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Wireless Multichannel Neural Recording With a 128-Mbps UWB Transmitter for an Implantable Brain-Machine Interfaces H. Ando, Member, IEEE, K. Takizawa, Member, IEEE, T. Yoshida, Member, IEEE, K. Matsushita, M. Hirata, Member, IEEE, and T. Suzuki, Member, IEEE

Abstract—Simultaneous recordings of neural activity at large scale, in the long term and under bio-safety conditions, can provide essential data. These data can be used to advance the technology for brain-machine interfaces in clinical applications, and to understand brain function. For this purpose, we present a new multichannel neural recording system that can record up to 4096-channel (ch) electrocorticogram data by multiple connections of customized application-specific integrated circuits (ASICs). The ASIC includes 64-ch low-noise amplifiers, analog time-division multiplexers, and 12-bit successive approximation register ADCs. Recorded data sampled at a rate of 1 kS/s are multiplexed with time division via an integrated multiplex board, and in total 51.2 Mbps of raw data for 4096 ch are generated. This system has an ultra-wideband (UWB) wireless unit for transmitting the recorded neural signals. The ASICs, multiplex boards, and UWB transmitter unit are designed with the aim of implanting them. From preliminary experiments with a human body-equivalent liquid phantom, we confirmed 4096-ch UWB wireless data transmission at 128 Mbps for distances below 20 mm. Index Terms—Brain-machine interface, electrocorticogram, implant, multichannel recording, ultra-wideband, wireless.

I. INTRODUCTION

A

great deal of research has been performed on brain-machine interfaces (BMIs). BMIs have the potential to enable us to control machines such as prosthetic arms or communication tools using only neuronal signals. In clinical applications,

Manuscript received July 13, 2015; revised November 02, 2015; accepted December 21, 2015. This work was supported in part by the Strategic Research Program for Brain Sciences by the Ministry of Education, Culture, Sports, Science and Technology of Japan. The VLSI chip in this study was fabricated under the chip fabrication program of the VLSI Design and Education Center, the University of Tokyo in collaboration with Rohm Corporation and Toppan Printing Corporation. This paper was recommended by Associate Editor T. Denison. H. Ando, K. Takizawa, and T. Suzuki are with the Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Osaka 565-0871, Japan (e-mail: [email protected]). T. Yoshida is with the Graduate School of Advanced Sciences of Matter, Hiroshima University, Hiroshima 739-8530, Japan (e-mail: [email protected]). K. Matsushita and M. Hirata are with the Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Osaka, 565-0871, Japan, and also with the Department of Neurosurgery, Osaka University Medical School, Osaka 565-0871, Japan (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TBCAS.2016.2514522

BMI technology can help patients suffering from diseases such as amyotrophic lateral sclerosis (ALS), spinal cord injury, and paralysis. Neural activity can be recorded in several ways. Although multichannel spike signals have sufficient information for decoding the trajectory and velocity of the hand and arm [1]–[4], recording methods with microelectrode arrays such as the Utah type are not suitable for BMIs requiring long-term stability [5]–[7]. Local field potentials (LFPs) are also recorded by penetrating invasive intracortical electrodes. LFPs have an advantage over multichannel spike signals in terms of long-term stability and providing accurate estimations of movement intentions [8]. However, spike signals and LFPs are highly invasive recording methods, because the electrodes directly penetrate into the brain. If we apply these methods to clinical BMI, we must consider reliability, safety, and long-term stability [9]. Moreover, we must consider how we can remove the electrodes without damaging the brain if neural activity is not recorded. With electrodes on the brain surface, electrocorticograms (ECoG) are less invasive and relatively safe because the brain is not damaged. ECoGs also have sufficient spatial resolution and provide rich information regarding movements in the same way as LFPs [10], [11]. Although spike and LFP research is based mostly on animal experiments, ECoGs are often recorded and analyzed in human clinical experiments; real-time estimations of finger movements and arm trajectories can also be produced [12]–[15]. Thus, for clinical applications, we believe that ECoGs are the desirable approach for neural signal recording in BMIs because it offers low invasiveness, long-term stability, and good performance. When producing a BMI system, it is important to develop a customized application-specific integrated circuit (ASIC) that includes several low-noise amplifiers tuned for ECoG in voltage level and bandwidth, an analog-to-digital converter (ADC), and a high-speed serial interface [16]. Moreover, if the system is implanted for free-moving animal experiments or clinical applications, recorded digital data by the ASIC must be transmitted wirelessly from inside to outside of the body. The best method is where all of these devices are included in one hermetic case and fully implanted, such as in an implantable pacemaker or deep brain stimulator [17], [18]. Therefore, we have developed a fully implantable wireless 128-channel (ch) ECoG-BMI system [19], [20]. This system consists of two 64-ch neural recording ASICs (including 64 low-noise amplifiers (LNAs), two 32:1 analog

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TABLE I COMPARATIVE ANALYSIS OF PRESENT WIRELESS DATA TRANSMISSION TECHNOLOGY

Fig. 1. Conceptual future implantable BMI system.

multiplexers (MUXs) and two ADCs), a Wi-Fi-based data transmitter (1.6 Mbps data rate at 128 ch), a wireless power supply unit (266 kHz, 400 mW), and a rechargeable Li polymer battery (3.7 V, 400 mAh). This is a prototype of our system, and we intended to separate the neural recording module and the wireless data/power transmission module [19], [20]. For more accurate estimation of movement intentions in BMI and the elucidation of brain mechanisms in neurophysiology, it is important to develop a multichannel and large-scale recording system that has more than 1,000 ch and records brain signals at several regions simultaneously [21], [22]. Implantable and wireless device development is also required for clinical applications and natural environment research. Fig. 1 shows a conceptual future vision of our target: implantable, wireless, distributed, multichannel BMI. Inside to outside body wireless communication for BMI is a challenge, especially in such multichannel systems: high data transfer rate over 10 Mbps, radio interference and interruption, damage to the human body, and degradation of communication quality through human body radio wave absorption. Several wireless technology comparisons for wireless multichannel BMI applications are presented in Table I. The medical implant communication service (MICS) has the most advantages in terms of radio interference and biocompatibility because it is generally used with medical implants such as pacemakers;

however, the actual data rate of the MICS is low for multichannel BMI. Conversely, Wi-Fi has a high data rate—up to 6933 Mbps using the IEEE 802.11ac standard [23]—but radio interference is inevitable because our living environment is filled with Wi-Fi-based consumer products, such as smartphones, computers, and microwave ovens. Moreover, Wi-Fi (and Bluetooth) is not based on peer-to-peer communication but on multi-input-multi-output; instead, Wi-Fi must initiate with career-sensing. Therefore, Wi-Fi is not well suited to a real-time, continuous- and constant-latency system such as BMI. Ultra-wideband (UWB) wireless technology is not generally used at the consumer level; recently, IEEE 802.15.6, which is the first international Wireless Body Area Network standard, incorporated used of UWB and now supports communications near or inside a human body to serve a variety of medical and nonmedical applications. UWB has several advantages: a high data rate over 100 Mbps, less interference because of low output power (below 41.3 dBm/MHz), feasibility for real-time peer-to-peer communication, and implantable antenna size below 1 cm considering 1/4 wavelength. Thus, UWB is the best technology for multichannel wireless BMI [24]–[26]. In this paper, we introduce a novel multichannel recording system that is able to transmit up to a 51.2-Mbps actual data rate with a 128-Mbps UWB transmitter. The system can support up to 4096 ch of ECoG signals (1 kSps, 12-bit digital data per channel) through multiple connections of 64-ch ASICs and time division multiplexing of the recorded data. This paper is organized as follows. Section II describes the system architecture of the proposed multichannel recording and wireless transmitter. The test results of the developed system are shown in Section III; Section IV concludes this paper. II. METHODS The block diagram of the proposed multichannel recording system and supposed realization image of this system are shown in Figs. 2 and 3, respectively. The system mainly consists of ECoG electrodes, recording ASICs, two type MUX modules, a UWB transmitter module, and a UWB receiver module. Note that this system is an advanced version of our previous one, especially intended for UWB high-data-rate wireless communication. The neural recording module and wireless data transmission module are intended to be implanted separately as shown in Fig. 3, and separated modules are

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Fig. 2. Schematic diagram of the system.

Fig. 3. Image showing a realization of the developed system.

connected by a flexible coaxial subcutaneous cable, which can transmit high data rate digital signals. ECoG neural signals are amplified, filtered and converted into 12-bit digital data by custom-made neural recording ASICs that can record 64 ch per chip. Time-division multiplexing is applied to recorded digital data by the MUX module. This system can connect a maximum of 64 ASICs corresponding to 4096-ch recording. The multiplexed data are wirelessly transmitted from inside the body by a UWB transmitter module at a 128-Mbps data rate, and received by a UWB receiver module located around the surface of the body. Received data are read into the computer via a USB2.0 connection, and displayed in a real-time wave viewer written in Borland C++. The ZigBee module is needed for our system to control implants because the direction of UWB communications is one way (i.e., inside to outside). The system implantation is assumed to be as follows: MUX modules with neural recording ASICs and UWB transmitter module are placed inside of the head and abdomen, respectively. The following sections describe each module in detail. A. Neural Recording ASIC The proposed system architecture of the 64-ch neural recording ASIC [Fig. 4(a)] is based on our previously reported design in [27]. The system provides AC amplification, band-pass filtering, and digitization to the recorded neural signals with fully programmable gain, bandwidth and channel selection via a serial interface. The analog front-end (AFE) consists of an LNA and a variable gain amplifier (VGA) [Fig. 4(b)]. The outputs of the 32-ch AFEs are multiplexed

by a 32:1 time division analog MUX and buffered by voltage follower opamp (Buff). The LNA, VGA and MUX operates fully differential, and the output of Buff is single-ended. The MUX output is digitized at 32 kSps using a 12-bit charge-redistribution successive approximation register (SAR) ADC. The ASIC includes two of these 32-ch recording units (32-ch AFEs, one MUX, and one ADC) [Fig. 4(a)], and the outputs of two ADCs are digitally multiplexed. As a result, the sampling rate of the AFE is 1 kSps, and the total amount of ECoG data per second becomes 768 kbits . Therefore, the ASIC transmits the 768 bits of raw data stream every 1 ms serially. The sampling frequency of 1 kHz is appropriate for BMI because over 100 Hz brain activity (high gamma) is useful to decode movement intention [15]. The operation condition of each channel can be changed flexibly. Via the serial interface, the low cut-off frequencies of 0.1, 1, and 10 Hz and the high cut-off frequencies of 240, 500, and 1000 Hz are selectable at each AFE independently. In some cases, voltage variations not related to the ECoG at low frequency (DC drift) may occur because of polarization voltages caused by electrodes. Therefore, we added a 0.1-Hz low cut-off frequency to remove this noise component. Similarly, the total gain of the AFE is adjustable from 40 to 80 dB in 10 dB steps. The generally used condition is 1- to 240-Hz frequency band and 60 dB gain. The LNA and VGA [Fig. 4(b)] consists of a fully differential op-amp, AC-coupled and feedback capacitors, and feedback pseudo-resistors implemented using cascade-diode-connected MOSFETs. A roll-off frequency of the high-pass filter is given by , and a gain of LNA is given by , where is 100 times that of , 50 fF. To achieve a value of less than 0.1 Hz of low roll-off frequency, the resistance value must be of the order of 1 . Therefore, we used a tunable pseudo-resistor implemented by the cascade MOSFETs operating in the sub-threshold region [16], [27]. Using multiple cascades, the resistance of the pseudo-resistor is multiplied by the resistance of a single MOSFET. Moreover, the drain-source voltage change of each MOSFET, which causes a distortion of the amplifier, is reduced by the number of cascaded MOSFETs. In addition, a wide variable range of bandwidth is accomplished by adjusting the gate voltage of MOSFET using a DAC. For a high voltage gain and wide output swing, a folded-cascade op-amp is used for both LNA and VGA. A roll-off frequency of the low-pass

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Fig. 6. Microphotographs of the 5.18-mm integration board.

5.18-mm ASIC and miniature

Fig. 4. Architecture of (a) neural recording ASIC, and (b) LNA and VGA.

is shorted to the , and the offset voltage of pre-amplifier is stored in . The CK1 is then low and CK2 is high after a delay, the amplified output in consideration of the offset voltage is input to the latch comparator. As a result, accurate comparison is achieved between the ADC input and the reference voltage generated by a split array capacitor DAC. A chip micrograph of the ASIC fabricated using 0.18CMOS technology and an integrated measurement board is shown in Fig. 6. The ASIC is integrated via a wire bonding process to a miniature integration board that has 64-ch input pads, 8-ch reference pads and a 11-pin flexible printed circuits connector for supplying power/ground, serial data and control signals. B. Multiplexer Module for Time Division Multiple Connection

Fig. 5. (a) 12-bit successive approximation register ADC. (b) Comparator with preamp and offset canceller.

filter is proportional to the capacitance , and we choose 30 pF, 60 pF, and 134 pF selectable for realizing 1000 Hz, 500 Hz, and 240 Hz low-pass frequencies, respectively. The variable gain of the AFE is achieved in VGA and Buff by selecting the capacitor value accordingly. In our design, is 1.5 pF and is adjustable to 50, 100, 350, and 1000 fF. Fig. 5(a) shows a schematic of the ADC. The ADC consists of a 12-bit split array capacitor DAC, a sample and hold switch, a latch comparator with preamp and offset canceller, and SAR logic. The value of the unit capacitance is 400 fF. This results in a total of 51.6 pF. The is generated by a resistive voltage divider with a pair of 50- resistors, and this divider consumes 18 . In the ADC, the is only used for comparison operation, so the divider does not need to charge the 51.6-pF capacitor. Therefore, the reference buffer is not included in the ASIC. Fig. 5(b) shows a schematic of the comparator which consists of pre-amplifier for offset cancellation, latch comparator, and RS flip-flop. When CK1 is high, the input of the pre-amplifier

Fig. 7 shows a photograph and block diagram of the MUX module that enables the connection of multiple ASICs. This module consists of a 48-MHz crystal, a small and low power field programmable gate array (FPGA) including a phase-locked loop (PLL), two low dropout regulators, two flash memories for storing ASIC’s operation parameters, and flexible printed circuit connectors. If a set comprising of one MUX module and a UWB transmitter is used, a maximum of eight integration boards can be connected and 512-ch recording, which corresponds to a 6.4-Mbps data rate, can be achieved [see Fig. 7(c)]. For more multiple connections, the MUX modules can be connected to each other in a cascade. Therefore, a maximum 4096-ch recording system, corresponding to a 51.2-Mbps data rate, can be realized if we connect eight MUX modules to one MUX module [Fig. 7(d); also Fig. 2]. The data format conversion to UWB is processed in core logic. C. UWB Transmitter Module Photographs and the system architecture of the UWB transmitter module (Fig. 8) show this module consists of a base board that receives neural recording data and manages power control, a radio frequency (RF) board transmitting UWB data, a band-pass filter board, an RF antenna, and a ZigBee transceiver

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Fig. 7. (a) Photographs of the multiplexer board. (b) Block diagram. (c) MUX and UWB transmitter connection in 512-ch recording and (d) 4096-ch recording.

board for controlling system operation. The necessity of an additional wireless board is that the UWB transmitter is only used for data transmission. We use impulse radio UWB (IR-UWB). A photograph and block diagram of our custom designed UWB chip are shown in Fig. 8(c) and (d), respectively. The GaAs HBT UWB chip is assembled on a printed circuit board measuring 20 mm by 35 mm using the flip-chip bonding technique. Because no bonding wires are required in this assembly method, degradation by the signal path is minimized in the module. A band-pass filter is placed between the antenna and UWB chip to comply with country regulations. The UWB chip consists of a transmitter and receiver in one GaAs HBT chip along with the required power regulator. Therefore, we use the same UWB-RF board in the UWB transmitter and receiver by changing the function of the chip. First, a TX-trigger pulse from the baseband logic which is included in the Core Logic of the MUX module is applied to the TX In port. Then the impulse generator block creates accordingly an UWB impulse at the down edge of the TX In signal. Finally, a generated UWB impulse is amplified up to the power level of 41.3 dBm/MHz by the pre-power amp (Pre-PA) and power amp (PA), then drives the antenna via the switch. The received UWB signal at the antenna is loaded to the LNA via the antenna switch and amplified with about 40 dB gain. The impulse envelop is produced by the envelop detector, and the resulting half-sign-shaped

Fig. 8. (a) Photographs of the UWB transmitter. (b) Photographs of the UWB transmitter in the waterproof casing. (c) Photograph of the UWB-RF board. (d) Block diagram of the UWB chip. (e) UWB transmitter module.

wave is amplified with a further 40-dB gain, then digitized by a comparator. We target the UWB high-band frequency, 7.25 to 10.5 GHz, to comply to international usage, and choose a center frequency of 7.9 GHz and a frequency bandwidth of 1.25 GHz. A bandwidth than 1.25 GHz is enabled if the band-pass filter board is unused. A low-voltage differential signaling interface is used to communicate with the UWB transmitter at the high data rate. The UWB transmitter, a Li polymer rechargeable battery, and an antenna are put into the waterproof case [Fig. 8(b)]. The main power on/off control is performed by a magnetic switch. According to IEEE standard C95.1-2005 concerning bio-safety levels with respect to human exposure, the maximum

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Fig. 10. Graphical user interface.

neural waveforms displayed in real time, but also the parameter settings of the ASICs such as gain and bandwidth can be controlled using this software. In this window, we can also register the number of sampling, elapsed time, battery voltage in UWB transmitter, and packet error rate. The simultaneous viewable number of channels in one window is 16, and the waveforms of other channels can be displayed by switching the display page (e.g., display page 2 shows data from channels 17 to 32, display page 256 shows data from channels 4081 to 4096). Although the simultaneous viewable number of channels in one window is limited, more neural waves can be presented simultaneously on another PC using a network mode with Ethernet connections. III. RESULTS Fig. 9. (a) Photographs of the UWB receiver. (b) Block diagram.

permissible exposure (MPE) is given as at 8 GHz. Therefore, it should be safe for the human body even if all of the RF energy emitted by the UWB transmitter is absorbed as the transmission power is less than 0.1 mW. D. UWB Receiver Module Photographs of the UWB receiver module and diagram of its system architecture are given in Fig. 9. This module consists of an UWB RF board that receives and processes UWB wireless data, an expansion board for controlling ZigBee and eight 7-segment LEDs which indicate the sampling time in hexadecimal, and a helical-type antenna for improving the receiving sensitivity. The size of the UWB receiver module is about , and it is intended to be placed around the waist, just above the implanted antenna. The received UWB data are transmitted to a PC using a USB2.0 wired cable. The elapsed time from the start of the measurements counted in the MUX module is shown using eight 7-segment LEDs. Therefore, if both situations of measurements and LEDs are recorded together using a video camera, we can align the behavior and recorded neural data accurately. E. Software Application (GUI) A screen shot of the GUI is shown in Fig. 10. This software is developed based on Borland C++. Not only are the recorded

A. Neural Recording ASIC The measurement results of the LNA and ADC are shown in Fig. 11. The LNA achieves a variable bandwidth from 0.1 to 1000 Hz, variable gain from 40 to 80 dB, and an input-referred noise of from 1 to 500 Hz [Fig. 11(a)–(c)]. Having understood that the performance degradation problem is caused by parasitic elements in the split-array-capacitor DAC, we carefully simulated the ADC laid out with parasitic elements and optimized the layout of this DAC. As a result, the measured differential nonlinearity (DNL) and integral nonlinearity (INL) of the 12-bit SAR ADC are within 1.5 LSB. The ADC achieves a signal-to-noise and distortion ratio of 62 dB and a power consumption of 140 at 32 kSps. The total power consumption of the chip is 5.4 mW at a supply voltage of 1.8 V. Some specifications of the ASIC are listed in Table II. Recordings of neural signals from a monkey are shown in Fig. 12. These results were obtained using our previous Bluetooth-based wireless system. B. 4096-ch Recording Performance in Wired System We evaluated the performance of 4096-ch data correction using a simulated 4096-ch recording system [Fig. 13(a)]. Although all of the ASICs were not connected to the 512-ch base unit, the MUX board emulates the ASICs serial data input and outputs the 6.4-Mbps recorded data to the next MUX module for 4096ch, which is connected to the UWB transmitter via a FPC cable. In this setup, the UWB receiver and transmitter were wired via an U.FL cable. Fig. 13(b) gives the measured packet

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TABLE II SPECIFICATIONS OF THE ASIC

Fig. 12. Measured ECoG signals obtained using the ASIC.

Fig. 13. (a) 4096-ch recording test measurement setup. (b) Evaluated packet error rate.

Fig. 11. Measured characteristics of the ASIC. (a) Gain characteristics of the LNA at each bandwidth setting. (b) Selectable gain characteristics at 1- to 500-Hz bandwidths. (c) Input-referred noise of LNA at 1- to 500-Hz bandwidths. (d) INL and DNL of the ADC.

error rate from the PC of the 4096-ch data correction, the data of which was all transmitted by the UWB receiver via a USB2.0 connection. In each trial, we achieved 10 minutes of continuous recordings and counted ECC uncorrected packet. The average of the counts was 4550 and the packet error rate was 0.025%.

Hence, our developed data correction system can record up to 4096-ch ECoG signals. C. UWB Wireless Performance Our developed UWB wireless performance was measured in both wired and wireless conditions using a human body-equivalent liquid phantom, which has an electrical conductivity of 3.10 S and relative permittivity of 5.88 at 8 GHz. The characteristics of the UWB were measured by directly wiring the UWB transmitter to a spectrum analyzer (Fig. 14). The UWB has a peak power spectrum of around 7.9 GHz center frequency and

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Fig. 14. Measured frequency spectrum of the UWB signals.

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Fig. 16. (a) Diagram and (b) photograph of the measurement setup.

Fig. 17. Packet error rate as a function of implant device depth .

TABLE III PERFORMANCE SUMMARY OF THE SYSTEM

Fig. 15. (a) Diagram and (b) photograph of the measurement setup.

a spread in bandwidth of 1.25 GHz. The total power of this spectrum is 12.3 dBm. To imitate the implant environment, we measured the UWB transmission performance using our human-body equivalent. A diagram and photograph of the measurement setup are shown in Fig. 15(a) and (b), respectively. The liquid phantom is contained in a cylinder of diameter 300 mm. The gap between cylinder and receiver antenna was set to about 10 mm. Then, varying the position of the transmitter changes distance [Fig. 15(a)] from the surface of the cylinder, thereby imitating the depth of the implant device. In this setup, the received signal strength (RSS) and packet error ratio (PER) are measured using a spectrum analyzer and our UWB receiver, respectively. The plot of RSS against distance (Fig. 16) shows a drop of about 20 dB using wireless because the RSS is about 30 dB at . The RSS drops linearly with distance, and is about 55 dBm at . Beyond 25 mm, we could not measure RSS accurately (due to saturation) because of noise from

the spectrum analyzer. From the fitted curve, RSS drops at a rate of 5.5 dB per 5-mm. The PER measured using the UWB receiver is shown in Fig. 17. Note that our UWB transmitter is designed to be implanted, and optimized for a low RSS level. Therefore, the PER is high and wireless communications are unstable below 5 mm because a strong RSS causes a voltage saturation of the UWB receiver LNA (Fig. 8) and the received data cannot be decoded correctly. As shown in Fig. 17, stable UWB communications are achieved at below 0.1% PER below 20 mm. UWB is unstable again at greater depths because the RSS is low and weak RF signals cannot be decoded. Hence, at 21 mm, PER is above 1%, and wireless communications cannot be established

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TABLE IV COMPARISON OF STATE-OF-THE-ART WIRELESS NEURAL RECORDING SYSTEMS

Fig. 18. Input-referred noise of the system.

beyond 22 mm. As a result, our UWB system can operate assuming a 20-mm implant depth. We evaluated the system’s input-referred noise by connecting wirelessly the ASIC to a 4096-ch recording system. The inputreferred noise of the LNA is estimated at about , roughly . Moreover, the measured input-referred noise of the whole system is (Fig. 18). Thus, the noise from the LNA obviously dominates and the UWB wireless system does not affect the total noise. Table III summarizes the performance of the system. IV. CONCLUSION AND DISCUSSION We have presented a prototype of a multichannel wireless data transmission system using high-band IR-UWB to allow for international usage. The developed MUX module can connect eight 64-ch ASICs and a maximum 4096-ch data recording/ transmission is enabled using cascading MUX modules. The performance of the 128-Mbps UWB transmitter module is sufficient for transmitting 4096-ch data corresponding to 51.2 Mbps. By communication experiments using a human body-equivalent liquid phantom, the capability of transcutaneous communication within 20 mm is demonstrated. Table IV shows a comparison of state-of-the-art wireless neural recording system. Our wireless system has a higher data rate than other systems. However, the system power consumption is relatively large because we intend to implant our system and a powerful power amplifier must be needed for UWB to overcome the strong absorption of radio waves in

human tissue. For implantable devices, the power consumption is one of the most critical parameters. The system operating time with a 3.7-V 400-mAh Li ion battery might be about 1 hour because the system power consumption in a 4096-ch recording is about 1400 mW. If an implant is operating while concurrently being wirelessly charged, we believe that a 1-hour operating time is reasonable because the implanted battery will be used only during emergencies such as a sudden loss of wireless power. The patient equipped with a BMI system will be safe because someone can give care while the battery is operating. However, for patients, wearing external devices throughout the day is discomforting. An operating time of at least 24 hours is beneficial for fully wireless operations because the patient does not need to wear the external device during the day and battery recharging can be performed during sleep. In this situation, we must reduce the power consumption below 100 mW with the same battery specification. Although we have developed the ASIC, MUX, and UWB transmitter to realize our future concept of fully implantable BMI systems (Fig. 1), the wireless data transfer module is separate from the head implant (Fig. 3) and the wireless power transfer module is still not included in the present system. Therefore, we plan to improve these modules by down-sizing the neural recording part by directly bonding the ASIC to the electrode as for a flip-chip and designing a more power-efficient UWB chip with another technology such as SiGe Bi-CMOS. Moreover, we intend to develop a one-package solution within a titanium skull head casing, which includes all of the electronics. Because our present ASIC was originally developed in 2011 [27], the performance is not as good as state-of-the-art designs. Therefore, we are also going to design a new ASIC by changing the process technology to improve the performance by improving input-referred noise, integration degree of channel per chip, resolution of ADC, and generally using a serial interface like SPI or I2C. In our developed UWB system, wireless performance does not only depend on depth, but also lateral offset between the TX and RX antenna, and this sensitivity increases with distance. Therefore, we must position the TX and RX antenna carefully to

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within an accuracy of a few centimeters when the device is implanted. We used the COTS ZigBee module to control implants from outside; the size and power of this is, however, unsuitable for an implant. However, if we use transcutaneous UWB communications, the size of the implant including the antenna must be larger to handle radio-wave absorption. Therefore, we shall be replacing it with a chip-level implementation, taking into account other wireless systems such as MICS. Our future work also includes in vivo experiments using actual implantation, and the demonstration of accurate and speedy ECoG BMI.

ACKNOWLEDGMENT The authors thank A. Iwata of A-R-Tec Corporation for his help in designing ASICs, and H. Nishikawa of Global Interface Technologies Inc. for his help in developing MUX and UWB modules. They also thank Y. Nishimura of the National Institute for Physiological Science, H. Watanabe of Tohoku University, and T. Umeda of the National Center of Neurology and Psychiatry for their help in animal experiments.

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K. Takizawa (M’03) received the B.E., M.E., and Ph.D. degrees in engineering from Niigata University, Niigata, Japan, in 1998, 2000, and 2003, respectively. He joined the Communications Research Laboratory, now the National Institute of Information and Communications Technology.

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. ANDO et al.: WIRELESS MULTICHANNEL NEURAL RECORDING WITH A 128-Mbps UWB TRANSMITTER

T. Yoshida (M’98) received the B.E., M.E., and Ph.D. degrees in engineering from Hiroshima University, Hiroshima, Japan, in 1994, 1996, and 2004, respectively. From 1996 to 2001, he was with System Electronics Laboratories, Nippon Telegraph and Telephone Corporation. Currently, he is an Associate Professor at the Graduate School of Advanced Sciences of Matter, Hiroshima University.

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M. Hirata (M’14) received the B.E. and M.E. degrees from the Faculty of Engineering, The University of Tokyo, Tokyo, Japan, in 1985 and 1987, respectively, and the M.D. and Ph.D. degrees from Osaka University Medical School, Osaka, Japan, in 1994 and 2001, respectively. He is a board-certified Neurosurgeon specializing in functional neurosurgery. He was promoted to a Specially Appointed Associate Professor, Department of Neurosurgery, Osaka University Medical School, serving as leader of the neural engineering group.

K. Matsushita received the B.E. degree from the Tokyo University of Science, Tokyo, Japan, the M.Sc. degree from the University of Sussex, Brighton, U.K., and the Ph.D. degree from The University of Tokyo, Tokyo, Japan, in 2000, 2004, and 2007, respectively. From 2007 to 2009, he was a JSPS Research Fellow and a Specially Appointed Assistant Professor at The University of Tokyo. Since 2009, he has been a Specially Appointed Assistant Professor in the Department of Neurosurgery, Osaka University Medical School, Osaka, Japan.

T. Suzuki (M’98) received the B.E., M.E., and Ph.D. degrees in engineering from The University of Tokyo, Tokyo, Japan, in 1993, 1995, and 1998, respectively. From 1998 to 2002, he was a Research Associate at The University of Tokyo. From 2002 to 2012, he was an Assistant Professor at the Graduate School of Information Science and Technology, The University of Tokyo. Since 2012, he has been a Senior Researcher at the National Institute of Information and Communications Technology.