floating-point, multiplication or division operations. • Desirable for chronic or high channel recordings where signal fidelity may vary. The progress in microtechnology has enabled an exponential trend in the number of neurons that can be simultaneously recorded. The data bandwidth requirement is however increasing with channel count. The vast majority of experimental work involving electrophysiology stores the raw data and then processes this offline; to detect the underlying spike events. Emerging applications however require new methods for local, real-time processing. We have developed an adaptive, low complexity spike detection algorithm that combines three novel components for: (1) removing the local field potentials; (2) enhancing the signal-to-noise ratio; and (3) computing an adaptive threshold. The proposed algorithm has been optimised for hardware implementation (i.e. minimising computations, translating to a fixed-point implementation), and demonstrated on low-power embedded targets. The algorithm has been validated on both synthetic datasets and real recordings yielding a detection sensitivity of up to 90%. The initial hardware implementation using an off-the-shelf embedded platform demonstrated a memory requirement of less than 0.1 kb ROM and 3 kb program flash, consuming an average power of 130 μW. The method presented has the advantages over other approaches, that it allows spike events to be robustly detected in real-time from neural activity in a completely autonomous way, without the need for any calibration, and can be implemented with low hardware resources. The proposed method can detect spikes effectively and adaptively. It alleviates the need for re-calibration, which is critical towards achieving a viable BMI, and more so with future ‘high bandwidth’ systems’ targeting 1000s of channels." @default.
• W3129384614 created "2021-03-01" @default.
• W3129384614 creator A5017596402 @default.
• W3129384614 creator A5059636372 @default.
• W3129384614 date "2021-04-01" @default.
• W3129384614 modified "2023-09-26" @default.
• W3129384614 title "Adaptive spike detection and hardware optimization towards autonomous, high-channel-count BMIs" @default.
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• W3129384614 doi "https://doi.org/10.1016/j.jneumeth.2021.109103" @default.
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