Brain–Machine Interface Using
Brain Surface Electrodes: Real-Time Robotic Control and a Fully
Implantable Wireless System
Masayuki Hirata1, Takufumi Yanagisawa1, Kojiro Matsushita1, Hisato Sugata1, Yukiyasu
Kamitani2, Takafumi Suzuki3, Hiroshi Yokoi4, Tetsu Goto1, Morris Shayne1, Yoichi Saitoh1,
Haruhiko Kishima1, Mitsuo Kawato2, and Toshiki Yoshimine1
1
Department of Neurosurgery, Osaka University Medical School, Japan2
ATR Computational Neuroscience Laboratories, Japan
3
Graduate School of Information Science and Technology, The University of Tokyo, Japan
4
The University of Tokyo, Interfaculty Initiative in Information Studies, Japan
ABSTRACTThe brain-machine interface (BMI) enables us to control machines and to communicate with others, notwith the use of input devices, but through the direct use of brain signals. This paper describes theintegrative approach we used to develop a BMI system with brain surface electrodes for real-time roboticarm control in severely disabled people, such as amyotrophic lateral sclerosis patients. This integrative
BMI approach includes effective brain signal recording, accurate neural decoding, robust robotic control,a wireless and fully implantable device, and a noninvasive evaluation of surgical indications.

INTRODUCTION
The brain-machine interface (BMI) is a man-machine interface that enables us to control machines andto communicate with others not with the use of input devices, but through the direct use of brain signals
alone (Figure 1). Several diseases and conditions can lead to the loss of muscular control without adisruption in patients’ brain function, including amyotrophic lateral sclerosis (ALS), brainstem stroke,
spinal cord injury, and muscular dystrophy, among others. BMI technology offers these patients greater
independence and a higher quality of life by enabling the control of external devices to communicate withothers and the ability to manipulate their environment at will (Wolpaw, Birbaumer, McFarland,
Pfurtscheller, & Vaughan, 2002).
There are two types of BMI: invasive BMI and noninvasive BMI. Invasive BMI requires surgicalprocedures and measures the brain signals from intracranial electrodes (needle electrodes or brain surface
electrodes), whereas noninvasive BMI measures brain signals noninvasively from outside of the bodyusing scalp electrodes, and so forth. To achieve a higher performance and a higher level of usefulness, weemployed invasive BMI techniques, which involve the implantation of devices. For use in a practicalsituation, invasive BMI requires an organic integration of the following medical and engineering
technologies:
1) Neural recording with high spatiotemporal resolution2) High-speed data transfer and processing3) Optimal extraction of appropriate neurophysiological features
4) Accurate neural decoding

25) Robust control of external devices such as robotic arms and electric wheelchairs6) Downsizing, integration, and implantation of electronic devices, and the use of wireless technology7) Noninvasive pre-surgical evaluations for appropriate surgical indications8) On-target survey and analysis of patient needs
9) Addressing of neuroethical issues
In this chapter, we describe the development of our invasive BMI system using brain surface electrodes.
Figure 1. A conceptual diagram of the brain machine interface.

NEURAL DECODING AND REAL-TIME ROBOTIC CONTROL USINGELECTROCORTICOGRAMSClinical studies using electrocorticograms recorded from brain surface
electrodes
In the process of providing neurosurgical treatments for specific groups of patients, we sometimes record
brain signals (electrocorticograms: ECoGs) or electrically stimulate the brain using electrodes that are
directly placed on the brain surface. The ECoGs can selectively measure brain signals within a limited
distance of a few millimeters without distortion. In addition, the ECoGs are insusceptible to external
noises, and the scalp skin electrodes measure the distorted brain signals (electroencephalograms: EEGs)
from a distance of up to a few centimeters. Furthermore, ECoG recordings from the brain surface
electrodes are stable for at least one year (Chao, Nagasaka, & Fujii, 2010), whereas the spike recordings
from the needle electrodes will gradually deteriorate in yield due to chronic inflammatory tissue reactions.
The ECoG is a well-balanced brain signal for BMI (Table 1). Thus, we prefer to use the ECoGs recordedby the brain surface electrodes for BMI to achieve a high performance.
In our clinical studies, all of the subjects were recruited from patients in whom we temporarily placed
brain surface electrodes to treat intractable pain or epilepsy. Informed consent was obtained from all of
the patients, and all of the studies were performed with the approval of the ethics committee of Osaka
University Medical Hospital. We measured the ECoGs during the performance of two or three types ofsimple motor tasks of the hand or arm, such as grasping, pinching, and elbow flexion. We predicted thetype of movement based on the analysis of a single ECoG trial using a support vector machine (SVM)
algorithm (Kamitani & Tong, 2005). As a result, we were able to predict movement types on a single trial
basis with an accuracy rate of 70-90%. Specifically, we first demonstrated that ECoGs from the anteriorwall of the central sulcus (a groove in the brain where most of the primary motor cortex lies) are usefulfor the accurate and early decoding of movement types (Yanagisawa et al., 2009). Most of the primary
motor cortex, which is responsible for the final functional output of motor commands, lies within theanterior wall of the central sulcus. In humans, the anterior wall of the central sulcus contains manyneurons that directly project to the spinal anterior horn cells, and such neurons are thought to be related tofine movement control (Rathelot & Strick, 2009). We suggest that an appropriate neurophysiologicalfeature extraction from the central sulcus contributed to our accurate movement decoding.
We applied this decoding method to an ECoG-based BMI system for real-time control of a robotic arm(Figure 2). The ECoGs were measured using a 128-channel digital EEG system (EEG 2000; Nihon
Koden Corporation, Tokyo, Japan) and digitized at a sampling rate of 1000 Hz. We introduced successive
decoding every 200 ms, and the Gaussian process regression was used to predict the movement onset.
Next, the SVM was used to infer the type of hand and arm movements. The robotic arm was anexperimental anthropomorphic hand developed by Prof. Yokoi H(Yokoi, Kita, & Nakamura, 2009). The
general movement mechanisms and degrees of freedom of the hand mimicked those of a human hand. In
addition, the hand was equipped with 8 DC motors to independently actuate 8 individual tendons in the
robotic hand. The 8 tendons functioned in a coordinated manner to accomplish flexion or extension ofeach individual finger. As a result, we found that the normalized power in the high gamma band (80 -3150 Hz) gave the highest decoding accuracy of all the frequency bands and ranged from 3 to 150 Hz(Yanagisawa et al., 2011). We succeeded in generating the voluntary control of grasping and releasing ofobjects (Figure 3) (Yanagisawa et al., in press). Using a successive decoding and control algorithm, asmooth robotic hand movement was achieved, although the decoding accuracy on a single trial basis wasapproximately 70%. We found that despite being severely paralyzed, just the imagery of the hand
movement could induce clear, high gamma band responses that were similar to those induced by real
movements.
Table 1. Brain signals used for the Brain-Machine Interface.
Figure 2. A real-time BMI system for robotic arm control.
Figure 3. Real-time control of a robotic arm. The patient voluntarily controlled grasping (right) andopening (left) of the robotic arm in real time.

A FULLY IMPLANTABLE WIRELESS SYSTEM
Wired leads, which penetrate the skin, pose a high risk of infection. It is necessary to fully implant arecording system within the body to reduce the infection risk from the penetration of wire leads.
Moreover, once the devices are implanted, it will be more convenient to use the BMI system because thepatients would not have to wear or remove the system. For this reason, we have developed the first
prototype of a fully implantable ECoG recording system for human brain-machine interfaces using brain
surface electrodes. By integrating this wireless system into a real-time BMI system, we ultimately aim todevelop a Wireless Human ECoG-based Real-time BMI System (W-HERBS) (Hirata et al., 2011).

System Overview
The first prototype is shown in Figure 4. This fully implantable system includes many new technologies
such as a 64-ch integrated analog amplifier chip, a Bluetooth wireless data transfer circuit, a wirelessly
rechargeable battery, 3-dimensional tissue conformable high-density electrodes, a titanium head casing,and a fluorine polymer body casing.
The implantable system consists of two parts; a head part and a body part. The head part consists oftissue conformable brain surface microelectrodes, a titanium head casing that also functions as an
artificial skull, and a 128-ch amplifier unit with 2 64-ch chips. The body part consists of a wireless datatransfer unit and a microchip data controller, a wireless rechargeable unit, and a fluorine polymer bodycasing.
Figure 4. A: The first prototype of a fully implantable wireless system for the W-HERBS. A fluorine
polymer body casing, which includes a wireless rechargeable unit and a wireless data transfer unit (a). A
titanium head casing / artificial skull (b). Brain surface microelectrodes conformable to the outer surfaceof the individual brain (c). Brain surface microelectrodes conformable to the brain groove (d). B: The
prototype is attached to the skull bone model.

Integrated Analog Amplifier Unit
The ECoG is characterized as signals with low frequency bands that range from 0.1 Hz to 500 Hz andproduces small amplitudes that range from 1 µV to 1 mV. It is necessary to reduce the input-referrednoise of the amplifier to record the ECoG signals (Yoshida et al., 2010). The variable bandwidth and wide
dynamic ranges are also important because commercial AC noises with similar frequency bands caneasily contaminate ECoG signals. Thus, a high-linearity low noise amplifier with a variable bandwidth
was developed to cover the frequency bands and voltage gains appropriate for recording ECoG signals

4(Yoshida et al., 2011). The low noise amplifier with a 0.1 Hz roll-off frequency was implemented withcore differential amplifiers using large-sized MOSFETs and a capacitor feedback scheme biased byultrahigh resistors of cascade 12 MOSFETs. A VLSI chip was fabricated using CMOS 0.18 µm process
technology in the chip fabrication program of the VLSI Design and Education Center (VDEC) at the
University of Tokyo.
The specifications of the chip functions are as follows:･number of channels: 64 channels･12 bits A/D converter･voltage gain: 40 – 80 dB･signal frequency bands: 0.1 – 1000 Hz･input referred noise: 2.8 µV･power consumption: 4.9 mW･chip size: 5.0 mm x 5.0 mm･master/slave function for a 128-channel system
A 128-channel analog amplifier board consists of two chips mounted on two high-density printed boards
that were bridged by flexible printed wiring (Figure 5). The size of the board was 20 mm x 30 mm x 2.5mm, which was small enough to be placed within a head casing, which will be described later in the text.
Figure 5. A 128-channel integrated analog amplifier board

Wireless Data Transfer Unit
We adapted the Bluetooth protocol communication (Class 2) for the first prototype for its high usability.
A combination of 2 sets of Bluetooth circuits enabled us to achieve effective data transmission rates of
400 kbps, which allowed the transfer of 128-ch x 12-bit ECoG data in real time. Power consumption wasapproximately 300 mW, which meant that most of the system power was consumed by the wireless datatransfer. Further improvements in the data transfer protocol should be made to achieve a faster and more
power-efficient operation of the system. The size was 60 mm x 60 mm x 8 mm, which should also be
reduced. One solution would be to change the data transfer protocol from Bluetooth to WLAN or UWB.

Wireless Rechargeable Unit
The wireless battery charging system consists of two parts. One is a transmitter positioned outside of thehuman body, and the other is a receiver located inside the human body. We achieved a wireless charging
power of 4 W at a distance of 38 mm, which was sufficient to run the entire implantable system. The coilsize of the abdominal portion was 40 mm in diameter and 8 mm in thickness, which may be scaled down
if the power consumption can be reduced.

Tissue Conformable Brain Surface Microelectrodes
To record the ECoGs with a higher spatiotemporal resolution, we developed 3-dimensional high-density
grid electrodes, which were designed to fit to the individual’s brain surface (Hirata et al., 2010). We
extracted 3-dimensional (3D) surface data of the brain surface and brain groove from the patient’s
individual magnetic resonance (MR) images. An automatic brain groove extraction software program(Brain VISA, http://brainvisa.info/) was used. Next, we designed male and female molds for the gridelectrodes using 3D CAD software (3 matic, Materialize Japan, Tokyo, Japan) (Figure 6). Next, the molds
were rapidly produced by a 3D printer. The silicon sheets fitting the brain surface were subsequently
produced from these molds. In addition, the location of each platinum electrode (1.0 mm in diameter) was
designed with the 3D CAD software, which took into account the individual’s anatomical information.
The inter-electrode spacing was up to 2.5 mm and the brain groove grid electrodes were located on bothsides of the electrode sheet. These 3D grid electrodes fitted onto the brain surface with only a minimal

5compression of the brain tissue and generated high ECoGs yields due to their close contact with the brain
surface.
Figure 6. Tissue conformable brain surface microelectrodes. The tissue conformable brain surface
microelectrodes fitted on to the individual brain surfaces. Left: High-density electrodes (inter-electrodespacing 2.5 mm) and standard electrodes (inter-electrode spacing 10 mm). Middle: Gyral (brain surface)electrodes. Right: Sulcal (brain groove) electrodes.

Head Casing and Artificial Skull BoneWe developed a titanium head casing, which contained a 128-channel amplifier unit. This casing
functioned as both a head casing and an artificial skull bone and was designed to fit a patient’s individualskull bone shape using the 3D CAD (3 matic, Materialize Japan, Tokyo) and 3D CAM (Gibbs CAM,
Gibbs and Associates, USA) software programs (Figure 7). This head casing not only had cosmetic
advantages, but it was also safer than other convex shapes that posed a higher risk of cutaneous fistula.
Figure 7. A titanium head casing / artificial skull bone. A: A computer simulation of machining pathusing 3D CAM software. B: A computer simulation of a head casing fitting the skull bone. Left: skull bone
opening. Middle: Outer side view of a head casing fitting the skull bone. Right: Inner side view. C: A
head casing designed using 3D CAD software. Upper: A head casing without an electronic circuit board.
Lower: A head casing with an electronic circuit board. D: A prototype casing. Upper: inner side view.
Lower: outer side view. E: A prototype casing attached to the skull bone model. Three-dimensional skullbone data were obtained from the individual’s CT images. The head casing contains two 64-channel
integrated amplifier chips on a small mounting board, which was mounted onto a folded inner panel as
indicated by the green color.

Fluorine Polymer Body Casing
Compared with the head casing, the body casing offers a larger space and does not require carefulcosmetic consideration. We introduced a soft casing made of fluorine polymer, which has advantages interms of cost, chemical stability, durability, and biocompatibility. This body casing embeds a wirelessdata transfer unit and a microchip data controller, a wireless power supply unit, and a rechargeable battery
in silicone covered by fluorine polymer films.

NONINVASIVE NEURAL DECODING USING MAGNETOENCEPHALOGRAPHY
A noninvasive evaluation of the individual BMI performance is indispensable for determining the surgicalindication of the invasive BMI treatment. A magnetoencephalography (MEG) is a potentiallynoninvasive method for evaluating individual BMI performance because of its high spatiotemporal
resolution and neurophysiological compatibility with the ECoG. We investigated the neural decoding
performance of 3 types of unilateral hand and arm movements on a single trial basis using an MEG(Sugata et al., 2012). We used an SVM to decode the movement types. The peak amplitudes of the firstcomponent after the movement onset of the movement-related cortical fields (pMRCF) were used as
decoding features. As a result, the neural decoding accuracies largely exceeded the chance level in all of
the 9 healthy subjects that were evaluated. The pMRCFs and decoding accuracies were strongly
correlated (rs = 0.900, p = 0.002) (Figure 8). These results suggested that the neurophysiological profiles
might serve as a predictor of individual BMI performance and assist in the improvement of general BMI
performance.

6
Figure 8. Neural decoding using magnetoencephalography. A: a typical averaged waveform of amovement-related cortical field. B: The relationship between the neural decoding accuracies and thepeak amplitudes of the first component after the movement onset of the movement-related cortical field.

CONCLUSION
We have developed an ECoG-based real-time BMI system and the first prototype of a fully implantablewireless system. The ECoG-based real-time BMI system successfully provided voluntary control over thegrasping and opening of a robotic hand. A fully implantable wireless system is indispensable for theclinical application of invasive BMI to reduce the risk of infection. The noninvasive evaluation of anindividual BMI performance using an MEG might be useful for determining the surgical indication ofinvasive BMI treatment.

ACKNOWLEDGMENTS
This work was supported by the Strategic Research Program for Brain Sciences of MEXT. We would liketo acknowledge Atsushi Iwata (A-R-Tec Corp.), Shinichi Morikawa, Yoshihiro Watanabe (Unique
Medical), Naohiro Hayaishi (Keisuugiken Corp.), Shinichi Yoshimura, Shuhei Kosaka (Aska Electric Co.
Ltd.), and Hirofumi Itoh (Junkosha Inc.) for prototype manufacturing of our implantable system, and wewould also like to thank the VLSI Design and Education Center (VDEC) and the University of Tokyo for
their help with the chip fabrication program.

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KEY TERMS AND DEFINITIONSBrain-machine interface: a man-machine interface, which enables us to control machines and tocommunicate with others without the use of input devices, but through the direct use of brain signals
alone.
Neural decoding: decoding neural signals.
Real time: to respond on the order of milliseconds and at times, microseconds.
Prosthetic arm: an artificial robotic arm that substitutes for a missing arm.
Implantable device: a medical device implanted within the body.
Brain surface electrodes: electrodes that are directly placed on the brain surface.
Motor restoration: recovery of neural motor function.