IMSE Publications

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Author: James B. Romaine
Year: Since 2002

Journal Papers


Phase Synchronization Operator for on-chip Brain Functional Connectivity Computation
M. Delgado-Restituto, J.B. Romaine and A. Rodriguez-Vazquez
Journal Paper · IEEE Transactions on Biomedical Circuits and Systems, vol. 13, no. 5, pp 957-970, 2019
abstract      doi      

This paper presents an integer-based digital processor for the calculation of phase synchronization between two neural signals. It is based on the measurement of time periods between two consecutive minima. The simplicity of the approach allows for the use of elementary digital blocks, such as registers, counters and adders. The processor, fabricated in a 0.18μm CMOS process, only occupies and consumes 15nW from a 0.5V supply voltage at a signal input rate of 1024S/s. These low-area and low-power features make the proposed processor a valuable computing element in closed-loop neural prosthesis for the treatment of neural disorders, such as epilepsy, or for assessing the patterns of correlated activity in neural assemblies through the evaluation of functional connectivity maps.

Highly Scalable Real Time Epilepsy Diagnosis Architecture Via Phase Correlation
J.B. Romaine, M. Delgado-Restituto and A. Rodríguez-Vázquez
Journal Paper · Procedia Technology, vol. 27, pp 55-56, 2017
abstract      doi      pdf

Epilepsy is at current the world´s second most common neurological disorder affecting an estimated 50 million people. While up to 70% of epileptic suffers are treated successfully with epileptic medication some 30% continue to suffer untreated. This gap could be filled by the implementation of implantable neural prostheses which are able to detect when a seizure is coming and eventually actuate in the brain to stop its progression.

Conferences


Highly scalable real time epilepsy diagnosis architecture via phase correlation and functional brain maps
J.B. Romaine, M. Delgado-Restituto and A. Rodríguez-Vázquez
Conference · IEEE Biomedical Circuits and Systems Conference BioCAS 2018
abstract     

The complexity of biomedical neural processing is evident, with vast amounts of data needing to be handled and processed in order to reveal possible biomarkers which may lead to the early diagnosis of certain neurological disorders. One disorder in particular is epilepsy, which is one of the most common neurological disorder in the world today.Our proposed solution is a highly efficient, scalable and low powered device for the diagnosis and verification of epilepsy via the identification of changes in synchronicity between interictal neural signal segments.

Real-time phase correlation based integrated system for seizure detection
J.B. Romaine, M. Delgado-Restituto, J.A. Leñero-Bardallo and A. Rodríguez-Vázquez
Conference · Bio-MEMS and Medical Microdevices III Conference 2017
abstract     

This paper reports a low area, low power, integer-based digital processor for the calculation of phase synchronization between two neural signals. The processor calculates the phase-frequency content of a signal by identifying the specific time periods associated with two consecutive minima. The simplicity of this phase-frequency content identifier allows for the digital processor to utilize only basic digital blocks, such as registers, counters, adders and subtractors, without incorporating any complex multiplication and or division algorithms. In fact, the processor, fabricated in a 0.18μm CMOS process, only occupies an area of 0.0625μm2 and consumes 12.5nW from a 1.2V supply voltage when operated at 128kHz. These low-area, low-power features make the proposed processor a valuable computing element in closed loop neural prosthesis for the treatment of neural diseases, such as epilepsy, or for extracting functional connectivity maps between different recording sites in the brain.

Integer-based digital processor for the estimation of phase synchronization between neural signals
J.B. Romaine, M. Delgado-Restituto, J.A. Lenero-Bardallo and A. Rodriguez-Vazquez
Conference · Conference on Ph.D Research in Microelectronics and Electronics PRIME 2016
abstract     

This paper reports a low area, low power, integer-based neural digital processor for the calculation of phase synchronization between two neural signals. The processor calculates the phase-frequency content of a signal by identifying the specific time periods associated with two consecutive minima. The simplicity of this phase-frequency content identifier allows for the digital processor to utilize only basic digital blocks, such as registers, counters, adders and subtractors, without incorporating any complex multiplication and or division algorithms. The low area and power consumptions make the processor an extremely scalable device which would work well in closed loop neural prosthesis for the treatment of neural diseases.

Highly scalable real time epilepsy diagnosis architecture via phase correlation and functional brain maps
J.B. Romaine, L. Acasandrei, M. Delgado-Restituto, A. Rodríguez-Vázquez
Conference · World Congress on Biosensors BIOSENSORS 2016
abstract     

The complexity of biomedical neural processing is evident, with vast amounts of data needing to be handled and processed in order to reveal possible biomarkers which may lead to the early diagnosis of certain neurological disorders. One disorder in particular is epilepsy which is the most common neurological disorder in the world today affecting an estimated 50 million people.
Our proposed architecture is a highly efficient, scalable and low powered solution for the diagnosis and verification of epilepsy via the identification of changes in synchronicity between inter-ictal neural recording signals and functional brain maps. This multipurpose diagnosis system is realized as a mixture of clever data handling and sixteen real time phase synchronization processors which have a total capability of calculating the phase correlation of an entire brain map set of 120 neural signal combinations, gathered from 16 inter-ictal recording electrode positions located around the brain. Such a design could eventually lead to prediction of epilepsy via the detection of complex biomarkers.
In order for real time calculations to be possible we use a combination of smart pipelining and control logic. The processors calculate the phase correlation over the brain map via the means of accumulative sample differences from minimum to minimum transition periods between neural signals on sample by sample basis. This performs extremely well when compared to other more intense diagnostic calculation methods such as extraction of instantaneous phase angles.
The proposed architecture favours an ASCI design that drastically reduces the number of phase correlation calculation elements and cluttered interconnects and in turn infers a potentially low powered system.

Hardware friendly algorithm for the calculation of phase synchronization between neural signals
J.B. Romaine and M. Delgado-Restituto
Conference · IEEE Biomedical Circuits and Systems Conference BioCAS 2014
abstract     

This paper reports a mathematically simple, hardware efficient algorithm for use in the detection of epileptic seizures via an approximation of synchronization between two neural EEG signals. The algorithm assumes that the signals are pre-filtered into a desired narrow band, spanning only several 10's of Hz. Using this narrow band it is possible to collect the discrete time stamps, which are represented as the number of samples between two consecutive minimum within a given signal. The difference between a discrete time stamp in one signal and in another, at a given period in time gives an indication as to the amount of frequency difference between the two signals. Once these differences are accumulated, it provides an estimate as to when large increases and decrease in frequency happen in the two signals, with respect to one another.

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