Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584776
Karen M. Lee, Z. Qian, R. Yabuki, B. Du, H. Kino, T. Fukushima, K. Kiyoyama, Tetsu Tanaka
A good habit of measuring blood pressure (BP) daily is helpful for us to stay healthy or to monitor hypertensive conditions. However, the conventional method of measuring BP using a pressure cuff has many weaknesses. In order to eliminate the use of this pressure cuff, we proposed a system using the pulse arrival time (PAT) to measure BP. This PAT can be measured using time difference between the R-peaks of electrocardiogram (ECG) and photoplethysmography (PPG) signals. In our system, we obtained these two signals by using our self-designed ECG and PPG sensors. Our sensors were fabricated in 0.18 μm CMOS technology with a small recording area of about 2.53 mm2and 6.25 mm2, respectively. Our ECG sensor has variable amplifying gains and can achieve a total maximum gain of 60 dB. Besides that, it has a high pass filter with wide cutoff frequencies between 0.1-200 Hz, and low pass filter with cutoff frequencies of 0.2-10 kHz. The design of our ECG circuit allows us to obtain the ECG signals using fingertips and without using a ground electrode. This compact system has the potential to become a wireless wearable in the future. The measured PAT was fitted into a mathematical model and cuff-less BP readings were obtained. A plot of reference BP using oscillometric cuff and cuff-less BP showed a good correlation of r = 0.83 for systolic blood pressure (SBP). The SBP and diastolic blood pressure (DBP) mean absolute difference for the system are 6.75 mmHg and 6.08 mmHg respectively, which fairly passed the strict standard set by IEEE. In the future, our system will be compared with the use of sphygmomanometer, which is the gold standard, to further evaluate its accuracies.
{"title":"Continuous Peripheral Blood Pressure Measurement with ECG and PPG Signals at Fingertips","authors":"Karen M. Lee, Z. Qian, R. Yabuki, B. Du, H. Kino, T. Fukushima, K. Kiyoyama, Tetsu Tanaka","doi":"10.1109/BIOCAS.2018.8584776","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584776","url":null,"abstract":"A good habit of measuring blood pressure (BP) daily is helpful for us to stay healthy or to monitor hypertensive conditions. However, the conventional method of measuring BP using a pressure cuff has many weaknesses. In order to eliminate the use of this pressure cuff, we proposed a system using the pulse arrival time (PAT) to measure BP. This PAT can be measured using time difference between the R-peaks of electrocardiogram (ECG) and photoplethysmography (PPG) signals. In our system, we obtained these two signals by using our self-designed ECG and PPG sensors. Our sensors were fabricated in 0.18 μm CMOS technology with a small recording area of about 2.53 mm2and 6.25 mm2, respectively. Our ECG sensor has variable amplifying gains and can achieve a total maximum gain of 60 dB. Besides that, it has a high pass filter with wide cutoff frequencies between 0.1-200 Hz, and low pass filter with cutoff frequencies of 0.2-10 kHz. The design of our ECG circuit allows us to obtain the ECG signals using fingertips and without using a ground electrode. This compact system has the potential to become a wireless wearable in the future. The measured PAT was fitted into a mathematical model and cuff-less BP readings were obtained. A plot of reference BP using oscillometric cuff and cuff-less BP showed a good correlation of r = 0.83 for systolic blood pressure (SBP). The SBP and diastolic blood pressure (DBP) mean absolute difference for the system are 6.75 mmHg and 6.08 mmHg respectively, which fairly passed the strict standard set by IEEE. In the future, our system will be compared with the use of sphygmomanometer, which is the gold standard, to further evaluate its accuracies.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126773359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584829
D. Kanemoto, Shun Katsumata, M. Aihara, M. Ohki
This paper proposes a novel compressed sensing (CS) framework for electroencephalogram (EEG) signals with artifacts. A feature of this framework is the application of an independent component analysis (ICA) to remove the interference of artifacts after CS in a data processing unit. Therefore, we can remove the ICA processing block from the sensing unit. In the framework, we use a random sampling measurement matrix in CS to suppress the Gaussian of the compressed sensing data. Herein, the proposed framework is evaluated using raw EEG signals with a pseudo-model of an eye-blinking artifact. The comparison of normalized mean square error (NMSE) values are shown to quantitatively demonstrate the effectiveness of proposed framework.
{"title":"Framework of Applying Independent Component Analysis After Compressed Sensing for Electroencephalogram Signals","authors":"D. Kanemoto, Shun Katsumata, M. Aihara, M. Ohki","doi":"10.1109/BIOCAS.2018.8584829","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584829","url":null,"abstract":"This paper proposes a novel compressed sensing (CS) framework for electroencephalogram (EEG) signals with artifacts. A feature of this framework is the application of an independent component analysis (ICA) to remove the interference of artifacts after CS in a data processing unit. Therefore, we can remove the ICA processing block from the sensing unit. In the framework, we use a random sampling measurement matrix in CS to suppress the Gaussian of the compressed sensing data. Herein, the proposed framework is evaluated using raw EEG signals with a pseudo-model of an eye-blinking artifact. The comparison of normalized mean square error (NMSE) values are shown to quantitatively demonstrate the effectiveness of proposed framework.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114141523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584674
Elisa Donati, M. Payvand, Nicoletta Risi, R. Krause, K. Burelo, G. Indiveri, T. Dalgaty, E. Vianello
Electromyography (EMG) signals carry information about the movements of skeleton muscles. EMG on-line processing and analysis can be applied to different types of human-machine interfaces and provide advantages to patient rehabilitation strategies in case of injuries or stroke. However, continuous monitoring and data collection produces large amounts of data and introduces a bottleneck for further processing by computing devices. Neuromorphic technology offers the possibility to process the data directly on the sensor side in real-time, and with very low power consumption. In this work we present the first steps toward the design of a neuromorphic event-based neural processing system that can be directly interfaced to surface EMG (sEMG) sensors for the on-line classification of the motor neuron output activities. We recorded the EMG signals related to two movements of open and closed hand gestures, converted them into asynchronous Address-Event Representation (AER) signals, provided them in input to a recurrent spiking neural network implemented on an ultra-low power neuromorphic chip, and analyzed the chip's response. We configured the recurrent network as a Liquid State Machine (LSM) as a means to classify the spatio-temporal data and evaluated the Separation Property (SP) of the liquid states for the two movements. We present experimental results which show how the activity of the silicon neurons can be encoded in state variables for which the average state distance is larger between two different gestures than it is between the same ones measured across different trials.
{"title":"Processing EMG signals using reservoir computing on an event-based neuromorphic system","authors":"Elisa Donati, M. Payvand, Nicoletta Risi, R. Krause, K. Burelo, G. Indiveri, T. Dalgaty, E. Vianello","doi":"10.1109/BIOCAS.2018.8584674","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584674","url":null,"abstract":"Electromyography (EMG) signals carry information about the movements of skeleton muscles. EMG on-line processing and analysis can be applied to different types of human-machine interfaces and provide advantages to patient rehabilitation strategies in case of injuries or stroke. However, continuous monitoring and data collection produces large amounts of data and introduces a bottleneck for further processing by computing devices. Neuromorphic technology offers the possibility to process the data directly on the sensor side in real-time, and with very low power consumption. In this work we present the first steps toward the design of a neuromorphic event-based neural processing system that can be directly interfaced to surface EMG (sEMG) sensors for the on-line classification of the motor neuron output activities. We recorded the EMG signals related to two movements of open and closed hand gestures, converted them into asynchronous Address-Event Representation (AER) signals, provided them in input to a recurrent spiking neural network implemented on an ultra-low power neuromorphic chip, and analyzed the chip's response. We configured the recurrent network as a Liquid State Machine (LSM) as a means to classify the spatio-temporal data and evaluated the Separation Property (SP) of the liquid states for the two movements. We present experimental results which show how the activity of the silicon neurons can be encoded in state variables for which the average state distance is larger between two different gestures than it is between the same ones measured across different trials.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115304683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584720
Kate D. Fischl, A. Andreou, T. Stewart, Kaitlin L. Fair
The Neural Engineering Framework (NEF) provides a methodology for implementing algorithms and models using spiking neurons. Although it is possible to run simulations based on the NEF on Von Neumann hardware, neuromorphic hardware holds the promise of increased computational efficiency and lower power implementation. This work describes an implementation of the NEF on IBM's TrueNorth Neurosynaptic system. Using one TrueNorth chip, a NEF neural population of 629 neurons representing five dimensions is demonstrated on hardware. However, the crossbar array architecture itself, utilized in the TrueNorth hardware, can be used to compute the basic NEF calculations for any sized neural population, representing any dimensionality. The computation time is a function of the maximum values used in the computations.
{"title":"Implementation of the Neural Engineering Framework on the TrueNorth Neurosynaptic System","authors":"Kate D. Fischl, A. Andreou, T. Stewart, Kaitlin L. Fair","doi":"10.1109/BIOCAS.2018.8584720","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584720","url":null,"abstract":"The Neural Engineering Framework (NEF) provides a methodology for implementing algorithms and models using spiking neurons. Although it is possible to run simulations based on the NEF on Von Neumann hardware, neuromorphic hardware holds the promise of increased computational efficiency and lower power implementation. This work describes an implementation of the NEF on IBM's TrueNorth Neurosynaptic system. Using one TrueNorth chip, a NEF neural population of 629 neurons representing five dimensions is demonstrated on hardware. However, the crossbar array architecture itself, utilized in the TrueNorth hardware, can be used to compute the basic NEF calculations for any sized neural population, representing any dimensionality. The computation time is a function of the maximum values used in the computations.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121722050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584689
Debnath Maji, M. Suster, P. Mohseni
This paper reports on monitoring the red blood cell (RBC) aggregation dynamics under flow and in static condition using a microfluidic dielectric sensor. The sensor employs a three-dimensional (3D), parallel-plate, capacitive sensing structure with a floating electrode integrated into a microfluidic channel with a height of 50μm. Using an impedance analyzer, the sensor is shown to measure the real part of complex relative dielectric permittivity of human whole blood in a frequency range of 10kHz-100MHz under flow and in static condition. The dielectric permittivity of human whole blood at 1MHz indicates the formation of RBC aggregate structures called rouleaux under static condition and their complete breakdown under a physiological shear flow rate of 500s−1• This work also demonstrates that the kinetics of RBC aggregation is dependent on fibrinogen concentration of the blood sample and establishes that the sensor is capable of distinguishing this difference in the aggregation process even under physiological shear flow rates. This work demonstrates the potential of dielectric spectroscopy in obtaining information on RBC aggregation dynamics using µL-volumes of whole blood under flow and in stasis.
{"title":"Monitoring Red Blood Cell Aggregation Dynamics in Stasis and Under Flow Using a Microfluidic Dielectric Sensor","authors":"Debnath Maji, M. Suster, P. Mohseni","doi":"10.1109/BIOCAS.2018.8584689","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584689","url":null,"abstract":"This paper reports on monitoring the red blood cell (RBC) aggregation dynamics under flow and in static condition using a microfluidic dielectric sensor. The sensor employs a three-dimensional (3D), parallel-plate, capacitive sensing structure with a floating electrode integrated into a microfluidic channel with a height of 50μm. Using an impedance analyzer, the sensor is shown to measure the real part of complex relative dielectric permittivity of human whole blood in a frequency range of 10kHz-100MHz under flow and in static condition. The dielectric permittivity of human whole blood at 1MHz indicates the formation of RBC aggregate structures called rouleaux under static condition and their complete breakdown under a physiological shear flow rate of 500s−1• This work also demonstrates that the kinetics of RBC aggregation is dependent on fibrinogen concentration of the blood sample and establishes that the sensor is capable of distinguishing this difference in the aggregation process even under physiological shear flow rates. This work demonstrates the potential of dielectric spectroscopy in obtaining information on RBC aggregation dynamics using µL-volumes of whole blood under flow and in stasis.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132363455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584690
T. Lauteslager, Mathias Tømmer, T. Lande, T. Constandinou
Single-chip UWB radar systems have enormous potential for the development of portable, low-cost and easy-to-use devices for monitoring the cardiovascular system. Using body coupled antennas, electromagnetic energy can be directed into the body to measure arterial pulsation and cardiac motion, and estimate arterial stiffness as well as blood pressure. In the current study we validate that heart rate signals, obtained using multiple UWB radar-on-chip modules and body coupled antennas, do indeed originate from arterial pulsation. Through ECG-aligned averaging, pulse arrival time at a number of locations in the body could be measured with high precision, and arterial pulse propagation through the femoral and carotid artery was demonstrated. In addition, cardiac dynamics were measured from the chest. Onset and offset of ventricular systole were clearly distinguishable, as well as onset of atrial systole. Although further validation is required, these results show that UWB radar-on-chip is highly suitable for monitoring of vascular health as well as the heart's mechanical functioning.
{"title":"Cross-Body UWB Radar Sensing of Arterial Pulse Propagation and Ventricular Dynamics","authors":"T. Lauteslager, Mathias Tømmer, T. Lande, T. Constandinou","doi":"10.1109/BIOCAS.2018.8584690","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584690","url":null,"abstract":"Single-chip UWB radar systems have enormous potential for the development of portable, low-cost and easy-to-use devices for monitoring the cardiovascular system. Using body coupled antennas, electromagnetic energy can be directed into the body to measure arterial pulsation and cardiac motion, and estimate arterial stiffness as well as blood pressure. In the current study we validate that heart rate signals, obtained using multiple UWB radar-on-chip modules and body coupled antennas, do indeed originate from arterial pulsation. Through ECG-aligned averaging, pulse arrival time at a number of locations in the body could be measured with high precision, and arterial pulse propagation through the femoral and carotid artery was demonstrated. In addition, cardiac dynamics were measured from the chest. Onset and offset of ventricular systole were clearly distinguishable, as well as onset of atrial systole. Although further validation is required, these results show that UWB radar-on-chip is highly suitable for monitoring of vascular health as well as the heart's mechanical functioning.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133707374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584687
A. Nikas, Sreenivas Jambunathan, Leonhard Klein, M. Völker, M. Ortmanns
In this paper an architecture for a continuous time sigma delta modulator used in a neural recording channel is proposed. The instrumentation amplifier based input structure of the converter exhibits a high input impedance and linear behavior. Properties making its use interesting in analog frontends for direct application on sensor elements. Circuit linearity is achieved solely by negative feedback. A prototype was developed as a 2nd order single bit modulator in a cascade of integrators feedforward form and was taped-out in a 180nm technology. Simulations show an input impedance of >1GΩ and a THD of 0.012%. The input referred noise is 2.2µV at an average current consumption of 14µA occupying an area of 0.07mm2.
{"title":"A Low Distortion Continuous Time Sigma Delta Modulator using a High Input Impedance Instrumentation Amplifier for Neural Recording","authors":"A. Nikas, Sreenivas Jambunathan, Leonhard Klein, M. Völker, M. Ortmanns","doi":"10.1109/BIOCAS.2018.8584687","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584687","url":null,"abstract":"In this paper an architecture for a continuous time sigma delta modulator used in a neural recording channel is proposed. The instrumentation amplifier based input structure of the converter exhibits a high input impedance and linear behavior. Properties making its use interesting in analog frontends for direct application on sensor elements. Circuit linearity is achieved solely by negative feedback. A prototype was developed as a 2nd order single bit modulator in a cascade of integrators feedforward form and was taped-out in a 180nm technology. Simulations show an input impedance of >1GΩ and a THD of 0.012%. The input referred noise is 2.2µV at an average current consumption of 14µA occupying an area of 0.07mm2.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131848299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584800
Byunghun Lee, Y. Jia, Fanpeng Kong, M. Connolly, B. Mahmoudi, Maysam Ghovanloo
We presented a robust multi-antenna receiver design using the multiple software defined radios (SDR), which enables the easy implementation and extension for increasing the number of Rxs. The proposed multi-SDR Rxs with the optimized antenna design of Tx/Rx, can minimize the RF blind spots of the headstage on a freely-behaving animal, resulted in the reduced Tx radiation power against spatial/angular misalignments of the antenna or the disturbance in the RF path. In the prototype design, the multi-SDR Rxs and RF antennas are dedicated to transmit/receive 9 Mbps neural recorded data modulated by 433 MHz On-off keying (OOK) in the WINeRS-8 headstage. The detail features about the hardware and software subsystems are described in the paper, and the measured results demonstrate that the proposed multi-SDR Rxs can remove the blind spot of the Tx radiation while the single Rx cannot cover all of the spots. The proposed system was also fully verified by the in vivo experiment on a freely behaving rat.
{"title":"Toward A Robust Multi-Antenna Receiver for Wireless Recording From Freely-Behaving Animals","authors":"Byunghun Lee, Y. Jia, Fanpeng Kong, M. Connolly, B. Mahmoudi, Maysam Ghovanloo","doi":"10.1109/BIOCAS.2018.8584800","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584800","url":null,"abstract":"We presented a robust multi-antenna receiver design using the multiple software defined radios (SDR), which enables the easy implementation and extension for increasing the number of Rxs. The proposed multi-SDR Rxs with the optimized antenna design of Tx/Rx, can minimize the RF blind spots of the headstage on a freely-behaving animal, resulted in the reduced Tx radiation power against spatial/angular misalignments of the antenna or the disturbance in the RF path. In the prototype design, the multi-SDR Rxs and RF antennas are dedicated to transmit/receive 9 Mbps neural recorded data modulated by 433 MHz On-off keying (OOK) in the WINeRS-8 headstage. The detail features about the hardware and software subsystems are described in the paper, and the measured results demonstrate that the proposed multi-SDR Rxs can remove the blind spot of the Tx radiation while the single Rx cannot cover all of the spots. The proposed system was also fully verified by the in vivo experiment on a freely behaving rat.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134437393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584833
M. Douthwaite, P. Georgiou
This work presents a circuit for asynchronous, automatic biasing of a CMOS ISFET array for wearable electrochemical measurement systems. The circuit is integrated into a temperature compensated pH-to-frequency converter utilising the floating gates of an ISFET array. The work represents the first effort to address the issue of variable bias points in integrated electrochemical sensors for wearable applications, and is a low power, low transistor solution without an output voltage ripple. Designed in a 0.35flm CMOS technology, the system achieves a low power consumption of 29.72µW with a typical settling time of 0.7ms.
{"title":"An Asynchronous Auto-biasing Circuit for Wearable Electrochemical Sensors","authors":"M. Douthwaite, P. Georgiou","doi":"10.1109/BIOCAS.2018.8584833","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584833","url":null,"abstract":"This work presents a circuit for asynchronous, automatic biasing of a CMOS ISFET array for wearable electrochemical measurement systems. The circuit is integrated into a temperature compensated pH-to-frequency converter utilising the floating gates of an ISFET array. The work represents the first effort to address the issue of variable bias points in integrated electrochemical sensors for wearable applications, and is a low power, low transistor solution without an output voltage ripple. Designed in a 0.35flm CMOS technology, the system achieves a low power consumption of 29.72µW with a typical settling time of 0.7ms.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124633202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-01DOI: 10.1109/BIOCAS.2018.8584667
James D. Rosenthal, Eleftherios Kampianakis, Apoorva Sharma, M. Reynolds
Wireless brain-computer interfaces (BCIs) used for fundamental neuroscience research in freely moving non-human primates (NHPs) require communication systems capable of transferring large volumes of recorded neural data while consuming minimal power. We introduce a 6.25 Mbps differential quadrature phase-shift keying (DQPSK) backscatter wireless uplink for the NeuroDisc BCI, operating in the 902–928 MHz industrial, scientific, and medical (ISM)-band. The backscatter uplink consumes 77.5 μ$W$ (only 0.06% of the system power budget), yielding a communication energy efficiency of 12.4 pJ/bit, while the measured error vector magnitude of the DQPSK constellation is 9.69%. The neural recording front-end has a measured input-referred noise of 2.35 μVrms at a maximum sampling rate of 20 kSps. We present end-to-end recording and wireless uplink validation with pre-recorded neural data as well as in vivo recordings from a pigtail macaque.
{"title":"A 6.25 Mbps, 12.4 pJ/bit DQPSK Backscatter Wireless Uplink for the NeuroDisc Brain-Computer Interface","authors":"James D. Rosenthal, Eleftherios Kampianakis, Apoorva Sharma, M. Reynolds","doi":"10.1109/BIOCAS.2018.8584667","DOIUrl":"https://doi.org/10.1109/BIOCAS.2018.8584667","url":null,"abstract":"Wireless brain-computer interfaces (BCIs) used for fundamental neuroscience research in freely moving non-human primates (NHPs) require communication systems capable of transferring large volumes of recorded neural data while consuming minimal power. We introduce a 6.25 Mbps differential quadrature phase-shift keying (DQPSK) backscatter wireless uplink for the NeuroDisc BCI, operating in the 902–928 MHz industrial, scientific, and medical (ISM)-band. The backscatter uplink consumes 77.5 μ$W$ (only 0.06% of the system power budget), yielding a communication energy efficiency of 12.4 pJ/bit, while the measured error vector magnitude of the DQPSK constellation is 9.69%. The neural recording front-end has a measured input-referred noise of 2.35 μVrms at a maximum sampling rate of 20 kSps. We present end-to-end recording and wireless uplink validation with pre-recorded neural data as well as in vivo recordings from a pigtail macaque.","PeriodicalId":259162,"journal":{"name":"2018 IEEE Biomedical Circuits and Systems Conference (BioCAS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134381389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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