A Dry Electrode Low Power CMOS EEG Acquisition System...

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A Dry Electrode Low Power CMOS EEG Acquisition System on Chip for Seizure Detection Matthieu Durbec, Valentin Béranger, Karim Eloueldrhiri Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA Email: [email protected], [email protected], [email protected] Abstract—This paper describes a low power EEG acquisition system on chip adapted for the use of dry electrodes to acquire signals suitable for seizure detection. The System on Chip consists of a High Pass Filter, a Low Noise Amplifier, a Low Pass Filter, an ADC driver and an ADC. The ADC output is then carried to a wireless processor where signals in a specific frequency range are analyzed for seizure detection. Keywords—Dry electrode, Impedance boosting, ADC, Seizure detection, choppers, electroencephalography. I. INTRODUCTION Great progress has been made in treatments dedicated to patients suffering with neurological conditions like epileptic seizures. It is crucial to develop new systems that will allow us to acquire neural signals and analyze them to find a correlation between these signals and the clinical symptoms [1]. This paper describes a non-invasive system on chip that extracts an analog EEG and converts it in the digital domain for analysis through machine learning algorithms. In current EEG monitoring systems, wet electrodes are a limitation due to the reduction of the impedance of the skin-electrode interface and the creation of a discomfort for the patient. Dry electrodes introduce a gel-free solution but require circuit modifications to counter the increase of impedance between the skin- electrode interface [2]. EEG has the advantage to be non- invasive and changes in the signal that happen seconds before the clinical symptoms can be detected to warn the patient or to trigger the recording of EEG for analysis by a neurologist. Machine learning algorithms are used for each patient to determine if the signal is a seizure EEG or a non-seizure EEG. Our work will be focused on the acquisition part of the system on chip by extracting and converting the EEG to a digital signal for the processing by the processor. The device is also intended to be used in a wireless environment so reception and transmission antenna systems will be part of the device but won’t be designed in this project. II. SYSTEM BLOCK DIAGRAM The proposed recording system consists of an analog front- end circuit including chopper-stabilized low noise amplifier, high and low pass filters, an ADC driver and a SAR ADC. Figure 1 shows the top-level block diagram. The integrated peripheral circuits include a bandgap circuit, two linear voltage regulators for separate analog and digital power supplies, and a current reference circuit to provide bias currents throughout the entire chip. Alimentation is provided by a lithium polymer battery; its durability will depend on the power consumption of the chip. It will be wireless rechargeable thanks to induction. The MCU provides the clock to ADC to easily program the ADC sampling frequency and the chopping frequency for the LNA. The MCU process the data extracted from the ADC and send them via wireless connection to the patient’s phone and to a clinical center. . Fig. 1. Top level block diagram III. DESIGN SPECIFICATIONS A. Body-Electrode Interface The electrode model used is the dry-contact which is equivalent to the circuit represented in Figure 2, where the resistor and capacitance in parallel represent the Stratum Corneum (surface skin) and the resistor represents the body [3]. EEG signals from the electrode are between 10 and 50 uV. Electrode Offset Voltage (EOV) is expected to be high (10- 100 mV). A high pass cut-off is used to reject EOV while passing low frequency EEG [1]. Fig. 2. Dry Contact Electrode Circuit Model B. Chopper-stabilized LNA Recording of low amplitude and relatively low-frequency EEG signals require minimization of the effects of intrinsic and popcorn noise sources from the active components. This excess noise can artificially depress the signal-to-noise ratio (SNR) and lead to incorrect diagnostic conclusions. Chopper stabilization allows to mitigate 1/f noise with low power consumption [1]. We have chosen to use a fully differential approach for the chopper-stabilized LNA to offer more noise

Transcript of A Dry Electrode Low Power CMOS EEG Acquisition System...

A Dry Electrode Low Power CMOS EEG Acquisition System on Chip for Seizure Detection

Matthieu Durbec, Valentin Béranger, Karim Eloueldrhiri

Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA Email: [email protected], [email protected], [email protected]

Abstract—This paper describes a low power EEG acquisition system on chip adapted for the use of dry electrodes to acquire signals suitable for seizure detection. The System on Chip consists of a High Pass Filter, a Low Noise Amplifier, a Low Pass Filter, an ADC driver and an ADC. The ADC output is then carried to a wireless processor where signals in a specific frequency range are analyzed for seizure detection.

Keywords—Dry electrode, Impedance boosting, ADC, Seizure detection, choppers, electroencephalography.

I. INTRODUCTION Great progress has been made in treatments dedicated to

patients suffering with neurological conditions like epileptic seizures. It is crucial to develop new systems that will allow us to acquire neural signals and analyze them to find a correlation between these signals and the clinical symptoms [1]. This paper describes a non-invasive system on chip that extracts an analog EEG and converts it in the digital domain for analysis through machine learning algorithms. In current EEG monitoring systems, wet electrodes are a limitation due to the reduction of the impedance of the skin-electrode interface and the creation of a discomfort for the patient. Dry electrodes introduce a gel-free solution but require circuit modifications to counter the increase of impedance between the skin-electrode interface [2]. EEG has the advantage to be non-invasive and changes in the signal that happen seconds before the clinical symptoms can be detected to warn the patient or to trigger the recording of EEG for analysis by a neurologist. Machine learning algorithms are used for each patient to determine if the signal is a seizure EEG or a non-seizure EEG. Our work will be focused on the acquisition part of the system on chip by extracting and converting the EEG to a digital signal for the processing by the processor. The device is also intended to be used in a wireless environment so reception and transmission antenna systems will be part of the device but won’t be designed in this project.

II. SYSTEM BLOCK DIAGRAM The proposed recording system consists of an analog front-

end circuit including chopper-stabilized low noise amplifier, high and low pass filters, an ADC driver and a SAR ADC. Figure 1 shows the top-level block diagram. The integrated peripheral circuits include a bandgap circuit, two linear voltage regulators for separate analog and digital power supplies, and a current reference circuit to provide bias currents throughout the entire chip. Alimentation is provided by a lithium polymer battery; its durability will depend on the power consumption of the chip. It will be wireless rechargeable thanks to induction. The MCU provides the clock to ADC to easily program the ADC sampling frequency and the chopping frequency for the LNA. The MCU process the data extracted from the ADC and send them via wireless connection to the patient’s phone and to a clinical center.

.

Fig. 1. Top level block diagram

III. DESIGN SPECIFICATIONS

A. Body-Electrode Interface The electrode model used is the dry-contact which is

equivalent to the circuit represented in Figure 2, where the resistor and capacitance in parallel represent the Stratum Corneum (surface skin) and the resistor represents the body [3]. EEG signals from the electrode are between 10 and 50 uV. Electrode Offset Voltage (EOV) is expected to be high (10-100 mV). A high pass cut-off is used to reject EOV while passing low frequency EEG [1].

Fig. 2. Dry Contact Electrode Circuit Model

B. Chopper-stabilized LNA Recording of low amplitude and relatively low-frequency

EEG signals require minimization of the effects of intrinsic and popcorn noise sources from the active components. This excess noise can artificially depress the signal-to-noise ratio (SNR) and lead to incorrect diagnostic conclusions. Chopper stabilization allows to mitigate 1/f noise with low power consumption [1]. We have chosen to use a fully differential approach for the chopper-stabilized LNA to offer more noise

mghovanloo3
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Dr. Guler comments: Still no conclusion in the paper Wise changes at the top level Add explanation about the functionality of the fully differential amplifier. How does it perform? Figures in Section II.B look very nice, but if you got them from a paper, include the reference numbers in the captions. Good descriptions about blocks Please think about supplying the ADC with 1V, consider cascade transistors and required operating voltages in a branch in the circuit in your ADC. Since you will use 0.6um technology, transistor thresholds are high. Your power consumption will be affected by this change. Consider your regulator one more time. Read more about regulators. Bandgap reference generator needs improvement in terms of design. Our technology is 0.5um, again consider supply voltage.
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(SoC)
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You need a reference for this statement.
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Such algorithms are being developed to be able to predict seizure, not just detect it. Because once the seizure is started, it is already too late!
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Do you mean communication systems?
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What type of MCU is being considered?
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Is this SPI, I2C, or ?
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Is this a single channel EEG? Most EEG systems are multichannel. Please clarify where is the reference voltage and reference electrode in this recording? Every EEG recording system, whether single or multichannel, needs a reference electrode.

and interference immunity. However, two challenges associated with chopper amplifiers are how to reduce their output ripple and residual offset.

1) Ripple reduction The output ripple is due to the amplifier’s up-modulated

offset voltage. Compared to the level bio potential signals, the ripple can be quite large and can limit the amplifier’s output headroom. Many prior designs address the output ripple by employing feedback to null the ripple [10]. In [11], foreground calibration is performed to generate a compensating current using a digital-to-analog converter to cancel the offset, and this current is fed to the output of gm1. However, these designs add power consumption. By placing a DC-blocking impedance between the first stage and the second stage (Fig.3), the opamp DC offset and flicker noise do not appear at the output. To ensure stable DC biasing, a large duty-cycled resistor Rf is placed across the first operational amplifier stage. This ripple rejection technique is analyzed in [12]. The schematic for the fully differential chopper amplifier is given Fig. 4.

Fig. 3. Two stage fully differential chopped amplifier with

ripple rejection

Fig. 4. Schematic of the fully differential amplifier

2) DC servo loop The residual output offset is caused by the spikes of the

input chopper, which in return are due to the charge injection mismatch of the switches. The spikes are demodulated by the output chopper and the result is a residual output offset. The residual offset can be compensated by a DC servo loop with filter which will also be used for the second pole of the high pass filter [13] (Fig.5).

3) High Pass Filter The EEG signal bandwidth is in the range 0.5-100 Hz, so the first stage is designed as a high pass amplifier with a cut-

off frequency close to 0.1 Hz. The high pass cut-off frequency is widely implemented using a pseudo-resistor larger than 100 GΩ in the feedback loop [13]. However pseudo-resistors limit the linearity of the front-end to 8 bits. Also, pseudo-resistors are very sensitive to process variations and their resistance can vary by a factor of 100. These issues make pseudo-resistors unreliable in a clinical setting [13]. Therefore, to realize large linear resistors, pseudo-resistors are replaced with duty-cycled resistors to enable high linearity and reliability (Fig. 5) [13]. The poles of the high pass filter are provided by the Chopper amplifier and the DC servo loop (Fig. 5)

4) Impedance Boosting Considering the impedance introduced by the

electrode, the input impedance of our current amplifier should be much larger to avoid signal attenuation [1]. Chopping reduces the DC input impedance of the sensing front-end. The low input impedance can generate offset currents that can cause tissue damage. To increase the input impedance, a capacitive impedance boosting loop can be used [2]. However, the boost impedance with the DC servo loop is not well suitable to increase the impedance. A new input-impedance boosting technique has been introduced in [13]. The auxiliary-path used charges the input caps Cin at the beginning of every chopping phase using aux-buffers (Fig. 5), reducing the charge provided by the input Vel to zero, thus boosting Zin. However, a positive feedback loop is formed around the aux-buffers, leading to the aux-buffer DC offset and flicker noise (modeled by Voff) being amplified and appearing. Such large offsets, if left unchecked, can saturate the front-end. In [14] Voff is up-modulated to fc/4 by using mixers M1 and M2 (Fig. 6), where fc is the chopping frequency. Therefore, Voff creates a benign ripple instead of a DC offset at Vel.

Fig. 5. Schematic of the chopped-stabilized LNA

Fig. 7. Auxiliary path with aux-chopping and pre-charge

assistance for boosting impedance

Storage capacitors Caux=8pF assist the aux-buffers at the beginning of the pre-charge phase (Fig. 6) by charge-sharing with Cin, and are disconnected for the remainder of the pre-charge phase. It leads to higher input impedance without increasing power consumption [14].

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What is gm1? It has not been introduced up to this point.
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If the image is taken from a reference, it should be included in the caption. This should be done throughout the paper.
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I'm not sure if this is the concern. But low input impedance in the presence of large electrode impedance can result in attenuation of the weak input signals, and perhaps also degradation in noise performance.
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Where is this signal? Cannot see it in the sch, and has not been defined before!
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These are very interesting circuits/designs, if you can properly simulate and getting them to work in our ON Semi 0.5um process.

5) Chopper-stabilized LNA The targeted chopper-stabilized LNA specifications are

given in Table 1.

Metrics Target Specification Voltage Supply 1.2V

Power consumption ~10µW DC Input impedance ~1GΩ Input-referred noise ~6µV

Ripple rejection yes TABLE 1. Chopper-stabilized LNA Specifications

6) Low pass filter and ADC driver The low-pass filter and single differential converter have a

much smaller impact than the CS-LNA on the noise and instrumentation issues. Together, their power is approximately 20% of the total instrumentation amplifier power [1]. Given the characteristics of EEG signals (0.5Hz – 100 Hz), the low pass filter provides two poles near 200Hz. This filter will use an OTA with a standard two-stage topology. The purpose of the low pass filter is to provide frequency cut-off for the EEG signals but also additional gain to the system. An example schematic for the low pass filters is shown in Fig. 8.

Fig. 8. Low Pass Filter Schematic

Each low pass filter will provide a gain 20dB and a cut-off frequency at -3dB equal to 200Hz. The filter is built around a two stage OTA with a differential stage and a buffer stage.

We use an additional gain and buffer block before the ADC input. The ADC processes signal differentially and is robust to any common-mode errors. Differential ADC also achieves superior linearity and better matching against process variations than the single-ended version. This block has also been designed to drive the capacitive sample-and-hold of the ADC. The topology uses two OTAS, each with a differential input stage and a buffer output stage to drive high resistors. The gain at each output is around 6dB. The OTAs used consist of a differential and a buffer stage. The schematic for this block is shown in Fig. 9.

Fig. 9. Gain stage and ADC driver

C. Analog-to-Digital Converter

1) General structure For seizure onset detection, ADC resolution of at least 12

bits is required [4]. SAR ADCs are quite popular with the neural sensing applications and could be a good profile for our requirements [5].

The ADC used in the system is thus a 12-bit successive approximation register converter. A block diagram is shown in Fig. 10: it uses a 6-bit main-DAC and a 6-bit sub-DAC architecture [6], both implemented as passive charge-redistribution capacitor arrays, which provide an inherent sample and hold function.

An important feature of the ADC for this application is that it maintains its energy per conversion down to very low speeds (i.e., 600 S/s).

Fig. 10. ADC Block Diagram

A controlled power-gating ensures that the static biasing remains on for only the minimum time required by the conversion. The ADC is fully differential, which helps achieving 12-bit dynamic range for the given noise floor and provides power-supply noise rejection, since supply noise originating from capacitor array switching is an important concern.

The conversion plan of this ADC is shown in Fig. 11: it starts by purging the DAC capacitors so that they can be used

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This is not possible in ON Semi 0.5 process. You should consider the the
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It is not clear how you are going to implement all of these large resistors in Fig. 8 and 9. Are these going to be duty-cycled similar to Fig. 5? If not, the area will be too large to fit within the SoC.
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Considering that the AFE shown in Fig. 3-7 is switched-cap based, it is not clear why the circuits in Fig. 8 and 9 are continuous, inconsistent with the earlier stages. Why these blocks are not sw-cap also?
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to derive a suitable auto-zeroing reference. Additionally, the auto-zeroing and sampling operations are separated. Consequently, sampling is delayed with respect to the start of the conversion. This delay is undesirable in some applications. However, purging, auto-zeroing, and sampling in separate phases improve the common-mode rejection, low-voltage operation, and noise performance of the ADC.

Fig. 11. ADC Conversion plan

2) DAC Circuits The sampled common-mode affects the DAC output

common-mode during bit-decisions. For proper offset cancellation in the comparator preamplifiers, however, it is critical that the DAC common-mode be equal to the auto-zeroing reference voltage. No differential-mode error is observed if the input common-mode equals the auto-zeroing reference. However, if the input common-mode deviates by even a few hundred millivolts, mismatch results in a differential current through the preamplifier input devices, causing a large differential-mode voltage error of several millivolts at the output.

First, as shown in Fig. 12(a), the capacitor arrays are purged of previous charge by shorting their top and bottom plates. Then, as shown in Fig. 12(b), they are switched so that an appropriate auto-zeroing reference can be generated for the comparator. Finally, input sampling is performed.

During sampling, which is shown in Fig. 12(c), the top-plates of the differential capacitor arrays are shorted, and their voltage simply floats to the input common-mode. Since only one switch is required to decouple the positive and negative arrays, the charge injection errors in this network are not subject to switch mismatch. Constituent pMOS and nMOS devices are sized so that the total switch impedance is symmetric about the common-mode voltage expected during differential sampling (i.e., mid-Vref). Consequently, the top-plate sampling switch sees a well-matched impedance on either side, and its channel charge distributes equally. An advantage of this sampling network is that, since half the input is sampled on each array, the DAC outputs always remain within the rails [7].

Fig. 12. DAC arrays during (a) purging, (b) auto-zeroing and

(c) sampling

More importantly, however, the top-plate voltage floats to the input common-mode, and, consequently, as desired, only the differential-mode input signal gets sampled. Sampling only differential-mode charge guarantees that the DAC output voltage during critical bit decisions will always be centered around midscale. Hence, to avoid differential mode errors in the preamplifiers due to device mismatch, the auto-zeroing voltage should also be at midscale. This voltage must be generated prior to sampling by switching the purged capacitor arrays into the divider configuration shown in Fig. 12(b).

3) Comparator

A block diagram of the comparator used is shown in Fig. 13: it has three cascaded preamplifiers. The preamplifier circuit has a nominal gain of 3.

Fig. 13. Comparator block diagram

Since the first preamplifier is noise limited, a bandwidth limiting output capacitor is used to manage its SNR, giving this stage the longest time-constant and greatest power consumption. However, all the preamplifier bias currents have been minimized, and, due to parasitics, the delays of even the later stages are sizeable. Under these circumstances reducing the gain requirements of the entire cascade saves considerable power [6].

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Where is the positive feedback stage in this design? Without it, the comparator will not be fast enough. You should compare with the performance of this comp with the designs in Baker and choose the best one for your application.

We want the relative offsets of the ADC to be small. Accordingly, offset cancelled preamplifiers can be used to obtain a signal swing beyond the latch offset. In this design, however, to maximize the comparator efficiency, an offset compensating latch is used. Thus, even in the presence of severe mismatch, the swing requirement at the output of the preamplifiers is just a couple of millivolts.

4) SAR Algorithm The SAR ADC operation is illustrated in Fig. 14. The

conversion starts with setting the MSB to 1. The resulting DAC output is then compared to input signal. If input signal is higher than DAC output, the MSB is kept as one and otherwise it is set to 0. In the next clock cycle, the second significant bit is compared in the same way and is accordingly. This is continued till the last bit.

Fig. 14. SAR algorithm for a 4-bit ADC

5) SAR Logic Block A simple SAR logic block can be developed using D flip-

flops. For an N-bit ADC, we will require 2N*D+1 flip-flops. The schematics of the SAR logic block and shift the registers are shown in Fig. 15. In the beginning of the conversion cycle, all flip-flops except X13 are set to state 0. X13 is set to state 1. X13 sets the output of X1 to 1. After comparing this digital code to the input signal, the comparator output sets X1 as explained in the previous section. The second shift register goes high in the second cycle and sets X2 to 1. X2 also serves as the clock signal for X1. This action is repeated till the LSB is decided. Once the conversion is over, the end of conversion (EOC) bit goes high.

Fig. 15. SAR logic block

6) ADC Specifications The targeted ADC specifications are given in Table 2.

Metrics Target Specification Voltage Supply 1V

Resolution 12 bits Maximum Sampling Rate 100kS/s

Power consumption 200nW @ 500S/s INL 0.19LSB DNL 0.16LSB

ENOB 10.55 SNDR (at Nyquist) 65dB (fin=50kHz)

TABLE 2. ADC Specifications

D. Voltage references

1) Bandgap voltage reference

The bandgap voltage reference is designed to provide a constant voltage Vref to our system independent of the power supply voltage Vdd and the temperature. The bandgap reference is formed using a diode-referenced self-biasing circuit (CTAT), a thermal voltage-referenced self-biasing circuit (PTAT) and the required start up circuit. The reference voltage is the sum of the PTAT and CTAT voltage drops. The schematic of the adopted circuit is shown in Fig. 16 [9].

Fig. 16. Bandgap voltage reference

For our system, we will be designing the bandgap voltage reference to generate Vref equal to 1.3V, for a supply voltage Vdd of 5V. The generated voltage reference will supply power to the different blocks and reference current.

2) Voltage regulator

In addition to the bandgap reference voltage circuit, a voltage regulator is added at the output of the BGR to ensure a constant voltage across the system. The voltage regulator uses a unity gain amplifier with negative feedback to output 99% of the input reference voltage. The schematic for the voltage regulator is shown in Fig. 17.

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Again, this is not possible. Realistically, 3V is as low as you can go.
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Make sure you have an accurate area consumption estimate for this.
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Obviously this is not from your ADC. So you should present it as a benchmarking table, and compare these specs with what you are trying to achieve as your design target. In its current form, you Draft-2 is missing realistic design targets.
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Why Vdd was 1V elsewhere, and 5 V here? Aren't all these blocks on the same chip?! Please clarify which block is at what voltage and, if different, why?

Fig. 17. Voltage regulator

IV. SYSTEM LEVEL SIMULATION We ran system level simulations for a few blocks of our

system using ideal operational amplifiers for most of them. System level simulations helped us define values and metrics for several parameters like the gain and cut-off frequencies of the low pass filters, ADC driver and supply voltage generated by the voltage references.

1) Low Pass filter

For this simulation, we used the schematic shown in Fig. 18. The amplifier used is an ideal operational amplifier with a linear gain of 120dB. We ran an AC Sweep simulation to get the output transfer function. The gain of the low pass filter is set at 20dB in the flat region with a cut-off frequency of 166 Hz at -3dB. Fig. 19. shows the output of the low pass filter in dB versus the frequency.

Fig. 18. Low Pass filter schematic

Fig. 19. Low Pass filter output

2) ADC Driver

The ADC driver has been simulated using the same ideal operational amplifier as in the low pass filter simulation. This block will provide us with an additional gain of 6dB and the ability to drive the input sample and hold capacitors of the ADC. Fig. 20. shows the schematic of the ADC driver and Fig. 21. displays the achieved 6dB gain for each one of the differential output.

Fig. 20. ADC Driver schematic

Fig. 21. ADC Driver output

3) Bandgap reference voltage

The bandgap voltage reference simulation has been implemented at transistor level. The transistors used are ideal and only the length and width parameters have been changed to achieve the desired results. Fig. 22. shows the schematic for the bandgap reference voltage circuit. We ran a DC sweep simulation to output the evolution of the reference voltage depending on the supply voltage Vdd. Results of the simulation are shown in Fig. 23. For a supply voltage Vdd of 5V, the generated reference voltage is equal to 1.32V.

mghovanloo3
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This is not a good voltage regulator, because the OpAmp output stage does not have the ability to drive the entire circuit. So an additional transistor is needed at the output. You should carefully review the literature for appropriate regulator designs.

Fig. 22. Bandgap reference voltage schematic

Fig. 23. Reference voltage evolution versus supply voltage

V. STATE OF THE ART

Metrics [1] [2] [8] This work

Voltage Supply 1V 3.3V 1.8V 1V

Technology (CMOS) 0.18µm 0.18µm 0.8µm 0.35µm

Zin >700MΩ >3.5GΩ >7.5MΩ >1GΩ

Input referred noise 1.3µVRMS 0.205µVRMS 0.98µVRMS ≈ 6µVRMS

CMRR 60dB 139.1dB 100dB -

Power Consumption (one channel)

3.5µW 36µW 2µW 5µW

Ripple rejection No No Yes Yes

TABLE 3. Comparisons with previous works

VI. TASKS TO BE COMPLETED

Tasks Owner

Chopper Stabilized LNA Matthieu Durbec

Low Pass Filter & ADC Driver Karim Eloueldrhiri

ADC Valentin Béranger

Voltage and current references Karim Eloueldrhiri

Integration Valentin Béranger, Karim Eloueldrhiri, Matthieu Durbec

TABLE 4. Distribution of tasks

REFERENCES [1] N. Verma, A. Shoeb, J. Bohorquez, J. Dawson, J. Guttag and A P.

Chandrakasan, “A Micro Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System,” IEEE journal of solid-state circuits, vol.45, no. 4, April 2010.

[2] S. Lim, C. Seok, H. Kim, H. Song and H. Ko, “A Fully Integrated EEG Analog Front-End IC with Capacitive Input Impedance Boosting Loop”, IEEE Custom Integrated Circuits Conference (CICC), 2014.

[3] YM. Chi, T-P Jung, G Cauwenberghs, “Dry-contact and Noncontact Biopotential Electrodes: Methodological Review,” IEEE Reviews in Biomedical Engineering, vol. 3, 2010.

[4] A. Bragin I. Fried R. J. Staba, C. L.Wilson and Jr J. Engel, “Quantitative analysis of high-frequency oscillations (80500 hz) recorded in human epileptic hippocampus and entorhinal cortex,” J. Neurophysiol., vol. 88, pp. 1743 – 1752, 10 2002.

[5] F. Shahrokhi, K. Abdelhalim, D. Serletis, P.L. Carlen, and R. Genov, “The 128-channel fully differential digital integrated neural recording and stimulation interface,” Biomedical Circuits and Systems, IEEE Transactions on, vol. 4, no. 3, pp. 149–161, June 2010.

[6] N.Verma and A.P.Chandrakasan, “An ultra low energy 12-bit rate-resolution scalable SAR ADC for wireless sensor nodes,” IEEE J. Solid- State Circuits, vol. 42, no. 6, pp. 1196–1205, Jun. 2007.

[7] G. Promitzer, “12-bit low-power fully differential noncalibrating successive approximation ADC with 1 MS/s,” IEEE J. Solid-State Circuits, vol. 36, no. 7, pp. 1138–1143, Jul. 2001.

[8] T. Denison, et al., “A 2 µW 100 nV/rtHz chopper-stabilized instrumentation amplifier for chronic measurement of neural field potentials,” IEEE J.Solid-State Circuits, vol. 42, no. 12, pp. 2934–2945, Dec. 2007.

[9] R. Jacob Baker, “CMOS Circuit Design, Layout, and Simulation”, 3rd Edition, IEEE Press Series on Microelectronic Systems, 2010.

[10] Q. Fan, F. Sebastiano, J. H. Huijsing, and K. A. A. Makinwa, , “A 1.8 µW60 nV/√Hz capacitively-coupled chopper instrumentation amplifier in 65 nm CMOS for wireless sensor nodes,” IEEE J. Solid-State Circuits, vol. 46, no. 7, pp. 1534–1543, Jul. 2011.

[11] J. Xu et al., “A 160 µW 8-channel active electrode system for EEG monitoring,” IEEE Trans. Biomed. Circuits Syst., vol. 5, no. 6, pp. 555–567, Dec. 2011.

[12] H. Chandrakumar et.al, “A Simple Area-Efficient Ripple-Rejection Technique for Chopped Biosignal Amplifiers,” IEEE Trans. Circuits and Systems-II: Express Briefs, vol. 62, no. 2, pp.189-193, Feb. 2015.

[13] H. Chandrakumar, et al., “A 2µW 40mVpp Linear-Input-Range Chopper-Stabilized Bio-Signal Amplifier with Boosted Input Impedance of 300MΩ and Electrode-Offset Filtering,” ISSCC, pp. 96-97, Feb. 2016.

[14] H. Chandrakumar, et al., “2.8µW 80mVpp-Linear-Input-Range 1.6GΩ-Input Impedance Bio-Signal Chopper Amplifier Tolerant to Common-Mode Interference up to 650mVpp” IEEE International Solid-State Circuits Conference (ISSCC) 2017

mghovanloo3
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Your BGR does not seem to be working properly. You need to double-check its operation. Perhaps because Vref is selected too low.
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Wrong technology! You should be using ON Semi 0.5um.
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No sure if this is realistic.