**Author details**

REFRENCES

44(9) 2591-2604.

of Technology 2011, Tehran.

Biomedical and Health Informatics 2012.

approaches contain enough information to actuate prosthetic devices [33], in some other applications, such as neuroscientific studies, they are not satisfactory due to considerable loss of important information, e.g., spike wave shapes. Mathematical approaches, on the other hand, have been successful from the standpoint of data compression, while preserving wave shape of the spikes. Nonetheless, increasing the number of recording channels can result in the potential problems of these approaches: large silicon area and high power consumption. In contrast with all of the mentioned techniques, hardware approaches are capable of data reduction without adding any extra block to the microsystem. This is achieved by modifying the present hardware of the implant. An implementation of one of these approaches focusing on the ADC circuit of the system was presented and discussed in detail. The proposed method results in considerable reduction of bit-rate in multi-channel neural recording microsystems. Thus, efficient design of application-specific circuits for building blocks of neural implants

> *Recorded Neural Signal*

> > *Reconstructed Neural Signal*

1 5 9 13 17

0 100 200 300

1 5 9 13 17

0 100 200 300

*Time (ms)*

*Time (ms)*

*Time (ms)*

*Time (ms)*

should be taken into account as an appropriate method of data reduction.

*0*

*0*

*1*

*-1*

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*-1*

Amplitude (mV)

*-1 -0.5 0 0.5 1*

*Amplitude (m*

**Figure 20.** Recorded neural signal and reconstructed neural signal

*V)*

*-1 -0.5 0 0.5 1*

*Amplitude (m*

*V)* Amplitude (mV)

312 Advances in Bioengineering

Mohsen Judy1 , Alireza Akhavian2 and Farzad Asgarian3


#### **References**

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[5] Sodagar A.M. Integrated Circuit and System (ICAS) Lab Internal Report. K. N. Toosi

[6] Akhavian A., Judy M., Sodagar A.M. Anti-Logarithmic Quantization for Data Reduc‐ tion in Multi-Channel Intra-Cortical Neural Recording Implants. Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI),

[7] Olsson III R., Wise K.D. A Three-Dimensional Neural Recording Microsystem with Implantable Data Compression Circuitry. IEEE Journal of Solid-State Circuits 2005;

[8] Eberhart R.C, Dobbins R.W., Webber W.R.S. EEG Waveform Analysis Using Case‐ Net. Proceedings of the Annual International Conference of the IEEE Engineering in

[9] Vaz F., Principe J.C. Neural Networks for EEG Signal Decomposition and Classifica‐ tion. Proceedings of the IEEE 17th Annual Conference on Engineering in Medicine

[10] Eberhart, R.C., Dobbins, R.W. and Webber, W.R.S. Neural Network Design Consider‐ ations for EEG Spike Detection. Proceedings of the 1989 Fifteenth Annual Northeast

[11] Choi J.H., Jung H.K., Kim T. A New Action Potential Detector Using the MTEO and Its Effects on Spike Sorting Systems at Low Signal-to-Noise Ratios. IEEE Transactions

[12] B. Gosselin and M. Sawan. An Ultra Low-Power CMOS Automatic Action Potential Detector IEEE Transactions on Neural Systems and Rehabilitation Engineering 2009;

[13] Sodagar A.M, Wise K.D, Najafi K. A Fully Integrated Mixed-Signal Neural Processor for Implantable Multichannel Cortical Recording. IEEE Transactions on Biomedical

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