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Difference between revisions of "Design of Scalable Event-driven Neural-Recording Digital Interface"

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(Introduction)
(Introduction)
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As most of the power is spent in transmitting, smart recording systems can be equipped of circuitry to extract and send only “interesting” portion of the signal. This reduce the bandwidth from the recording system depending on the signal activity (more interesting events --> higher bandwidth and viceversa).
 
As most of the power is spent in transmitting, smart recording systems can be equipped of circuitry to extract and send only “interesting” portion of the signal. This reduce the bandwidth from the recording system depending on the signal activity (more interesting events --> higher bandwidth and viceversa).
 
For instance, an epileptic seizure detection device may send to a computer only portion of the signal that contains the actual seizure and do not send anything when the brain acts normally.
 
For instance, an epileptic seizure detection device may send to a computer only portion of the signal that contains the actual seizure and do not send anything when the brain acts normally.
In the context of action potentials, a neural recording system can send to a digital processor only the spikes and do not send anything when the only content of the signal is background noise. Once a spike has been extracted, it has to be assigned to one of the neuron sensed by the electrode (one electrode can sense 1 – 4 neurons in the neighbourhood). Spikes generated by different neurons are usually different in terms of shape and amplitude. This procedure is called “Spike sorting” and can be implemented in different ways. (REF SPIKE SORTING).  For instance, every extracted spike can be compared against 1 to 4 templates representative of the neuron activity. Another methods are based on extracted features - in the frequency domain (as Wavelett) or based on dimensionality reduction (e.g. principal component analysis)  - followed by clustering methods.
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In the context of action potentials, a neural recording system can send to a digital processor only the spikes and do not send anything when the only content of the signal is background noise. Once a spike has been extracted, it has to be assigned to one of the neuron sensed by the electrode (one electrode can sense 1 – 4 neurons in the neighbourhood). Spikes generated by different neurons are usually different in terms of shape and amplitude. This procedure is called “Spike sorting” and can be implemented in different ways. For instance, every extracted spike can be compared against 1 to 4 templates representative of the neuron activity. Another methods are based on extracted features - in the frequency domain (as Wavelett) or based on dimensionality reduction (e.g. principal component analysis)  - followed by clustering methods.
 
Depending on the context, neural recording systems may have from 8 - 16 channels for simple experiments with rats to 100,000 channels to study more complex neural networks as the human brain!
 
Depending on the context, neural recording systems may have from 8 - 16 channels for simple experiments with rats to 100,000 channels to study more complex neural networks as the human brain!
 
For every application, the analog and digital part have to be re-designed, verified, tested and validated, making the process long and expensive.  
 
For every application, the analog and digital part have to be re-designed, verified, tested and validated, making the process long and expensive.  
 
An extendible, systolic system composed of a network of simpler few-channels recording system would reduce the design time and simplify the other phases as the main effort in focused on the kernel device.
 
An extendible, systolic system composed of a network of simpler few-channels recording system would reduce the design time and simplify the other phases as the main effort in focused on the kernel device.

Revision as of 22:33, 16 July 2018

Introduction

Brain Computer Interfaces (BCI) are devices that decode the brain activity and use the decoded information for a wide application range: to control games in the entertainment field, to predict epileptic seizures or in more to control the seizures, to simply record data for scientific studies. The brain activity can be extracted invasively or not. Invasive methods can be used to sense: the extracellular single neuron activity (action potentials or spikes) with very tiny electrodes close to the neuron; an average communication among neurons close to the electrode via neuron’s axons via local field potentials – LFPs. Finally, ECoG is the signal used to acquire even more neurons ‘activity more superficially, but still implanted under the skull. Non-invasive methods are also possible by sensing EEG signals with electrodes very far from the neurons. Such electrodes are placed on the scalp and acquire the macro-scale activity of the brain at very low frequency (< 40Hz).

Brain signal electrodes.png

Many-channels recording systems are made of an analog-front-end (AFE) that is composed by amplifiers, filters and analog-to-digital converters (ADCs) and a digital part that streams the signals to a digital processor. This part is usually implemented with standard protocols like SPI or USB. As most of the power is spent in transmitting, smart recording systems can be equipped of circuitry to extract and send only “interesting” portion of the signal. This reduce the bandwidth from the recording system depending on the signal activity (more interesting events --> higher bandwidth and viceversa). For instance, an epileptic seizure detection device may send to a computer only portion of the signal that contains the actual seizure and do not send anything when the brain acts normally. In the context of action potentials, a neural recording system can send to a digital processor only the spikes and do not send anything when the only content of the signal is background noise. Once a spike has been extracted, it has to be assigned to one of the neuron sensed by the electrode (one electrode can sense 1 – 4 neurons in the neighbourhood). Spikes generated by different neurons are usually different in terms of shape and amplitude. This procedure is called “Spike sorting” and can be implemented in different ways. For instance, every extracted spike can be compared against 1 to 4 templates representative of the neuron activity. Another methods are based on extracted features - in the frequency domain (as Wavelett) or based on dimensionality reduction (e.g. principal component analysis) - followed by clustering methods. Depending on the context, neural recording systems may have from 8 - 16 channels for simple experiments with rats to 100,000 channels to study more complex neural networks as the human brain! For every application, the analog and digital part have to be re-designed, verified, tested and validated, making the process long and expensive. An extendible, systolic system composed of a network of simpler few-channels recording system would reduce the design time and simplify the other phases as the main effort in focused on the kernel device.