Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links bet...

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

We present a fully implantable neural recording IC with a spike-driven data compression scheme to improve the power efficiency and preserve crucial data for monitoring brain activities. A difference between two consecutive neural signals, $\Delta $ -neural signal, is sampled in each channel to reduce the full dynamic range and the required resolution of an analog-to-digital converter (ADC), enabling the whole analog chain to be operated at a 0.5-V supply. A set of multiple $\Delta $ -signals are stored in analog memory to extract the magnitude and frequency features of the incoming neural signals, which are utilized to discriminate spikes in these signals instantaneously after the acquisition in the analog domain. The energy- and area-efficient successive approximation ADC is implemented and only converts detected spikes, decreasing the power dissipation and the amount of neural data. A prototype 16-channel neural interface IC was fabricated using a 0.18-μm CMOS process, and each component in the analog front-end was fully characterized. We successfully demonstrated precise spike detection through both in vitro and in vivo acquisition of the neural signal. The prototype chip consumed 0.88 μW/channel at a 0.5-V supply for the recording and compressed about 89% of neural data, saving the power consumption and bandwidth in the system.

A Sub- ${\mu}$ W/Ch Analog Front-End for $\Delta $ -Neural Recording With Spike-Driven Data Compression

Fully-implantable wireless intracortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10× is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data-rate is reduced drastically. Earlier work on neuromorphic decoder chips only showed classification of discrete states. We present results for continuous state decoding using a low-power neuromorphic decoder chip termed S pike-input E xtreme L earning Ma chine (SELMA) that implements a nonlinear decoder without memory and its memory-based version with time-delayed bins, SELMA-bins. We have compared SELMA, SELMA-bins against state-of-the-art S teady- S tate K alman F ilter (SSKF), a linear decoder with memory, across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% (20%) or more increase in the fraction of variance accounted for (FVAF) by SELMA (SELMA-bins) over SSKF across the datasets. Estimated energy consumption comparison shows SELMA (SELMA-bins) consuming $\approx$ 9 $nJ/update$ (23 $nJ/update$ ) against SSKF's $\approx$ 7.4 $nJ/update$ for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF while consuming energy in the same range as SSKF whereas SELMA-bins performs the best with moderately increased energy consumption, albeit far less than energy required for raw data transmission. This paves the way for reducing transmission data rates in future scaled iBMI systems.

/pdf/towards-intelligent-intracortical-bmi-i-2-bmi-low-power-ut97exkgiz.pdf

Towards Intelligent Intracortical BMI (i $^2$ BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters

Electrocorticography (ECoG) is a type of electrophysiological monitoring useful for recording the activity from the cerebral cortex. It has emerged as a promising recording technique in brain-computer interfaces (BCI). Compression of these signals is essential for saving power and bandwidth in the novel application scenarios of Health-based IoT and Body Area Networks. However, this task is particularly challenging since, ECoG signals are not compressible either in time domain or in frequency domain. To that end, Block Sparse Bayesian Learning (BSBL) techniques were suggested for the reconstruction of compressed EEG and ECG signals, which is however, computationally demanding. Furthermore, given the heterogeneity in modern computing systems, careful design partitioning is required to most effectively evaluate the particular resources available on the deployed architecture. In this paper, we propose to utilise a combination of compressive sensing and neural network for the compression and reconstruction of ECoG signals, respectively. For the choice of the neural network, a multi-layer perceptron regressor with a stochastic gradient descent solver is developed. For a sample system, we show that the network has a compression ratio of 50%, and reconstruction accuracy of 89.85% after training with a practical, medium-sized dataset. In general, the results show that the most efficient system implementation is a heterogeneous architecture combining a CPU and a fieldprogrammable gate array (FPGA).

An FPGA-Based Brain Computer Interfacing Using Compressive Sensing and Machine Learning

................................................................................................................................ IV DEDICATION ............................................................................................................................ VI ACKNOWLEDGEMENT ........................................................................................................ VII LIST OF ABBREVIATIONS ................................................................................................. XIII CHAPTER ONE: INTRODUCTION ......................................................................................... 1 A. HUMAN ENHANCEMENTS IN THE MILITARY .......................................................................... 1 B. CAN HE UPHOLD HUMAN DIGNITY & SIMULTANEOUSLY PROMOTE THE COMMON GOOD? 5 C. MORAL CRITERIA, THEIR APPLICATION, AND ADVANCEMENT OF THE COMMON GOOD. .... 11 D. CHAPTER OUTLINES AND THEIR SUPPORT OF THE THESIS .................................................. 19 CONCLUSION ........................................................................................................................... 21 CHAPTER TWO: MILITARY ETHICS AND HUMAN ENHANCEMENT: A BACKGROUND ......................................................................................................................... 24 INTRODUCTION ........................................................................................................................... 24 I. Military Culture.................................................................................................................. 25 A. Introduction................................................................................................................. 25 B. Military Ethics and Values ......................................................................................... 25 C. Military Virtues .......................................................................................................... 30 II. Military Medical Ethics .................................................................................................... 35 A. Introduction ................................................................................................................ 35 B. Physician-Patient Relationship ................................................................................... 36 C. Dual Loyalty ............................................................................................................... 38 D. International Law ........................................................................................................ 40 E. Torture ........................................................................................................................ 43 III. Just War Theory (JWT).................................................................................................... 46 A. Introduction ................................................................................................................ 46 B. Jus ad Bellum.............................................................................................................. 47 C. Jus in Bello ................................................................................................................. 54 D. International Law ........................................................................................................ 55 E. New Wars, Old Rules? ............................................................................................... 57 F. Emerging Technologies .............................................................................................. 58 i. Cyber warfare.......................................................................................................... 59 ii. Drones ..................................................................................................................... 60 IV. Human Enhancement: An Overview ................................................................................ 62 A. Introduction ................................................................................................................ 62

/pdf/how-can-the-use-of-human-enhancement-he-technologies-in-the-4vpeexyq96.pdf

How Can the Use of Human Enhancement (HE) Technologies in the Military Be Ethically Assessed

Individuals with tetraplegia lack independent mobility, making them highly dependent on others to move from one place to another. Here, we describe how two macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex, and decoded to achieve brain control of the platform. We found that free-running brain control of the platform (which was not equipped with any machine intelligence) was fast and accurate, resembling the performance achieved using joystick control. The decoding algorithms can be trained in the absence of joystick movements, as would be required for use by tetraplegic individuals, demonstrating that the non-human primate model is a good pre-clinical model for developing such a cortically-controlled movement prosthetic. Interestingly, we found that the response properties of some neurons differed greatly depending on the mode of control (joystick or brain control), suggesting different roles for these neurons in encoding movement intention and movement execution. These results demonstrate that independent mobility can be achieved without first training on prescribed motor movements, opening the door for the implementation of this technology in persons with tetraplegia.

/pdf/independent-mobility-achieved-through-a-wireless-brain-335to7fvu5.pdf

Kulkarni Vinayak Vishal

Papers

Independent Mobility Achieved through a Wireless Brain-Machine Interface