Understanding brain structure and principles of operation is one of the major challengesofmodernscience.SincetheexperimentsbyGalvanionfrogmuscle contraction in 1792, it is known that electrical impulses lie at the core of the brain activity. The technology of neuro-electronic interfacing, besides its importance for neurophysiological research, has also clinical potential, so called neuropr- thetics. Sensory prostheses are intended to feed sensory data into patient’s brain by means of neurostimulation. Cochlear prostheses  are one example of sensory prostheses that are already used in patients. Retinal prostheses are currently under research . Recent neurophysiological experiments [3, 4] show that brain signals recorded from motor cortex carry information regarding the movement of subject’s limbs (Fig. 1.1). These signals can be further used to control ext- nal machines  that will replace missing limbs, opening the ?eld of motor prosthetics, devices that will restore lost limbs or limb control. Fig. 1.1. Robotic arm controlled by monkey motor cortex signals. MotorLab, U- versity of Pittsburgh. Prof Andy Schwartz, U. Pitt 2 1 Introduction Another group of prostheses would provide treatment for brain diseases, such as prevention of epileptic seizure or the control of tremor associated with Parkinson disease . Brain implants for treatment of Epilepsy and Parkinson symptoms (Fig. 1.2) are already available commercially [6, 7]. Fig. 1.2. Implantable device for Epilepsy seizures treatment . Cyberonics, Inc.
Explains the complete design of a brain-computer interface chip
Written by and oriented to electronic engineers
Focuses on implanted devices
Neuronal electronic interfaces carry significant potential for scientific research and medical applications. Neuroprosthetics may help to restore damaged sensory and motor brain functionality. Neuronal interfaces are evolving into complex micro-fabricated arrays of hundreds or thousands of sensors, and require tighter integration, advanced embedded computation, and wireless communication. At the very least, the electronic circuit of the implanted neuronal interface must acquire the data and transmit it outside. However, the huge data rates produced by large-scale neuronal interfaces exceed the communication bandwidth provided by low-power wireless channels. Hence, extensive embedded computations must be integrated into the interface in order to reduce the amount of transmitted data.
This book presents the Neuroprocessor, a novel computational neuronal interface device implemented in VLSI technology. In addition to neuronal signals acquisition, it can process the data, generate stimuli and transmit the data over wireless channels, while using minimum electric energy.
The NeuroProcessor opens with a brief background on neuronal communication and microelectrode recording. It introduces three generations of the Neuroprocessor and presents their architecture, circuits and algorithms. Applications to a miniature head-stage for in-vivo experiments and multi-electrode arrays for in-vitro studies are described.
1 Introduction. 1.1 Overview of the Book. 2 Recording from Biological Neural Networks. 2.1 The Neuron. 2.2 Interfacing Neurons Electrically. 2.3 Neuronal Probes for Extracellular Recording. 2.4 Recording from Cultured Neural Networks. 2.5 Typical Multi-Electrode Recording Setup. 2.6 Recorded Signal Information Content. 3 The Neuroprocessor. 3.1 Datarate Reduction in Neuronal Interfaces. 3.2 Neuroprocessor Overview. 4 Integrated Front-end for Neuronal Recording. 4.1 Background. 4.2 NPR01: First Front-end Generation. 4.3 NPR02: Analog Front-end with Sprike/LFP Separation. 5 NPR03: Mixed-Signal Integrated Front-end for Neuronal Recording. 5.1 Overview. 5.2 NPR03 Architecture. 5.3 Host Interface. 5.4 NPR03 Channel. 5.5 Analog-to-Digital Converter. 5.6 Integrated Preamplifier with DC Blocking. 5.7 NPR03 Measurements. 5.8 An NPR03 -Based Miniature Headstage. 5.9 A Novel Opamp for the Front-end Preamplifier. 5.10 Conclusions. 6 Algorithms for Neuroprocessor Spike Sorting. 6.1 Introduction. 6.2 Spike Sorting in a Neuroprocessor. 6.3 Spike Sorting Algorithms. 6.4 Detection and Alignment Algorithms. 7 MEA on Chip: In-Vitro Neuronal Interfaces. 7.1 Prototype Sensor. 7.2 Temperature sensor and heater. 7.3 Post-Processing and Bath Formation. 7.4 Conclusions and Future Work. 8 Conclusions. 8.1 Research Contributions. 8.2 Future Work. A NPR02 Technical Details. A.1 NPR02 Preamp Sizing. A.2 NPR02 Testboard Output Channel. B NPR03 Technical Details. B.1 NPR03 Instruction Set. B.2 NPR03 Registers. B.3 NPR03 Preamp Sizing. B.4 Measurements of Additional NPR03 Channel Circuits. References. Index.