## 5 Discussion and Conclusions  
Table 4 compares the implemented system to other wireless neural recording ICs with respect to the three most important features, _i.e._, the transmitted data type (raw data, spike detection or spike waveform), the transmission range and the power consumption per channel. For a fair comparison, since state-of-the-art wireless neural recording systems feature a transmission range always lower than \(1\) m, the proposed circuit can be thought sized to get a similar transmission range. Since the power needed at the antenna is inversely proportional to the square of the transmission distance, the PA power consumption could be scaled down by a factor of \(900\), _i.e._, (\(30\) m/\(1\) m)\({}^{2}\). In this case the equivalent power consumption of the whole system reduces to about \(6.7\) mW, corresponding to less than \(105\)\(\mu\)W per channel. Figure 21: Example of a reconstructed trace registered in an _in vivo_ experiment. Figure 22: Action potentials registered on a same channel and aligned in time. By comparing our system with the one described in [3], working in the same frequency range, the proposed system features a lower power consumption per channel. Also the data quality is greatly improved since spike detection is replaced by waveform detection, making possible single spike identification over a large microelectrode array. Compared with the IC in [7], our system shows an improvement of a factor larger than \(2\) in terms of power consumption per channel. It is true that the data rate does not allow all raw data acquisition as in [7], but our design optimizes information transfer and is potentially compliant with the narrowband spectral limitations in the Medical Implant Communication Service (\(MICS\)) band range (\(402\)-\(405\) MHz). In the future, this system could also be designed to work in the Industrial Scientific and Medical (\(ISM\)) band (\(902\)-\(928\) MHz). The system in [6] is better in term of power per channel but, relying on an UWB transmitter, it can suffer from interference. Moreover, this system has never been tested in _in vivo_ experiments and no information on the adopted antenna or on the power delivered by the PA is given. Regarding the system presented in [35], its power consumption per channel (\(80\)\(\mu\)W/ch) was evaluated considering the minimum transmitter power, assuming that it allows a \(1\) m transmission distance. However, the data rate is limited to \(1.5\) Mbit/s, translating into data acquisition at a sampling rate \(<\)\(3\) kS/s from all \(64\) channels. Higher sampling rates are possible only for a limited number of channels: assuming that \(20\) kS/s is the minimum sampling rate to acquire good quality neural signals, the maximum number of channels that can be recorded and transmitted simultaneously is only \(9\). In conclusion, thanks to the implemented compression algorithm that allows to preserve the information needed for single neuron identification while limiting the data throughput and the bandwidth, we achieved a good balance between power consumption and quality of transmitted data and transmission range. In addition, the power consumption per channel was lowered by a careful sizing of each individual circuit block.