1
A Neuronal Signal Detector For Biologically Generated Magnetic Fields Tiago Costa1 , Mois´es S. Piedade2, Jos´e Germano3 , Jos´e Amaral4 , Paulo P. Freitas5 INESC/University of Lisbon Rua Alves Redol, 9 1000-029 Lisboa, Portugal Email: 1
[email protected],{2msp,3 jahg}@inesc-id.pt {4 jamaral,5 pfreitas}@inesc-mn.pt
Abstract—This paper presents a neuronal signal detector for biologically generated magnetic fields. The system includes a hardware section implemented with discrete electronics, which has an ultra-low noise DC or DC+AC current source for magnetoresistive (MR) sensor biasing, signal amplification and filtering, and a software interface that allows signal demodulation, visualization and digital post processing. Compared to the previous measurement setup, the results show that, for the same bandwidth, the proposed instrumentation system has approximately 50 times better noise performance, making the sensor noise the dominant noise source. The system is able to record the magnetic field generated by ionic currents from action potentials of in vitro experiments with mice brain slices. Also, in order to obtain an increased spatial resolution, by scaling the number of sensors that can be read, and to enhance the system immunity to external interferences, two integrated circuits with an ultra-low noise current source for MR biasing and a low-noise variable gain amplifier were developed and are also presented. Index Terms—Neuronal measurements, magnetoresistive sensors, ultra-low noise, CMOS front-end.
I. I NTRODUCTION The recording of brain signals provides valuable information for physiologists and neuroscientists to understand the brain. Until now, such recording has been done using dense microelectrode systems, either on the brain surface or deep within the brain [1], or MOS transistors, which have also been successfully used to record neuronal signals [2]. Both these technologies measure the voltage generated by the flow of ionic currents due to neuronal interactions [1]. In the past years, magnetoresisive (MR) sensors have been used in biological applications, more specifically in biomolecular recnognition [3]–[6] and flow cytometry applications [7], [8]. In both applications they replace fluorescent/optical based systems, providing improved performance, cost, and portability. In biomolecular recnognition applications, the MR sensors are used as sensing probes to detect magnetized biomolecule targets that are disease specific. Due to the very high sensitivity of the MR sensors in [4], [9], which were fabricated at INESCMN, together with an ultra-low noise interface platform, implemented with discrete electronics, designed at INESCID [4], the limit of detection was 40 femtomolar (fM). In [6], an integrated circuit interface for MR sensors, including an analog front-end, ADCs and temperature drift digital correction, successfully detected biomolecule concentrations as low as 10 fM. Since MR sensors are capable of detecting such small magnetized biomolecules, they have been recently
used to read neuronal signals [10]: MR sensors [5], [11] (fabricated at INESC-MN) detect the magnetic field produced by the ionic currents generated at each neuronal interaction, by converting it into a resistance variation. The measurement of such ionic currents can bring new and valuable information regarding how neurons interact. The MR sensors are fabricated at nanoscale size, either in planar substrates [10] or at the tip of microfabricated needles [12], which are completely passivated by an oxide and can be used in similar applications as microelectrode arrays. Furthermore, MR sensors do not require a galvanic contact with the brain, which avoids contact between metal and brain tissue. The first results with this new technology [10], [12] were obtained using commercial desktop instrumentation equipment. With a 70 Hz bandwidth and a DC biasing current of 1 mA, the total noise observed in the measurement was approximately 1 µVRMS , allowing the detection of neuronal signals of 20 µV [10]. Firstly, it should be noted that this setup has a limited bandwidth of 70 Hz, which prevents the measurement of higher frequency neuronal signals, such as spikes. Additionally, wires among the sensors, the biasing circuit and the pre-amplification instrumentation equipment introduced extra noise and interference, which made the electronic’s noise the dominant noise source in the system, thus further limiting the minimum number of neurons whose action potentials can be measured. In order to improve the bulky instrumentation system based on desktop equipment used in [10], and make the electronic’s noise negligible when compared to the MR sensor’s noise while increasing the measurement bandwidth, as well as decreasing the number of noise sensitive connecting cables, the work presented in [13] was developed. The proposed system successfully increased the measurement bandwidth to 2.75 kHz while decreasing the integrated noise to 814 nVRMS , which shows an approximately 50 times better noise performance (if the same bandwidth is considered), making the sensor noise dominant and increasing noise immunity, due to shielding and a decreased number of cable interconnections. Furthermore, by providing 16 amplification and filtering channels, it is possible to measure 16 MR sensor signals simultaneously, which would be impractical with desktop equipment. This paper is an extended version of [13] and presents a neuronal signal detector for biologically generated magnetic fields. The system hardware is implemented with discrete electronics and provides MR sensors biasing, signal amplification and filtering. The software interface allows signal visualization,
2
!
which for neuronal applications corresponds to the magnetic field produced by the ionic currents generated by an action potential. The MR sensors used in [10], [12] are of two types: spin valves (SV), which have a typical magnetoresistive ratio (∆R/R) of 7 %, and magnetic tunnel junctions (MTJs), that can have a magnetoresistive ratio of up to 200 % ( [11]). R (H) = RN OM + ∆R (H)
[Ω]
(1)
In Fig. 1 a diagram of the experiment performed in [10] is presented, where mice brain slices (300 µm thick) are transferred to a recording chamber for submerged slices, and continuously superfused with gassed Krebs solution (artificial cerebrospinal fluid solution used to keep the hippocampus brain slice alive during the experiments [10]). The MR device is integrated in the recording chamber and is placed in the area of interest of the mouse hippocampus brain slice; in this case, CA1 region of the hippocampus. An electrode stimulates the brain tissue, and the MR sensors detect the neuronal activity at the tissue surface. The measurement is then performed by biasing the MR sensors with a current to convert ∆R(H) into a voltage, which is amplified and bandpass filtered, in order to remove out-of-band noise. In [10], [12], [13], the MR sensor is biased with a DC current (IBDC ), and the corresponding output voltage is given by (2), where G is the amplifier gain and BP F (f ) is the bandpass filter frequency response. VO (f ) = IBDC · (RN OM + ∆R (H(f ))) · G · BP F (f ) [V ] (2)
−5
4
x 10
Stimulus signal Voltage [V]
storing and additional digital filtering of the measured signals. In addition to the work in [13], this paper introduces the following: experiments performed in [10], [12] revealed that the measured signal is higher than predicted, mainly as a result of capacitive coupling between the brain tissue and the MR sensors, which induces a voltage at the sensor terminals that is capable of masking the signal generated by the magnetic fields in the brain. A DC+AC current source to bias the MR sensors enables an amplitude modulation scheme. The system herein described was designed to separate the biasing independent capacitive signal from the magnetic signal detected by the MR sensor, in which the central frequency depends on the AC biasing current frequency. To demodulate the magnetic signal back to the baseband, a demodulation block in the software was introduced. Moreover, as the work in [6] revealed, by integrating MR sensor front-end electronics in CMOS technology, the system resolution can be further improved due to the signal biasing and amplification being closer to the MR sensors. Also, by integrating the front-end in CMOS technology, it is feasible to increase the number of measured MR sensors by at least one order of magnitude, which makes the MR sensor technology competitive with microelectrode arrays for neuronal measurements, with the advantage of providing electrical isolation between the brain and the MR sensors. Thus, a CMOS front-end for neuronal magnetic reading was designed and characterized, comprising a MR sensor biasing circuit and a low-noise, high-gain, two stage amplifier, that will be included in future versions of the instrumentation system. The paper is organized as follows: section II introduces the MR sensors interface for neuronal detection; section III presents the proposed neuronal signal detector; section IV describes the software interface; section V presents the new CMOS frontend; section VI presents the experimental results and section VII draws the main conclusions.
Sensor response
2
0
−2
8.74
8.742
8.744 8.746 Time [s]
8.748
8.75
Fig. 2. Neuronal signal detection.
Oxide Layer
Substrate Fig. 1. Neuronal measurement with MR sensor: MR sensors below mice brain slices measure the magnetic fields produced by the ionic currents generated by action potentials. Capacitive coupling between brain and MR sensors induce an undesired voltage.
II. M AGNETORESISTIVE S ENSORS I NTERFACE FOR N EURONAL DETECTION MR sensors convert a magnetic field (H) into a resistance variation (∆R (H)) around a nominal value (RN OM ) (1) [11],
By using the instrumentation system proposed in [13], with the MR sensors used in [12], a MR sensor signal was recorded after stimulating the tissue with a pulse, and its representation in the time domain (the inverse transform of (2)) is presented in Fig. 2. However, in [10], [12] it is stated that the nature of the measured signals is electro-magnetic, since, in addition to the magnetic field detected by the MR sensor, there is a capacitive link between the brain tissue and the MR sensors, as illustrated in Fig. 1. To distinguish the effect of the magnetic field generated by the ionic currents in the brain from the voltage generated in the MR sensors by capacitive coupling, the MR sensor must be biased with a DC+AC current, as described in (3), where IBDC and IBAC are the DC current
3
and AC current amplitude, fi is the AC current frequency and Vc is the voltage due to capacitive coupling. VRS (t)
= (IBDC + IBAC sin(2πfi t)) · (RN OM + ∆R(H(t))) + Vc (t)
[V ]
(3)
Considering that the same current is applied to a resistance RX , which is integrated in the instrumentation system and is not sensitive to a magnetic field, the voltage at RX terminals is: VRX (t) = (IBDC + IBAC sin(2πfi t)) · RX
[V ]
(4)
Ideally, RX is considered to be equal to the MR sensor nominal resistance RN OM , and, by performing AC coupling and measuring the voltage difference between MR sensor and RX , the output voltage is given by (5). VRS (t) − VRX (t) = +
by using the Data Acquisition Toolbox to transfer the data acquired by the analog to digital converter (ADC) DT9836 to the pc. The developed software includes the driver to get the data from the ADC and allows demodulation, real time visualization of the signals, both in the time and frequency domains. Furthermore, it also includes real-time visualization of the calculated system noise; it has a 50 Hz notch filter to remove power grid interference and it is possible to use averaging to further decrease the measurement noise. Finally, it allows both time and frequency domain measurements to be stored in the hard drive. Each sub-block of the system is discussed with more detail in the following sub-sections. A. Hardware
R2
[(IBDC + IBAC sin(2πfi t)) · ∆R(H(t)) Vc (t)] [V ] (5)
Due to fabrication dispersion of RN OM , RX should be tuned in order to match the MR sensor resistance. From (5) it can be seen that ∆R(H(t)) appears at the baseband and around frequency fi , while Vc (t) is only present in the baseband. Thus, the two components can be measured separately, or, the desired magnetic signal can be measured by filtering Vc (t).
R3
Q2
Q3
IB
vB ampC
Q1 C1
Vmr R1
C2
R4
MR Sensors
RREF
III. P ROPOSED S YSTEM A RCHITECTURE Fig. 4. MR sensor biasing current source.
Hardware External Electronics
IC and External Electronics
LNA MR1
MR2
VGA
2nd order HPF
5th order LPF
MR15 Data Acquisition DT9836
PC
Software Signal Demodulation
Signal Visualization
50 Hz removal and averaging
Noise Measurement
Data Storing
vB
Fig. 3. Proposed system architecture.
A block diagram of the proposed instrumentation system is presented in Fig. 3. The system is composed of hardware and software parts. The hardware is composed of a frontend comprising a current source to bias the MR sensors, and an ultra-low noise AC coupled instrumentation amplifier to amplify the weak MR sensor resistance variation. Following the front-end, a variable gain amplifier, a 2nd order highpass filter (HPF) and a 5th order lowpass filter (LPF), provide amplification and limit the measurement bandwidth in order to remove out-of-band noise. The software part of the system is implemented in Matlab,
VREF Fig. 5. Wien bridge oscillator for DC+AC current generation. VREF generated by buffered voltage divider from the power supply.
1) Current Source: All the biasing current source noise is propagated to the output, therefore it has an high impact on the measurement resolution. Hence, a low noise current source is required. The biasing current source architecture is presented in Fig. 4. It is based on a typical voltage regulator structure
4
with negative feedback, but with a current output: a voltage vB , which can be DC or DC+AC, is applied to an amplifier (ampC ) that, due to the negative feedback, ensures a current iB , so that the voltage drop on RREF equals vB . The biasing current (iB ) is given by (6), where GC is the gain of ampC , implemented by an ultra-low noise amplifier (LT6230), and vB can be either a DC or a DC+AC voltage. When the desired biasing current is DC, a DC voltage is generated by an ultralow noise 1.25 V voltage reference circuit (LTC6655). In order to separate the capacitive coupling from the magnetic signal measurement, the biasing current must have an AC component. This component is generated by the circuit presented in Fig. 5, which is the schematic of a typical Wien bridge oscillator, where the low noise rail to rail amplifier LT1677 is used. RREF is a precision resistor and defines the biasing current. iB =
GC vB · vB ≃ 1 + GC · RREF RREF
[A]
(6)
The RC network, composed of C1 , C2 and R4 , ensures the loop stability; transistor Q1 and resistor R1 perform voltage to current conversion and the matched transistor pair Q2 and Q3 , with resistors R2 and R3 , mirror the current to the output branch, to bias the sensor, which makes ampC output excursion independent from the sensor voltage drop. The current source noise is given by (7), and the corresponding noise at the MR sensor terminals is given by (8). When compared to the MR sensor noise [11], it can be seen that the noise introduced by the current source can be highly attenuated: RREF is higher than the typical MR sensors resistance value (hundreds of Ω), ergo, ampC , vB and RREF noises are attenuated; moreover, √ √ ampC (LT6320) has a noise spectral density of 1.1 nV/ Hz at 10 kHz, and around 5 nV/ √ Hz at 10 Hz, and LT1677 √ has a noise density of 3 nV/ Hz at 10 kHz and 7 nV/ Hz at 10 Hz, which is considerably less than the MR sensor’s noise [11].
noise at the sensor terminals. The voltage gain was set to 100 for the DC measurements and to 20 for the DC+AC measurements, in order to decrease the next stage’s noise impact on the signal to noise ratio (SNR) of the measurement; AD8231 is a variable gain instrumentation amplifier, with digitally controlled voltage gain from 1 to 128. In spite of the neuronal signal being typically in the tens of µV range, stimulation artifact can have higher amplitude and, thereby, amplification gain should be configurable in order to avoid system saturation. After the signal is properly amplified, a 2nd order switchedcapacitor highpass filter (LMF100) is used to reduce the flicker noise, generated mainly by the MR sensor, and a 5th order switched-capacitor lowpass filter (LTC1062) is used to reduce high-frequency noise. Both filters cut-off frequencies are set by the, respectively, clocks CKLHP and CLKLP , that can be adjusted. 3) Control board: A control board was implemented to configure the amplifiers and filters of the system, for both DC and DC+AC measurements. For DC measurements, it allows manual selection of the voltage amplification gain, from 46 dB to 88 dB, and it provides two modes of operation: mode 1 has bandpass filtering from 50 Hz to 2.75 kHz; mode 2 has bandpass filtering from 300 Hz to 2.75 kHz being suitable for detecting higher frequency signals while reducing flicker noise. For the AC measurements, the gain can be changed from 26 dB to 68 dB and the bandwidth is increased to around 13 kHz. The frequency of the clock signals for the switched capacitor filters are generated by an integrated circuit 555 CMOS oscillator. IV. S OFTWARE I NTERFACE FS
di2B ≃ 2 dvMR
2 2 + dvB dvAmp C 2 RREF
+ di2rref
N
2
[A /Hz]
Carrier signal
= di2B · RS2 + dvr2s [V 2 /Hz] R 2 S 2 2 = dvampC + dvB + 4KT RREF RREF +
dvr2s
Configure ADC
(7)
2
[V /Hz]
(8)
Multiply and filter
Acquire
Yes
Demodulation?
No
Apply Notch filter
Yes
50 Hz removal? No
Gain Selector
CD1 Vmr VCM
CD2
RD1 RD2
CLKHP
CLKLP
LMF100 2nd order HPF
LTC1062 V o 5th order LPF
3 AD8429 AD8231
VCM
Fig. 6. Variable gain amplifier and bandpass filter.
2) Amplification and filtering: The amplification and filtering block diagram is presented in Fig. 6. The amplification is performed by two different amplifiers: AD8429 is an ultra-low noise instrumentation √ amplifier, having a noise spectral density less than 2 nV/ Hz for f > 10 Hz, thus, adding negligible
Average with previous acquisition
Yes Averaging? No
Noise visualization
Time domain plot
FFT plot
Yes Store data
Store Data?
Fig. 7. Data acquisition software block diagram.
No
5
The DT9836 ADC driver was implemented in Matlab, using the Data Acquisition Toolbox. The software summary is given by the block diagram of Fig. 7. After configuring the ADC with sampling frequency FS and number of samples per acquisition N , the data is retrieved. The ADC analog input signals are: the sensor signal, at the output of the hardware part of the instrumentation system; the stimulus applied to the tissue and the signal obtained from a recording electrode, for comparison purposes. If the MR sensor biasing current is DC+AC, demodulation is necessary to retrieve the MR sensor variation with the magnetic field independently from the capacitive coupling signal Vc . The MR sensor signal is demodulated as follows: the signal is bandpass filtered to remove Vc and the unmodulated magnetic component IBDC ∆R(H(f )). Then, the carrier signal vB with frequency fi is multiplied by the modulated signal, thus bringing the MR sensor signal back to baseband. Finally, the remaining higher frequency components are lowpass filtered. Although the proposed instrumentation system, the ADC and the PC are battery powered, 50 Hz magnetic interference from the power grid can be picked up by the MR sensors. For this reason, a 50 Hz notch-filter with 5 Hz bandwidth is implemented in the software. Another procedure to remove noise from the measurement is averaging. Considering that the sensor response always occurs after a precise delay from the stimulus [10], averaging attenuates single occurrence deviations from the expected responses. The software also computes the system noise level without stimulus, allowing to observe the impact of the contact with the brain tissue on the system overall noise level. All signals are displayed, both in time and frequency domains, and can be stored in the PC hard drive for further processing. V. N EW CMOS F RONT-E ND
SW21 M2 VB
SW11
SW2N M31 M3N SW1N SW31
M1 IS1
RS1
ISN
SW3N RSN
+ -
R
implemented in CMOS technology. Therefore, a CMOS frontend was designed, fabricated and tested, to demonstrate a future high-density MR sensor based neuronal signal detector. The circuits were separated in two ICs: one with the ultralow noise current source, that was previously presented in [14] and will be briefly discussed with emphasis on the MR sensor addressing and noise; the other is a low-noise variable gain amplifier. The IC’s, which were not yet included in the instrumentation system, were both implemented in AMS 0.35 µm 3.3 V CMOS technology. A. Integrated Current Source The CMOS current source circuit diagram, shown in Fig. 8, has been presented in [14], and is similar to the discrete electronics version of Fig. 4, in which the MR sensor biasing current is the output current of a voltage regulator inspired structure with negative feedback. In the discrete version of the circuit of Fig. 4, the MR sensors are manually selected by jumpers, which are noiseless. In the CMOS circuit, if the addressing were performed at the same location as in Fig. 4, the noise power from the addressing switches would add to the front-end noise, consequently degrading the system resolution. To overcome this, the MR sensor addressing is implemented by switches SW1,2i at the gates of transistors M3i , where i corresponds to the MR sensor that is being addressed. Considering sensor RS1 , by closing switch SW11 and opening switch SW2,1 , the current from transistor M1 is mirrored to M31 , thus biasing RS1 . Afterwards, switch SW3,1 connects the biased MR sensor to the low noise amplifier. Concerning the switches noise, since the addressing switches are at the gates of MOS transistors where the current is negligible, there is no flicker noise, which is the dominant noise source in band of interest. Also, because the switches are within a negative feedback loop, their thermal noise is attenuated by the current source amplifier gain, thus having a negligible impact on the front-end resolution. The current source employs chopper modulation [15] to reduce flicker noise, and has an NMOS differential pair to reduce the thermal noise [14]. The overall noise of the current source can then be given by (9), and, consequently, the noise at the sensor terminals is given by (10). It can be seen that, as in the discrete current source, the amplifier and RREF noise are attenuated by a factor RS /RREF , thus having a minor contribution when compared to the sensor noise. di2o ≃
RREF
dvo2 Fig. 8. Integrated circuit for MR sensor biasing.
The proposed instrumentation system, described in the previous section, is able to measure signals from a maximum of 16 sensors, if 16 current sources are designed. However, if an higher number of sensors is required, as in high-density neuronal measurements, this up scaling can only be achieved by having a front-end integrated circuit (IC) that can be
2 dvamp + di2rref 2 RREF
[A2 /Hz]
(9)
= di2o · RS2 + dvr2s [V 2 /Hz] R 2 S 2 = dvamp + 4KT RREF RREF + dvr2s
[V 2 /Hz]
(10)
B. Integrated amplifier A low noise variable gain two stage amplifier to amplify the weak MR sensor signals was designed and implemented.
6
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/*01234 "#$
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200
150
without chooping with chopping
100
50
0
@AB
10
1
10
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The first stage is a telescopic operational transconductance amplifier (OTA) with capacitive feedback, and is based on the amplifier presented in [16]. The amplifier has a bandpass characteristic, in which the highpass pole is established by C2 and Rpseudo ; Rpseudo is implemented with MOS-bipolar transistors [16]. The mid-band differential voltage gain is given by C1 /C2 and the lowpass corner frequency is given by the telescopic OTA gain-bandwidth product (GBW ) divided by the mid-band gain. The second stage is implemented with a current-mirror OTA with AC coupling in a non-inverting capacitive feedback structure. The gain is given by 1+C4 /C5 , where C5 can be changed by switch SWG in order to change the amplifier gain. The load capacitor CL2 is also switchable, to maintain the amplifier bandwidth independent from the mid-band gain.
C. New CMOS Front-End Experimental Characterization
3
4
10
10
Fig. 11. Experimental current source noise, with and without chopper modulation.
concluded that the current source noise is negligible when compared with the MR sensor noise. 500 Voltage Noise Density nV/sqrt(Hz)
Fig. 9. Integrated amplifier simplified schematic.
2
10 Frequency (Hz)
without chopping
450 400 350 300 250 200
output−sensor noise nV/sqrt(Hz)
()*
%'
%&
Current Noise Density pA/sqrt(Hz)
/*01234
20 15 10 5 0 0
5000 10000 Frequency (Hz)
150 100 50
with chopping 0
10
1
10
2
10 Frequency (Hz)
3
10
4
10
Fig. 12. Experimental output noise with MR sensor, with and without chopper modulation. Inset figure shows difference between the output noise and the MR sensor noise.
First Stage Second Stage Fig. 10. Current source IC microphotograph.
1) Integrated Current Source: The current source chip microphotograph is presented in Fig. 10, and, without pads, occupies an area of 350 µm × 100 µm. The current source noise is presented in Fig. 11 ( [14]), with and without chopper modulation, showing that the flicker noise is highly attenuated. Fig. 12 shows the output noise, consisting in the noise at the sensor terminals, which is biased by the IC current source (the sensor is the same as in the discrete instrumentation system noise measurements). From the inset figure in Fig. 12, which shows that the difference between the ouput noise power and the MR sensor noise power is approximately zero, it can be
Fig. 13. Integrated amplifier microphotograph.
2) Integrated Amplifier: The integrated amplifier chip microphotograph is presented in Fig. 13. The area with pads is approximately 0.1 mm2 and the current consumption is 8 µA with 3.3 V supply voltage. In Fig. 14 the amplifier transfer function is presented for the three different gains: 30 dB, 68 dB, and 73 dB. The low frequency cut-off corner is not visible due to a limitation of the measuring equipment, while the lowpass cut-off frequency is 5 kHz for the second stage unity gain configuration, and 2.5 kHz for the remaining
7
are implemented on the same board; it amplifies the weak neuronal signal without the possible interference from the switched-capacitor filter clocks. The pre-amplified signal is then fed to another board, where the remaining amplification and filtering is performed. Although the current source only biases one MR sensor, the amplification and filtering part were designed to allow the simultaneous reading of 16 MR sensors. Accordingly, a current source to bias 16 MR sensors is currently being developed.
100 Magnitude [dB]
80 60 40 20 0 −20 1 10
2
10
3
10 Frequency [Hz]
4
10
5
10
100
Fig. 14. Integrated amplifier experimental frequency response.
60 40 20
1000
0 1 10
2
10
3
4
10 10 Frequency [Hz]
5
10
800 Fig. 17. Instrumentation system frequency response for DC measurements.
600 400
80
200 0 1 10
60
2
10
3
10 Frequency [Hz]
4
10
5
10
Fig. 15. Integrated amplifier input referred noise experimental results.
Magnitude [dB]
Voltage noise density [nV/Sqrt(Hz)]
configurations. Fig. 15 shows the input referred noise measurements for the integrated amplifier. The total integrated in-band noise is 8.6 µVRMS for the maximum gain of 73 dB.
Magnitude [dB]
80
40
20
0
VI. P ROPOSED I NSTRUMENTATION S YSTEM E XPERIMENTAL R ESULTS
−20 1 10
2
10
3
10 Frequency [Hz]
4
10
5
10
Fig. 18. Instrumentation system frequency response for DC+AC measurements. Control Board
B. Frequency Response and Noise Measurements Variable gain amplifier + Filters
Current source+Preamplifier
Fig. 16. Proposed instrumentation system.
A. Instrumentation System Setup The proposed instrumentation system is presented in Fig. 16. The current source and the pre-amplifier AD8429
The system frequency response measurement was performed using HP 4195A Network Analyzer and is presented in Fig. 17 for the DC measurements (mode 1 of operation) and in Fig. 18 for the AC measurements, in each of the 8 different configurable gains: from 40 dB to 88 dB for the DC case, and from 26 dB to 74 dB, both with steps of 6 dB, for the AC case. The measured bandwidth is 50 Hz to 2.75 kHz for the DC case and around 13 kHz for the AC case. In Fig. 19 the modes of operation 1 and 2 are presented, where the highpass cut-off frequency is changed from 50 Hz to 200 Hz, to allow decreasing low frequency noise and detect only higher
8
100 Mode 1 Mode 2 Magnitude [dB]
80
60
40
20 1 10
2
10
3
10 Frequency [Hz]
4
5
10
10
Fig. 19. Instrumentation system modes 1 and 2 of operation: mode 1 allows detection of both low frequency neuronal signals and higher frequency spikes; mode 2 only allows the detection of spikes, but attenuates the flicker noise.
Amplifiers/Filters noise 80 Noise due to current source
C. Demodulation Process
60 Sensor noise −40
40
−60
20 0 1 10
2
10 Frequency [Hz]
Amplitude [dB]
Voltage noise (nV/sqrt(Hz)
100
The MR sensor integrated noise in the system bandwidth is 775 nVRMS , while the total integrated noise is 814 nVRMS (an increase of approximately 5%). Therefore, it can be concluded that the noise generated by the electronic circuits has only a marginal impact on the total noise, and allows a higher measurement bandwidth than the current measurement setup [10]. If the same bandwidth as in [10] is considered, the proposed instrumentation system has approximately a 50 times better noise performance. The noise of the proposed instrumentation system frontend, comprising the current source and the pre-amplifier, is further compared with the new CMOS front-end, presented in Section V, in order to validate its future integration in the instrumentation system: while the CMOS current source presents a noise comparable to the discrete current-source, the CMOS pre-amplifier exhibits a higher noise than the ultralow noise discrete amplifier AD8239. In the future, chopper modulation should also be used to decrease the low-frequency noise of the CMOS pre-amplifier, to increase the measurement resolution.
3
10
Fig. 20. Measured noise components referred to the MR sensor terminals.
Input signal (fs) Carrier signal (fi)
−80 Magnetic signal (fi−fs) −100 −120 −140
−180 2 10
100 80
3
10 Frequency [Hz]
4
10
Total input referred noise
Fig. 22. Example of modulated signal FFT. 60 −40
40
−60
20 0 1 10
2
10 Frequency [Hz]
3
10
Amplitude [dB]
Voltage noise density (nV/sqrt(Hz)
−160
Demodulated magnetic signal (fs)
−80 −100 −120
Fig. 21. Total measured noise referred to the MR sensor terminals. −140
frequency neuronal signals. The proposed instrumentation system noise level was characterized using the Rohde and Schwarz Baseband Signal Analyzer FMU36, with a 395 Ω spin valve MR sensor, for the mode 1 of operation and DC current. In Fig. 20 the noise components of the system are presented and in Fig. 21 the total noise is shown. Both measurements of Fig. 20 and Fig. 21 are referred to the sensor terminals, in accordance with (8).
−160 2 10
3
10 Frequency [Hz]
4
10
Fig. 23. Example of demodulated signal FFT.
In order to test the demodulation process, a 200 Hz sinusoidal magnetic field of approximately 1 Oe was applied to a 395 Ω spin valve MR sensor by an electromagnet. The MR sensor has a magnetoresistive ratio of approximately 7%. The circuit in Fig. 4 is used to bias the MR sensor
9
−3
3
Amplitude [V]
2
x 10
Demodulated signal Modulated signal
1 0 −1 −2 −3
2.007 2.008 2.009 2.01 2.011 2.012 2.013 2.014 Time [s]
Fig. 24. Example of signal demodulation in the time domain.
with a DC+AC current, with a DC value of 1 mA, an AC amplitude of 0.1 mA and frequency fi = 5.9 kHz. The same circuit is used to generate the same current to bias a precision thin film resistance in series with a potentiometer (RX , as described in (4)), with a calibrated value to match the MR sensor resistance. By measuring the difference between the MR sensor voltage and RX voltage, the obtained signal FFT, after amplification and filtering, is presented in Fig. 22, and is in accordance with (5). Due to the DC component of the MR sensor current, the magnetic field appears both at the excitation frequency of 200 Hz (fs ) and around the carrier frequency, at fi +fs and fi −fs . However, a possible capacitive coupling voltage only appears at fs , and is shown in Fig. 22 (together with the unmodulated magnetic signal) as the input signal. In order to digitally demodulate the magnetic signal only, the frequency fi − fs is chosen. A digital bandpass filter removes the unmodulated input signal at fs , and the remaining signal is then multiplied by the carrier frequency and lowpass filtered, to remove the modulation and demodulation higher frequency components. The FFT of the demodulated signal is presented in Fig. 23. For comparison purposes, the modulated and demodulated signals are also presented in the time domain, in Fig. 24. VII. C ONCLUSION This paper presented a neuronal signal detector for biologically generated magnetic fields. The system performs MR sensor biasing with DC or DC+AC ultra-low noise current and performs amplification and filtering of the MR sensor signals of up to 16 MR sensors. The results show that, when compared to the current measurement setup, the proposed instrumentation system increases the measurement bandwidth from 70 Hz to 2.75 kHz, and reduces the integrated noise in the signal bandwidth from 1 µVRMS to 814 nVRMS , which, for the same bandwidth, corresponds to a 50 times improvement in the noise performance. Furthermore, by applying a modulation scheme, it is ensured that only the MR sensor resistance variation, due to a magnetic signal, is measured. By biasing the MR sensors with a DC current, the system was able to successfully record the neuronal signals of in vitro experiments with mice brain slices. Nevertheless, due to
capacitive coupling, DC+AC experiments must be performed. To increase spatial resolution by increasing the number of sensors that can be read, and to enhance the system immunity to external interferences, two integrated circuits with an ultralow noise current source for MR biasing and a low-noise variable gain amplifier were presented and experimental results were given. The proposed current source effectively biased the MR sensor with a negligible noise contribution, while the integrated amplifier, although presenting a high gain, still suffers from excess noise. A chopper stabilized version of the integrated amplifier is currently being designed. Increasing integration of the electronics interface in CMOS technology, along with the improvement of the MR sensor’s sensitivity, can make this technology competitive with the use of high-density microelectrode arrays (voltage sensing), with the advantage of complete electrical isolation from the brain tissue. ACKNOWLEDGMENT This work was supported by national funds through FCT Fundac¸a˜ o para a Ciˆencia e Tecnologia, under projects PestOE/EEI/LA0021/2013 and EXPL/EEI-ELC/1029/2012, and by the PhD scholarship SFRH/BD/61569/2009. The authors also acknowledge funding through the European project FP7ICT-2011-7 (IMAGIC-GA288381). The authors wish to thank Professor Manuel Medeiros Silva for the technical revision of this manuscript. R EFERENCES [1] K. Wise, A. Sodagar, Y. Yao, M. Gulari, G. Perlin, and K. Najafi, “Microelectrodes, microelectronics, and implantable neural microsystems,” Proceedings of the IEEE, vol. 96, no. 7, pp. 1184–1202, 2008. [2] B. Eversmann, M. Jenkner, F. Hofmann, C. Paulus, B. Holzapfl, R. Thewes, D. Schmitt-Landsiedel, A. Lambacher, A. Kaul, R. Zeitler, M. Merz, A. Junze, and P. Fromhrz, “Cmos sensor array for electrical imaging of neuronal activity,” in Circuits and Systems, 2005. ISCAS 2005. IEEE International Symposium on, may 2005, pp. 3479 – 3482 Vol. 4. [3] P. Lopes, J. Germano, T. de Almeida, L. Sousa, M. Piedade, F. Cardoso, H. Ferreira, and P. Freitas, “Measuring and extraction of biological information on new handheld biochip-based microsystem,” Instrumentation and Measurement, IEEE Transactions on, vol. 59, no. 1, pp. 56 –62, jan. 2010. [4] J. Germano, V. Martins, F. Cardoso, T. Almeida, L. Sousa, P. Freitas, and M. Piedade, “A portable and autonomous magnetic detection platform for biosensing,” Sensors, vol. 9, no. 6, pp. 4119–4137, 2009. [5] F. A. Cardoso, T. Costa, J. Germano, J. Borme, J. Gaspar, J. R. Fernandes, M. S. Piedade, and P. P. Freitas, “Integration of magnetoresistive biochips on a cmos circuit,” IEEE Transactions on Magnetics, vol. 48, no. 11, 2012. [6] D. Hall, R. Gaster, K. Makinwa, S. Wang, and B. Murmann, “A 256 pixel magnetoresistive biosensor microarray in 0.18 m cmos,” SolidState Circuits, IEEE Journal of, vol. 48, no. 5, pp. 1290–1301, 2013. [7] J. Loureiro, P. Z. Andrade, S. Cardoso, C. L. da Silva, J. M. Cabral, and P. P. Freitas, “Magnetoresistive chip cytometer,” Lab Chip, vol. 11, pp. 2255–2261, 2011. [8] P. P. Freitas, F. A. Cardoso, V. C. Martins, S. A. Martins, J. Loureiro, J. Amaral, R. C. Chaves, S. Cardoso, L. P. Fonseca, A. M. Sebastio, M. Pannetier-Lecoeur, and C. Fermon, “Spintronic platforms for biomedical applications,” Lab Chip, vol. 12 (3), pp. 546–557, 2012. [9] V. C. Martins, F. A. Cardoso, J. Germano, S. Cardoso, L. Sousa, M. Piedade, P. P. Freitas, and L. P. Fonseca, “Femtomolar limit of detection with a magnetoresistive biochip,” Biosensors and Bioelectronics, vol. 24, pp. 2690–2695, 2009. [10] J. Amaral, S. Cardoso, P. P. Freitas, and A. M. Sebastiao, “Toward a system to measure action potential on mice brain slices with local magnetoresistive probes,” J. of Applied Physics, vol. 109, no. 7, 2011.
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