Performance of an ultra wideband (UWB) wireless system for real-time neural signal monitoring is evaluated by comparing spiking characteristics between transmitted and received signals for different experimental set-ups. Spike detection quality is selected as the main spiking characteristic of evaluated signals. Results are presented in receiver-operating characteristics and area-under-the-curve (AUC). In order to assess spike detection quality, a set of artificially generated neural signals is constructed from real neural recordings such that the ground truth is known. Data analysis shows how channel signal-to-noise-ratio (SNR) variation affects AUC in different signal SNR cases. Signals with low SNRs get less affected by reduced channel SNRs than those with higher SNR. Increasing bit error rate modifies spiking characteristics such that an under-estimation of the spiking frequency occurs due to spike losses. For practical application of real-time neural signal monitoring, UWB seems to offer best transmission conditions in a near-body environment.
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